Merge pull request #452 from Djelibeybi/add-oci-awareness

feat: Add OCI awareness across agents and skills

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -20,7 +20,7 @@ body:
       label: Preliminary Checks
       description: Please confirm you have completed these steps
       options:
-        - label: I have read the [Code of Conduct](.github/CODE_OF_CONDUCT.md)
+        - label: I have read the [Code of Conduct](https://github.com/wshobson/agents/blob/main/.github/CODE_OF_CONDUCT.md)
           required: true
         - label: >-
             I have searched existing issues to ensure this is not a duplicate

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -19,7 +19,7 @@ body:
       label: Preliminary Checks
       description: Please confirm you have completed these steps
       options:
-        - label: I have read the [Code of Conduct](.github/CODE_OF_CONDUCT.md)
+        - label: I have read the [Code of Conduct](https://github.com/wshobson/agents/blob/main/.github/CODE_OF_CONDUCT.md)
           required: true
         - label: >-
             I have searched existing issues to ensure this is not a duplicate

.github/ISSUE_TEMPLATE/new_subagent.yml

@@ -20,7 +20,7 @@ body:
       label: Preliminary Checks
       description: Please confirm you have completed these steps
       options:
-        - label: I have read the [Code of Conduct](.github/CODE_OF_CONDUCT.md)
+        - label: I have read the [Code of Conduct](https://github.com/wshobson/agents/blob/main/.github/CODE_OF_CONDUCT.md)
           required: true
         - label: >-
             I have reviewed existing subagents to ensure this is not a duplicate

Makefile

@@ -0,0 +1,120 @@
+# YouTube Design Extractor - Setup and Usage
+# ==========================================
+
+PYTHON := python3
+PIP := pip3
+SCRIPT := tools/yt-design-extractor.py
+
+.PHONY: help install install-ocr install-easyocr deps check run run-full run-ocr run-transcript clean
+
+help:
+	@echo "YouTube Design Extractor"
+	@echo "========================"
+	@echo ""
+	@echo "Setup (run in order):"
+	@echo "  make install-ocr     Install system tools (tesseract + ffmpeg)"
+	@echo "  make install         Install Python dependencies"
+	@echo "  make deps            Show what's installed"
+	@echo ""
+	@echo "Optional:"
+	@echo "  make install-easyocr Install EasyOCR + PyTorch (~2GB, for stylized text)"
+	@echo ""
+	@echo "Usage:"
+	@echo "  make run URL=<youtube-url>           Basic extraction"
+	@echo "  make run-full URL=<youtube-url>      Full extraction (OCR + colors + scene)"
+	@echo "  make run-ocr URL=<youtube-url>       With OCR only"
+	@echo "  make run-transcript URL=<youtube-url> Transcript + metadata only"
+	@echo ""
+	@echo "Examples:"
+	@echo "  make run URL='https://youtu.be/eVnQFWGDEdY'"
+	@echo "  make run-full URL='https://youtu.be/eVnQFWGDEdY' INTERVAL=15"
+	@echo ""
+	@echo "Options (pass as make variables):"
+	@echo "  URL=<url>          YouTube video URL (required)"
+	@echo "  INTERVAL=<secs>    Frame interval in seconds (default: 30)"
+	@echo "  OUTPUT=<dir>       Output directory"
+	@echo "  ENGINE=<engine>    OCR engine: tesseract (default) or easyocr"
+
+# Installation targets
+install:
+	$(PIP) install -r tools/requirements.txt
+
+install-ocr:
+	@echo "Installing Tesseract OCR + ffmpeg..."
+	@if command -v apt-get >/dev/null 2>&1; then \
+		sudo apt-get update && sudo apt-get install -y tesseract-ocr ffmpeg; \
+	elif command -v brew >/dev/null 2>&1; then \
+		brew install tesseract ffmpeg; \
+	elif command -v dnf >/dev/null 2>&1; then \
+		sudo dnf install -y tesseract ffmpeg; \
+	else \
+		echo "Please install tesseract-ocr and ffmpeg manually"; \
+		exit 1; \
+	fi
+
+install-easyocr:
+	@echo "Installing PyTorch (CPU) + EasyOCR (~2GB download)..."
+	$(PIP) install torch torchvision --index-url https://download.pytorch.org/whl/cpu
+	$(PIP) install easyocr
+
+deps:
+	@echo "Checking dependencies..."
+	@echo ""
+	@echo "System tools:"
+	@command -v ffmpeg >/dev/null 2>&1 && echo "  ✓ ffmpeg" || echo "  ✗ ffmpeg (run: make install-ocr)"
+	@command -v tesseract >/dev/null 2>&1 && echo "  ✓ tesseract" || echo "  ✗ tesseract (run: make install-ocr)"
+	@echo ""
+	@echo "Python packages (required):"
+	@$(PYTHON) -c "import yt_dlp; print('  ✓ yt-dlp', yt_dlp.version.__version__)" 2>/dev/null || echo "  ✗ yt-dlp (run: make install)"
+	@$(PYTHON) -c "from youtube_transcript_api import YouTubeTranscriptApi; print('  ✓ youtube-transcript-api')" 2>/dev/null || echo "  ✗ youtube-transcript-api (run: make install)"
+	@$(PYTHON) -c "from PIL import Image; print('  ✓ Pillow')" 2>/dev/null || echo "  ✗ Pillow (run: make install)"
+	@$(PYTHON) -c "import pytesseract; print('  ✓ pytesseract')" 2>/dev/null || echo "  ✗ pytesseract (run: make install)"
+	@$(PYTHON) -c "from colorthief import ColorThief; print('  ✓ colorthief')" 2>/dev/null || echo "  ✗ colorthief (run: make install)"
+	@echo ""
+	@echo "Optional (for stylized text OCR):"
+	@$(PYTHON) -c "import easyocr; print('  ✓ easyocr')" 2>/dev/null || echo "  ○ easyocr (run: make install-easyocr)"
+
+check:
+	@$(PYTHON) $(SCRIPT) --help >/dev/null && echo "✓ Script is working" || echo "✗ Script failed"
+
+# Run targets
+INTERVAL ?= 30
+ENGINE ?= tesseract
+OUTPUT ?=
+
+run:
+ifndef URL
+	@echo "Error: URL is required"
+	@echo "Usage: make run URL='https://youtu.be/VIDEO_ID'"
+	@exit 1
+endif
+	$(PYTHON) $(SCRIPT) "$(URL)" --interval $(INTERVAL) $(if $(OUTPUT),-o $(OUTPUT))
+
+run-full:
+ifndef URL
+	@echo "Error: URL is required"
+	@echo "Usage: make run-full URL='https://youtu.be/VIDEO_ID'"
+	@exit 1
+endif
+	$(PYTHON) $(SCRIPT) "$(URL)" --full --interval $(INTERVAL) --ocr-engine $(ENGINE) $(if $(OUTPUT),-o $(OUTPUT))
+
+run-ocr:
+ifndef URL
+	@echo "Error: URL is required"
+	@echo "Usage: make run-ocr URL='https://youtu.be/VIDEO_ID'"
+	@exit 1
+endif
+	$(PYTHON) $(SCRIPT) "$(URL)" --ocr --interval $(INTERVAL) --ocr-engine $(ENGINE) $(if $(OUTPUT),-o $(OUTPUT))
+
+run-transcript:
+ifndef URL
+	@echo "Error: URL is required"
+	@echo "Usage: make run-transcript URL='https://youtu.be/VIDEO_ID'"
+	@exit 1
+endif
+	$(PYTHON) $(SCRIPT) "$(URL)" --transcript-only $(if $(OUTPUT),-o $(OUTPUT))
+
+# Cleanup
+clean:
+	rm -rf yt-extract-*
+	@echo "Cleaned up extraction directories"

README.md

@@ -1,18 +1,18 @@
 # Claude Code Plugins: Orchestration and Automation
 
-> **⚡ Updated for Opus 4.5, Sonnet 4.5 & Haiku 4.5** — Three-tier model strategy for optimal performance
+> **⚡ Updated for Opus 4.6, Sonnet 4.6 & Haiku 4.5** — Three-tier model strategy for optimal performance
 
 [![Run in Smithery](https://smithery.ai/badge/skills/wshobson)](https://smithery.ai/skills?ns=wshobson&utm_source=github&utm_medium=badge)
 
 > **🎯 Agent Skills Enabled** — 146 specialized skills extend Claude's capabilities across plugins with progressive disclosure
 
-A comprehensive production-ready system combining **112 specialized AI agents**, **16 multi-agent workflow orchestrators**, **146 agent skills**, and **79 development tools** organized into **73 focused, single-purpose plugins** for [Claude Code](https://docs.claude.com/en/docs/claude-code/overview).
+A comprehensive production-ready system combining **112 specialized AI agents**, **16 multi-agent workflow orchestrators**, **146 agent skills**, and **79 development tools** organized into **72 focused, single-purpose plugins** for [Claude Code](https://docs.claude.com/en/docs/claude-code/overview).
 
 ## Overview
 
 This unified repository provides everything needed for intelligent automation and multi-agent orchestration across modern software development:
 
-- **73 Focused Plugins** - Granular, single-purpose plugins optimized for minimal token usage and composability
+- **72 Focused Plugins** - Granular, single-purpose plugins optimized for minimal token usage and composability
 - **112 Specialized Agents** - Domain experts with deep knowledge across architecture, languages, infrastructure, quality, data/AI, documentation, business operations, and SEO
 - **146 Agent Skills** - Modular knowledge packages with progressive disclosure for specialized expertise
 - **16 Workflow Orchestrators** - Multi-agent coordination systems for complex operations like full-stack development, security hardening, ML pipelines, and incident response
@@ -20,7 +20,7 @@ This unified repository provides everything needed for intelligent automation an
 
 ### Key Features
 
-- **Granular Plugin Architecture**: 73 focused plugins optimized for minimal token usage
+- **Granular Plugin Architecture**: 72 focused plugins optimized for minimal token usage
 - **Comprehensive Tooling**: 79 development tools including test generation, scaffolding, and security scanning
 - **100% Agent Coverage**: All plugins include specialized agents
 - **Agent Skills**: 146 specialized skills following for progressive disclosure and token efficiency
@@ -49,7 +49,7 @@ Add this marketplace to Claude Code:
 /plugin marketplace add wshobson/agents
 ```
 
-This makes all 73 plugins available for installation, but **does not load any agents or tools** into your context.
+This makes all 72 plugins available for installation, but **does not load any agents or tools** into your context.
 
 ### Step 2: Install Plugins
 
@@ -73,7 +73,7 @@ Install the plugins you need:
 
 # Security & quality
 /plugin install security-scanning           # SAST with security skill
-/plugin install code-review-ai             # AI-powered code review
+/plugin install comprehensive-review       # Multi-perspective code analysis
 
 # Full-stack orchestration
 /plugin install full-stack-orchestration   # Multi-agent workflows
@@ -114,7 +114,7 @@ rm -rf ~/.claude/plugins/cache/claude-code-workflows && rm ~/.claude/plugins/ins
 
 ### Core Guides
 
-- **[Plugin Reference](docs/plugins.md)** - Complete catalog of all 73 plugins
+- **[Plugin Reference](docs/plugins.md)** - Complete catalog of all 72 plugins
 - **[Agent Reference](docs/agents.md)** - All 112 agents organized by category
 - **[Agent Skills](docs/agent-skills.md)** - 146 specialized skills with progressive disclosure
 - **[Usage Guide](docs/usage.md)** - Commands, workflows, and best practices
@@ -203,14 +203,14 @@ Strategic model assignment for optimal performance and cost:
 
 | Tier       | Model    | Agents | Use Case                                                                                        |
 | ---------- | -------- | ------ | ----------------------------------------------------------------------------------------------- |
-| **Tier 1** | Opus 4.5 | 42     | Critical architecture, security, ALL code review, production coding (language pros, frameworks) |
+| **Tier 1** | Opus 4.6 | 42     | Critical architecture, security, ALL code review, production coding (language pros, frameworks) |
 | **Tier 2** | Inherit  | 42     | Complex tasks - user chooses model (AI/ML, backend, frontend/mobile, specialized)               |
 | **Tier 3** | Sonnet   | 51     | Support with intelligence (docs, testing, debugging, network, API docs, DX, legacy, payments)   |
 | **Tier 4** | Haiku    | 18     | Fast operational tasks (SEO, deployment, simple docs, sales, content, search)                   |
 
-**Why Opus 4.5 for Critical Agents?**
+**Why Opus 4.6 for Critical Agents?**
 
-- 80.9% on SWE-bench (industry-leading)
+- 80.8% on SWE-bench (industry-leading)
 - 65% fewer tokens for complex tasks
 - Best for architecture decisions and security audits
 
@@ -218,14 +218,14 @@ Strategic model assignment for optimal performance and cost:
 Agents marked `inherit` use your session's default model, letting you balance cost and capability:
 
 - Set via `claude --model opus` or `claude --model sonnet` when starting a session
-- Falls back to Sonnet 4.5 if no default specified
+- Falls back to Sonnet 4.6 if no default specified
 - Perfect for frontend/mobile developers who want cost control
 - AI/ML engineers can choose Opus for complex model work
 
 **Cost Considerations:**
 
-- **Opus 4.5**: $5/$25 per million input/output tokens - Premium for critical work
-- **Sonnet 4.5**: $3/$15 per million tokens - Balanced performance/cost
+- **Opus 4.6**: $5/$25 per million input/output tokens - Premium for critical work
+- **Sonnet 4.6**: $3/$15 per million tokens - Balanced performance/cost
 - **Haiku 4.5**: $1/$5 per million tokens - Fast, cost-effective operations
 - Opus's 65% token reduction on complex tasks often offsets higher rate
 - Use `inherit` tier to control costs for high-volume use cases
@@ -283,13 +283,13 @@ Uses kubernetes-architect agent with 4 specialized skills for production-grade c
 
 ## Plugin Categories
 
-**24 categories, 73 plugins:**
+**24 categories, 72 plugins:**
 
 - 🎨 **Development** (4) - debugging, backend, frontend, multi-platform
 - 📚 **Documentation** (3) - code docs, API specs, diagrams, C4 architecture
 - 🔄 **Workflows** (5) - git, full-stack, TDD, **Conductor** (context-driven development), **Agent Teams** (multi-agent orchestration)
 - ✅ **Testing** (2) - unit testing, TDD workflows
-- 🔍 **Quality** (3) - code review, comprehensive review, performance
+- 🔍 **Quality** (2) - comprehensive review, performance
 - 🤖 **AI & ML** (4) - LLM apps, agent orchestration, context, MLOps
 - 📊 **Data** (2) - data engineering, data validation
 - 🗄️ **Database** (2) - database design, migrations
@@ -330,7 +330,7 @@ Three-tier architecture for token efficiency:
 ```
 claude-agents/
 ├── .claude-plugin/
-│   └── marketplace.json          # 73 plugins
+│   └── marketplace.json          # 72 plugins
 ├── plugins/
 │   ├── python-development/
 │   │   ├── agents/               # 3 Python experts

docs/architecture.md

@@ -334,7 +334,7 @@ Feature Development Workflow:
 1. backend-development:feature-development
 2. security-scanning:security-hardening
 3. unit-testing:test-generate
-4. code-review-ai:ai-review
+4. comprehensive-review:full-review
 5. cicd-automation:workflow-automate
 6. observability-monitoring:monitor-setup
 ```

docs/plugins.md

@@ -1,6 +1,6 @@
 # Complete Plugin Reference
 
-Browse all **72 focused, single-purpose plugins** organized by category.
+Browse all **71 focused, single-purpose plugins** organized by category.
 
 ## Quick Start - Essential Plugins
 
@@ -68,14 +68,6 @@ Multi-agent coordination from backend → frontend → testing → security →
 
 Generate pytest (Python) and Jest (JavaScript) unit tests automatically with comprehensive edge case coverage.
 
-**code-review-ai** - AI-powered code review
-
-```bash
-/plugin install code-review-ai
-```
-
-Architectural analysis, security assessment, and code quality review with actionable feedback.
-
 ### Infrastructure & Operations
 
 **cloud-infrastructure** - Cloud architecture design
@@ -150,11 +142,10 @@ Next.js, React + Vite, and Node.js project setup with pnpm and TypeScript best p
 | **unit-testing**  | Automated unit test generation (Python/JavaScript) | `/plugin install unit-testing`  |
 | **tdd-workflows** | Test-driven development methodology                | `/plugin install tdd-workflows` |
 
-### 🔍 Quality (3 plugins)
+### 🔍 Quality (2 plugins)
 
 | Plugin                         | Description                                   | Install                                      |
 | ------------------------------ | --------------------------------------------- | -------------------------------------------- |
-| **code-review-ai**             | AI-powered architectural review               | `/plugin install code-review-ai`             |
 | **comprehensive-review**       | Multi-perspective code analysis               | `/plugin install comprehensive-review`       |
 | **performance-testing-review** | Performance analysis and test coverage review | `/plugin install performance-testing-review` |
 

docs/usage.md

@@ -70,7 +70,6 @@ Claude Code automatically selects and coordinates the appropriate agents based o
 
 | Command                             | Description                |
 | ----------------------------------- | -------------------------- |
-| `/code-review-ai:ai-review`         | AI-powered code review     |
 | `/comprehensive-review:full-review` | Multi-perspective analysis |
 | `/comprehensive-review:pr-enhance`  | Enhance pull requests      |
 
@@ -361,7 +360,7 @@ Compose multiple plugins for complex scenarios:
 /unit-testing:test-generate
 
 # 4. Review the implementation
-/code-review-ai:ai-review
+/comprehensive-review:full-review
 
 # 5. Set up CI/CD
 /cicd-automation:workflow-automate

plugins/accessibility-compliance/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "accessibility-compliance",
+  "version": "1.2.2",
+  "description": "WCAG accessibility auditing, compliance validation, UI testing for screen readers, keyboard navigation, and inclusive design",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/accessibility-compliance/skills/screen-reader-testing/SKILL.md

@@ -536,10 +536,3 @@ function logAccessibleName(element) {
 - **Don't test only happy path** - Test error states
 - **Don't skip dynamic content** - Most common issues
 - **Don't rely on visual testing** - Different experience
-
-## Resources
-
-- [VoiceOver User Guide](https://support.apple.com/guide/voiceover/welcome/mac)
-- [NVDA User Guide](https://www.nvaccess.org/files/nvda/documentation/userGuide.html)
-- [JAWS Documentation](https://support.freedomscientific.com/Products/Blindness/JAWS)
-- [WebAIM Screen Reader Survey](https://webaim.org/projects/screenreadersurvey/)

plugins/accessibility-compliance/skills/wcag-audit-patterns/SKILL.md

@@ -546,10 +546,3 @@ class AccessibleDropdown extends HTMLElement {
 - **Don't hide focus outlines** - Keyboard users need them
 - **Don't disable zoom** - Users need to resize
 - **Don't use color alone** - Multiple indicators needed
-
-## Resources
-
-- [WCAG 2.2 Guidelines](https://www.w3.org/TR/WCAG22/)
-- [WebAIM](https://webaim.org/)
-- [A11y Project Checklist](https://www.a11yproject.com/checklist/)
-- [axe DevTools](https://www.deque.com/axe/)

plugins/agent-orchestration/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "agent-orchestration",
+  "version": "1.2.1",
+  "description": "Multi-agent system optimization, agent improvement workflows, and context management",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/agent-orchestration/commands/multi-agent-optimize.md

@@ -146,7 +146,7 @@ class CostOptimizer:
         self.token_budget = 100000  # Monthly budget
         self.token_usage = 0
         self.model_costs = {
-            'gpt-5': 0.03,
+            'gpt-5.2': 0.03,
             'claude-4-sonnet': 0.015,
             'claude-4-haiku': 0.0025
         }

plugins/api-scaffolding/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "api-scaffolding",
+  "version": "1.2.2",
+  "description": "REST and GraphQL API scaffolding, framework selection, backend architecture, and API generation",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/api-scaffolding/agents/backend-architect.md

@@ -44,7 +44,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Service boundaries**: Domain-Driven Design, bounded contexts, service decomposition
 - **Service communication**: Synchronous (REST, gRPC), asynchronous (message queues, events)
 - **Service discovery**: Consul, etcd, Eureka, Kubernetes service discovery
-- **API Gateway**: Kong, Ambassador, AWS API Gateway, Azure API Management
+- **API Gateway**: Kong, Ambassador, AWS API Gateway, Azure API Management, OCI API Gateway
 - **Service mesh**: Istio, Linkerd, traffic management, observability, security
 - **Backend-for-Frontend (BFF)**: Client-specific backends, API aggregation
 - **Strangler pattern**: Gradual migration, legacy system integration
@@ -54,8 +54,8 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 
 ### Event-Driven Architecture
 
-- **Message queues**: RabbitMQ, AWS SQS, Azure Service Bus, Google Pub/Sub
-- **Event streaming**: Kafka, AWS Kinesis, Azure Event Hubs, NATS
+- **Message queues**: RabbitMQ, AWS SQS, Azure Service Bus, Google Pub/Sub, OCI Queue
+- **Event streaming**: Kafka, AWS Kinesis, Azure Event Hubs, Google Pub/Sub, OCI Streaming, NATS
 - **Pub/Sub patterns**: Topic-based, content-based filtering, fan-out
 - **Event sourcing**: Event store, event replay, snapshots, projections
 - **Event-driven microservices**: Event choreography, event collaboration
@@ -86,10 +86,10 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **CSRF protection**: Token-based, SameSite cookies, double-submit patterns
 - **SQL injection prevention**: Parameterized queries, ORM usage, input validation
 - **API security**: API keys, OAuth scopes, request signing, encryption
-- **Secrets management**: Vault, AWS Secrets Manager, environment variables
+- **Secrets management**: Vault, AWS Secrets Manager, Azure Key Vault, OCI Vault, environment variables
 - **Content Security Policy**: Headers, XSS prevention, frame protection
 - **API throttling**: Quota management, burst limits, backpressure
-- **DDoS protection**: CloudFlare, AWS Shield, rate limiting, IP blocking
+- **DDoS protection**: CloudFlare, AWS Shield, Azure DDoS Protection, OCI WAF, rate limiting, IP blocking
 
 ### Resilience & Fault Tolerance
 
@@ -168,7 +168,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 ### API Gateway & Load Balancing
 
 - **Gateway patterns**: Authentication, rate limiting, request routing, transformation
-- **Gateway technologies**: Kong, Traefik, Envoy, AWS API Gateway, NGINX
+- **Gateway technologies**: Kong, Traefik, Envoy, AWS API Gateway, Azure API Management, OCI API Gateway, NGINX
 - **Load balancing**: Round-robin, least connections, consistent hashing, health-aware
 - **Service routing**: Path-based, header-based, weighted routing, A/B testing
 - **Traffic management**: Canary deployments, blue-green, traffic splitting

plugins/api-scaffolding/skills/fastapi-templates/SKILL.md

@@ -538,30 +538,3 @@ async def test_create_user(client):
     assert data["email"] == "test@example.com"
     assert "id" in data
 ```
-
-## Resources
-
-- **references/fastapi-architecture.md**: Detailed architecture guide
-- **references/async-best-practices.md**: Async/await patterns
-- **references/testing-strategies.md**: Comprehensive testing guide
-- **assets/project-template/**: Complete FastAPI project
-- **assets/docker-compose.yml**: Development environment setup
-
-## Best Practices
-
-1. **Async All The Way**: Use async for database, external APIs
-2. **Dependency Injection**: Leverage FastAPI's DI system
-3. **Repository Pattern**: Separate data access from business logic
-4. **Service Layer**: Keep business logic out of routes
-5. **Pydantic Schemas**: Strong typing for request/response
-6. **Error Handling**: Consistent error responses
-7. **Testing**: Test all layers independently
-
-## Common Pitfalls
-
-- **Blocking Code in Async**: Using synchronous database drivers
-- **No Service Layer**: Business logic in route handlers
-- **Missing Type Hints**: Loses FastAPI's benefits
-- **Ignoring Sessions**: Not properly managing database sessions
-- **No Testing**: Skipping integration tests
-- **Tight Coupling**: Direct database access in routes

plugins/api-testing-observability/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "api-testing-observability",
+  "version": "1.2.0",
+  "description": "API testing automation, request mocking, OpenAPI documentation generation, observability setup, and monitoring",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/application-performance/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "application-performance",
+  "version": "1.3.0",
+  "description": "Application profiling, performance optimization, and observability for frontend and backend systems",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/application-performance/agents/observability-engineer.md

@@ -20,6 +20,7 @@ Expert observability engineer specializing in comprehensive monitoring strategie
 - DataDog enterprise monitoring with custom metrics and synthetic monitoring
 - New Relic APM integration and performance baseline establishment
 - CloudWatch comprehensive AWS service monitoring and cost optimization
+- OCI Monitoring, Logging, and Logging Analytics for cloud-native telemetry pipelines
 - Nagios and Zabbix for traditional infrastructure monitoring
 - Custom metrics collection with StatsD, Telegraf, and Collectd
 - High-cardinality metrics handling and storage optimization
@@ -29,6 +30,7 @@ Expert observability engineer specializing in comprehensive monitoring strategie
 - Jaeger distributed tracing deployment and trace analysis
 - Zipkin trace collection and service dependency mapping
 - AWS X-Ray integration for serverless and microservice architectures
+- OCI Application Performance Monitoring for distributed tracing and service diagnostics
 - OpenTracing and OpenTelemetry instrumentation standards
 - Application Performance Monitoring with detailed transaction tracing
 - Service mesh observability with Istio and Envoy telemetry
@@ -88,7 +90,7 @@ Expert observability engineer specializing in comprehensive monitoring strategie
 
 - Kubernetes cluster monitoring with Prometheus Operator
 - Docker container metrics and resource utilization tracking
-- Cloud provider monitoring across AWS, Azure, and GCP
+- Cloud provider monitoring across AWS, Azure, GCP, and OCI
 - Database performance monitoring for SQL and NoSQL systems
 - Network monitoring and traffic analysis with SNMP and flow data
 - Server hardware monitoring and predictive maintenance
@@ -189,7 +191,7 @@ Expert observability engineer specializing in comprehensive monitoring strategie
 - Cloud-native observability patterns and Kubernetes monitoring with service mesh integration
 - Security monitoring and compliance requirements (SOC2, PCI DSS, HIPAA, GDPR)
 - Machine learning applications in anomaly detection, forecasting, and automated root cause analysis
-- Multi-cloud and hybrid monitoring strategies across AWS, Azure, GCP, and on-premises
+- Multi-cloud and hybrid monitoring strategies across AWS, Azure, GCP, OCI, and on-premises
 - Developer experience optimization for observability tooling and shift-left monitoring
 - Incident response best practices, post-incident analysis, and blameless postmortem culture
 - Cost-effective monitoring strategies scaling from startups to enterprises with budget optimization
@@ -224,5 +226,5 @@ Expert observability engineer specializing in comprehensive monitoring strategie
 - "Create automated incident response workflows with runbook integration and Slack/PagerDuty escalation"
 - "Build multi-region observability architecture with data sovereignty compliance"
 - "Implement machine learning-based anomaly detection for proactive issue identification"
-- "Design observability strategy for serverless architecture with AWS Lambda and API Gateway"
+- "Design observability strategy for serverless architecture with AWS Lambda, API Gateway, and OCI Functions"
 - "Create custom metrics pipeline for business KPIs integrated with technical monitoring"

plugins/application-performance/agents/performance-engineer.md

@@ -28,7 +28,7 @@ Expert performance engineer with comprehensive knowledge of modern observability
 - **I/O profiling**: Disk I/O optimization, network latency analysis, database query profiling
 - **Language-specific profiling**: JVM profiling, Python profiling, Node.js profiling, Go profiling
 - **Container profiling**: Docker performance analysis, Kubernetes resource optimization
-- **Cloud profiling**: AWS X-Ray, Azure Application Insights, GCP Cloud Profiler
+- **Cloud profiling**: AWS X-Ray, Azure Application Insights, GCP Cloud Profiler, OCI Application Performance Monitoring
 
 ### Modern Load Testing & Performance Validation
 
@@ -44,7 +44,7 @@ Expert performance engineer with comprehensive knowledge of modern observability
 - **Application caching**: In-memory caching, object caching, computed value caching
 - **Distributed caching**: Redis, Memcached, Hazelcast, cloud cache services
 - **Database caching**: Query result caching, connection pooling, buffer pool optimization
-- **CDN optimization**: CloudFlare, AWS CloudFront, Azure CDN, edge caching strategies
+- **CDN optimization**: CloudFlare, AWS CloudFront, Azure CDN, GCP CDN, OCI CDN
 - **Browser caching**: HTTP cache headers, service workers, offline-first strategies
 - **API caching**: Response caching, conditional requests, cache invalidation strategies
 
@@ -78,7 +78,7 @@ Expert performance engineer with comprehensive knowledge of modern observability
 ### Cloud Performance Optimization
 
 - **Auto-scaling optimization**: HPA, VPA, cluster autoscaling, scaling policies
-- **Serverless optimization**: Lambda performance, cold start optimization, memory allocation
+- **Serverless optimization**: Lambda, Azure Functions, Cloud Functions, OCI Functions cold start optimization and memory allocation
 - **Container optimization**: Docker image optimization, Kubernetes resource limits
 - **Network optimization**: VPC performance, CDN integration, edge computing
 - **Storage optimization**: Disk I/O performance, database performance, object storage
@@ -139,7 +139,7 @@ Expert performance engineer with comprehensive knowledge of modern observability
 - Load testing strategies and performance validation techniques
 - Caching architectures and strategies across different system layers
 - Frontend and backend performance optimization best practices
-- Cloud platform performance characteristics and optimization opportunities
+- Cloud platform performance characteristics and optimization opportunities across AWS, Azure, GCP, and OCI
 - Database performance tuning and optimization techniques
 - Distributed system performance patterns and anti-patterns
 

plugins/application-performance/commands/performance-optimization.md

@@ -1,124 +1,681 @@
-Optimize application performance end-to-end using specialized performance and optimization agents:
+---
+description: "Orchestrate end-to-end application performance optimization from profiling to monitoring"
+argument-hint: "<application or service> [--focus latency|throughput|cost|balanced] [--depth quick-wins|comprehensive|enterprise]"
+---
 
-[Extended thinking: This workflow orchestrates a comprehensive performance optimization process across the entire application stack. Starting with deep profiling and baseline establishment, the workflow progresses through targeted optimizations in each system layer, validates improvements through load testing, and establishes continuous monitoring for sustained performance. Each phase builds on insights from previous phases, creating a data-driven optimization strategy that addresses real bottlenecks rather than theoretical improvements. The workflow emphasizes modern observability practices, user-centric performance metrics, and cost-effective optimization strategies.]
+# Performance Optimization Orchestrator
 
-## Phase 1: Performance Profiling & Baseline
+## CRITICAL BEHAVIORAL RULES
 
-### 1. Comprehensive Performance Profiling
+You MUST follow these rules exactly. Violating any of them is a failure.
 
-- Use Task tool with subagent_type="performance-engineer"
-- Prompt: "Profile application performance comprehensively for: $ARGUMENTS. Generate flame graphs for CPU usage, heap dumps for memory analysis, trace I/O operations, and identify hot paths. Use APM tools like DataDog or New Relic if available. Include database query profiling, API response times, and frontend rendering metrics. Establish performance baselines for all critical user journeys."
-- Context: Initial performance investigation
-- Output: Detailed performance profile with flame graphs, memory analysis, bottleneck identification, baseline metrics
+1. **Execute steps in order.** Do NOT skip ahead, reorder, or merge steps.
+2. **Write output files.** Each step MUST produce its output file in `.performance-optimization/` before the next step begins. Read from prior step files — do NOT rely on context window memory.
+3. **Stop at checkpoints.** When you reach a `PHASE CHECKPOINT`, you MUST stop and wait for explicit user approval before continuing. Use the AskUserQuestion tool with clear options.
+4. **Halt on failure.** If any step fails (agent error, test failure, missing dependency), STOP immediately. Present the error and ask the user how to proceed. Do NOT silently continue.
+5. **Use only local agents.** All `subagent_type` references use agents bundled with this plugin or `general-purpose`. No cross-plugin dependencies.
+6. **Never enter plan mode autonomously.** Do NOT use EnterPlanMode. This command IS the plan — execute it.
 
-### 2. Observability Stack Assessment
+## Pre-flight Checks
 
-- Use Task tool with subagent_type="observability-engineer"
-- Prompt: "Assess current observability setup for: $ARGUMENTS. Review existing monitoring, distributed tracing with OpenTelemetry, log aggregation, and metrics collection. Identify gaps in visibility, missing metrics, and areas needing better instrumentation. Recommend APM tool integration and custom metrics for business-critical operations."
-- Context: Performance profile from step 1
-- Output: Observability assessment report, instrumentation gaps, monitoring recommendations
+Before starting, perform these checks:
 
-### 3. User Experience Analysis
+### 1. Check for existing session
 
-- Use Task tool with subagent_type="performance-engineer"
-- Prompt: "Analyze user experience metrics for: $ARGUMENTS. Measure Core Web Vitals (LCP, FID, CLS), page load times, time to interactive, and perceived performance. Use Real User Monitoring (RUM) data if available. Identify user journeys with poor performance and their business impact."
-- Context: Performance baselines from step 1
-- Output: UX performance report, Core Web Vitals analysis, user impact assessment
+Check if `.performance-optimization/state.json` exists:
 
-## Phase 2: Database & Backend Optimization
+- If it exists and `status` is `"in_progress"`: Read it, display the current step, and ask the user:
 
-### 4. Database Performance Optimization
+  ```
+  Found an in-progress performance optimization session:
+  Target: [name from state]
+  Current step: [step from state]
 
-- Use Task tool with subagent_type="database-cloud-optimization::database-optimizer"
-- Prompt: "Optimize database performance for: $ARGUMENTS based on profiling data: {context_from_phase_1}. Analyze slow query logs, create missing indexes, optimize execution plans, implement query result caching with Redis/Memcached. Review connection pooling, prepared statements, and batch processing opportunities. Consider read replicas and database sharding if needed."
-- Context: Performance bottlenecks from phase 1
-- Output: Optimized queries, new indexes, caching strategy, connection pool configuration
+  1. Resume from where we left off
+  2. Start fresh (archives existing session)
+  ```
 
-### 5. Backend Code & API Optimization
+- If it exists and `status` is `"complete"`: Ask whether to archive and start fresh.
 
-- Use Task tool with subagent_type="backend-development::backend-architect"
-- Prompt: "Optimize backend services for: $ARGUMENTS targeting bottlenecks: {context_from_phase_1}. Implement efficient algorithms, add application-level caching, optimize N+1 queries, use async/await patterns effectively. Implement pagination, response compression, GraphQL query optimization, and batch API operations. Add circuit breakers and bulkheads for resilience."
-- Context: Database optimizations from step 4, profiling data from phase 1
-- Output: Optimized backend code, caching implementation, API improvements, resilience patterns
+### 2. Initialize state
 
-### 6. Microservices & Distributed System Optimization
+Create `.performance-optimization/` directory and `state.json`:
 
-- Use Task tool with subagent_type="performance-engineer"
-- Prompt: "Optimize distributed system performance for: $ARGUMENTS. Analyze service-to-service communication, implement service mesh optimizations, optimize message queue performance (Kafka/RabbitMQ), reduce network hops. Implement distributed caching strategies and optimize serialization/deserialization."
-- Context: Backend optimizations from step 5
-- Output: Service communication improvements, message queue optimization, distributed caching setup
+```json
+{
+  "target": "$ARGUMENTS",
+  "status": "in_progress",
+  "focus": "balanced",
+  "depth": "comprehensive",
+  "current_step": 1,
+  "current_phase": 1,
+  "completed_steps": [],
+  "files_created": [],
+  "started_at": "ISO_TIMESTAMP",
+  "last_updated": "ISO_TIMESTAMP"
+}
+```
 
-## Phase 3: Frontend & CDN Optimization
+Parse `$ARGUMENTS` for `--focus` and `--depth` flags. Use defaults if not specified.
 
-### 7. Frontend Bundle & Loading Optimization
+### 3. Parse target description
 
-- Use Task tool with subagent_type="frontend-developer"
-- Prompt: "Optimize frontend performance for: $ARGUMENTS targeting Core Web Vitals: {context_from_phase_1}. Implement code splitting, tree shaking, lazy loading, and dynamic imports. Optimize bundle sizes with webpack/rollup analysis. Implement resource hints (prefetch, preconnect, preload). Optimize critical rendering path and eliminate render-blocking resources."
-- Context: UX analysis from phase 1, backend optimizations from phase 2
-- Output: Optimized bundles, lazy loading implementation, improved Core Web Vitals
+Extract the target description from `$ARGUMENTS` (everything before the flags). This is referenced as `$TARGET` in prompts below.
 
-### 8. CDN & Edge Optimization
+---
 
-- Use Task tool with subagent_type="cloud-infrastructure::cloud-architect"
-- Prompt: "Optimize CDN and edge performance for: $ARGUMENTS. Configure CloudFlare/CloudFront for optimal caching, implement edge functions for dynamic content, set up image optimization with responsive images and WebP/AVIF formats. Configure HTTP/2 and HTTP/3, implement Brotli compression. Set up geographic distribution for global users."
-- Context: Frontend optimizations from step 7
-- Output: CDN configuration, edge caching rules, compression setup, geographic optimization
+## Phase 1: Performance Profiling & Baseline (Steps 1–3)
 
-### 9. Mobile & Progressive Web App Optimization
+### Step 1: Comprehensive Performance Profiling
 
-- Use Task tool with subagent_type="frontend-mobile-development::mobile-developer"
-- Prompt: "Optimize mobile experience for: $ARGUMENTS. Implement service workers for offline functionality, optimize for slow networks with adaptive loading. Reduce JavaScript execution time for mobile CPUs. Implement virtual scrolling for long lists. Optimize touch responsiveness and smooth animations. Consider React Native/Flutter specific optimizations if applicable."
-- Context: Frontend optimizations from steps 7-8
-- Output: Mobile-optimized code, PWA implementation, offline functionality
+Use the Task tool to launch the performance engineer:
 
-## Phase 4: Load Testing & Validation
+```
+Task:
+  subagent_type: "performance-engineer"
+  description: "Profile application performance for $TARGET"
+  prompt: |
+    Profile application performance comprehensively for: $TARGET.
 
-### 10. Comprehensive Load Testing
+    Generate flame graphs for CPU usage, heap dumps for memory analysis, trace I/O operations,
+    and identify hot paths. Use APM tools like DataDog or New Relic if available. Include database
+    query profiling, API response times, and frontend rendering metrics. Establish performance
+    baselines for all critical user journeys.
 
-- Use Task tool with subagent_type="performance-engineer"
-- Prompt: "Conduct comprehensive load testing for: $ARGUMENTS using k6/Gatling/Artillery. Design realistic load scenarios based on production traffic patterns. Test normal load, peak load, and stress scenarios. Include API testing, browser-based testing, and WebSocket testing if applicable. Measure response times, throughput, error rates, and resource utilization at various load levels."
-- Context: All optimizations from phases 1-3
-- Output: Load test results, performance under load, breaking points, scalability analysis
+    ## Deliverables
+    1. Performance profile with flame graphs and memory analysis
+    2. Bottleneck identification ranked by impact
+    3. Baseline metrics for critical user journeys
+    4. Database query profiling results
+    5. API response time measurements
 
-### 11. Performance Regression Testing
+    Write your complete profiling report as a single markdown document.
+```
 
-- Use Task tool with subagent_type="performance-testing-review::test-automator"
-- Prompt: "Create automated performance regression tests for: $ARGUMENTS. Set up performance budgets for key metrics, integrate with CI/CD pipeline using GitHub Actions or similar. Create Lighthouse CI tests for frontend, API performance tests with Artillery, and database performance benchmarks. Implement automatic rollback triggers for performance regressions."
-- Context: Load test results from step 10, baseline metrics from phase 1
-- Output: Performance test suite, CI/CD integration, regression prevention system
+Save the agent's output to `.performance-optimization/01-profiling.md`.
 
-## Phase 5: Monitoring & Continuous Optimization
+Update `state.json`: set `current_step` to 2, add step 1 to `completed_steps`.
 
-### 12. Production Monitoring Setup
+### Step 2: Observability Stack Assessment
 
-- Use Task tool with subagent_type="observability-engineer"
-- Prompt: "Implement production performance monitoring for: $ARGUMENTS. Set up APM with DataDog/New Relic/Dynatrace, configure distributed tracing with OpenTelemetry, implement custom business metrics. Create Grafana dashboards for key metrics, set up PagerDuty alerts for performance degradation. Define SLIs/SLOs for critical services with error budgets."
-- Context: Performance improvements from all previous phases
-- Output: Monitoring dashboards, alert rules, SLI/SLO definitions, runbooks
+Read `.performance-optimization/01-profiling.md` to load profiling context.
 
-### 13. Continuous Performance Optimization
+Use the Task tool:
 
-- Use Task tool with subagent_type="performance-engineer"
-- Prompt: "Establish continuous optimization process for: $ARGUMENTS. Create performance budget tracking, implement A/B testing for performance changes, set up continuous profiling in production. Document optimization opportunities backlog, create capacity planning models, and establish regular performance review cycles."
-- Context: Monitoring setup from step 12, all previous optimization work
-- Output: Performance budget tracking, optimization backlog, capacity planning, review process
+```
+Task:
+  subagent_type: "observability-engineer"
+  description: "Assess observability setup for $TARGET"
+  prompt: |
+    Assess current observability setup for: $TARGET.
 
-## Configuration Options
+    ## Performance Profile
+    [Insert full contents of .performance-optimization/01-profiling.md]
 
-- **performance_focus**: "latency" | "throughput" | "cost" | "balanced" (default: "balanced")
-- **optimization_depth**: "quick-wins" | "comprehensive" | "enterprise" (default: "comprehensive")
-- **tools_available**: ["datadog", "newrelic", "prometheus", "grafana", "k6", "gatling"]
-- **budget_constraints**: Set maximum acceptable costs for infrastructure changes
-- **user_impact_tolerance**: "zero-downtime" | "maintenance-window" | "gradual-rollout"
+    Review existing monitoring, distributed tracing with OpenTelemetry, log aggregation,
+    and metrics collection. Identify gaps in visibility, missing metrics, and areas needing
+    better instrumentation. Recommend APM tool integration and custom metrics for
+    business-critical operations.
+
+    ## Deliverables
+    1. Current observability assessment
+    2. Instrumentation gaps identified
+    3. Monitoring recommendations
+    4. Recommended metrics and dashboards
+
+    Write your complete assessment as a single markdown document.
+```
+
+Save the agent's output to `.performance-optimization/02-observability.md`.
+
+Update `state.json`: set `current_step` to 3, add step 2 to `completed_steps`.
+
+### Step 3: User Experience Analysis
+
+Read `.performance-optimization/01-profiling.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "performance-engineer"
+  description: "Analyze user experience metrics for $TARGET"
+  prompt: |
+    Analyze user experience metrics for: $TARGET.
+
+    ## Performance Baselines
+    [Insert contents of .performance-optimization/01-profiling.md]
+
+    Measure Core Web Vitals (LCP, FID, CLS), page load times, time to interactive,
+    and perceived performance. Use Real User Monitoring (RUM) data if available.
+    Identify user journeys with poor performance and their business impact.
+
+    ## Deliverables
+    1. Core Web Vitals analysis
+    2. User journey performance report
+    3. Business impact assessment
+    4. Prioritized improvement opportunities
+
+    Write your complete analysis as a single markdown document.
+```
+
+Save the agent's output to `.performance-optimization/03-ux-analysis.md`.
+
+Update `state.json`: set `current_step` to "checkpoint-1", add step 3 to `completed_steps`.
+
+---
+
+## PHASE CHECKPOINT 1 — User Approval Required
+
+You MUST stop here and present the profiling results for review.
+
+Display a summary from `.performance-optimization/01-profiling.md`, `.performance-optimization/02-observability.md`, and `.performance-optimization/03-ux-analysis.md` (key bottlenecks, observability gaps, UX findings) and ask:
+
+```
+Performance profiling complete. Please review:
+- .performance-optimization/01-profiling.md
+- .performance-optimization/02-observability.md
+- .performance-optimization/03-ux-analysis.md
+
+Key bottlenecks: [summary]
+Observability gaps: [summary]
+UX findings: [summary]
+
+1. Approve — proceed to optimization
+2. Request changes — tell me what to adjust
+3. Pause — save progress and stop here
+```
+
+Do NOT proceed to Phase 2 until the user selects option 1. If they select option 2, revise and re-checkpoint. If option 3, update `state.json` status and stop.
+
+---
+
+## Phase 2: Database & Backend Optimization (Steps 4–6)
+
+### Step 4: Database Performance Optimization
+
+Read `.performance-optimization/01-profiling.md` and `.performance-optimization/03-ux-analysis.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Optimize database performance for $TARGET"
+  prompt: |
+    You are a database optimization expert. Optimize database performance for: $TARGET.
+
+    ## Profiling Data
+    [Insert contents of .performance-optimization/01-profiling.md]
+
+    ## UX Analysis
+    [Insert contents of .performance-optimization/03-ux-analysis.md]
+
+    Analyze slow query logs, create missing indexes, optimize execution plans, implement
+    query result caching with Redis/Memcached. Review connection pooling, prepared statements,
+    and batch processing opportunities. Consider read replicas and database sharding if needed.
+
+    ## Deliverables
+    1. Optimized queries with before/after performance
+    2. New indexes with justification
+    3. Caching strategy recommendation
+    4. Connection pool configuration
+    5. Implementation plan with priority order
+
+    Write your complete optimization plan as a single markdown document.
+```
+
+Save output to `.performance-optimization/04-database.md`.
+
+Update `state.json`: set `current_step` to 5, add step 4 to `completed_steps`.
+
+### Step 5: Backend Code & API Optimization
+
+Read `.performance-optimization/01-profiling.md` and `.performance-optimization/04-database.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Optimize backend services for $TARGET"
+  prompt: |
+    You are a backend performance architect. Optimize backend services for: $TARGET.
+
+    ## Profiling Data
+    [Insert contents of .performance-optimization/01-profiling.md]
+
+    ## Database Optimizations
+    [Insert contents of .performance-optimization/04-database.md]
+
+    Implement efficient algorithms, add application-level caching, optimize N+1 queries,
+    use async/await patterns effectively. Implement pagination, response compression,
+    GraphQL query optimization, and batch API operations. Add circuit breakers and
+    bulkheads for resilience.
+
+    ## Deliverables
+    1. Optimized backend code with before/after metrics
+    2. Caching implementation plan
+    3. API improvements with expected impact
+    4. Resilience patterns added
+    5. Implementation priority order
+
+    Write your complete optimization plan as a single markdown document.
+```
+
+Save output to `.performance-optimization/05-backend.md`.
+
+Update `state.json`: set `current_step` to 6, add step 5 to `completed_steps`.
+
+### Step 6: Microservices & Distributed System Optimization
+
+Read `.performance-optimization/01-profiling.md` and `.performance-optimization/05-backend.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "performance-engineer"
+  description: "Optimize distributed system performance for $TARGET"
+  prompt: |
+    Optimize distributed system performance for: $TARGET.
+
+    ## Profiling Data
+    [Insert contents of .performance-optimization/01-profiling.md]
+
+    ## Backend Optimizations
+    [Insert contents of .performance-optimization/05-backend.md]
+
+    Analyze service-to-service communication, implement service mesh optimizations,
+    optimize message queue performance (Kafka/RabbitMQ), reduce network hops. Implement
+    distributed caching strategies and optimize serialization/deserialization.
+
+    ## Deliverables
+    1. Service communication improvements
+    2. Message queue optimization plan
+    3. Distributed caching setup
+    4. Network optimization recommendations
+    5. Expected latency improvements
+
+    Write your complete optimization plan as a single markdown document.
+```
+
+Save output to `.performance-optimization/06-distributed.md`.
+
+Update `state.json`: set `current_step` to "checkpoint-2", add step 6 to `completed_steps`.
+
+---
+
+## PHASE CHECKPOINT 2 — User Approval Required
+
+Display a summary of optimization plans from steps 4-6 and ask:
+
+```
+Backend optimization plans complete. Please review:
+- .performance-optimization/04-database.md
+- .performance-optimization/05-backend.md
+- .performance-optimization/06-distributed.md
+
+1. Approve — proceed to frontend & CDN optimization
+2. Request changes — tell me what to adjust
+3. Pause — save progress and stop here
+```
+
+Do NOT proceed to Phase 3 until the user approves.
+
+---
+
+## Phase 3: Frontend & CDN Optimization (Steps 7–9)
+
+### Step 7: Frontend Bundle & Loading Optimization
+
+Read `.performance-optimization/03-ux-analysis.md` and `.performance-optimization/05-backend.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "frontend-developer"
+  description: "Optimize frontend performance for $TARGET"
+  prompt: |
+    Optimize frontend performance for: $TARGET targeting Core Web Vitals improvements.
+
+    ## UX Analysis
+    [Insert contents of .performance-optimization/03-ux-analysis.md]
+
+    ## Backend Optimizations
+    [Insert contents of .performance-optimization/05-backend.md]
+
+    Implement code splitting, tree shaking, lazy loading, and dynamic imports. Optimize bundle
+    sizes with webpack/rollup analysis. Implement resource hints (prefetch, preconnect, preload).
+    Optimize critical rendering path and eliminate render-blocking resources.
+
+    ## Deliverables
+    1. Bundle optimization with size reductions
+    2. Lazy loading implementation plan
+    3. Resource hint configuration
+    4. Critical rendering path optimizations
+    5. Expected Core Web Vitals improvements
+
+    Write your complete optimization plan as a single markdown document.
+```
+
+Save output to `.performance-optimization/07-frontend.md`.
+
+Update `state.json`: set `current_step` to 8, add step 7 to `completed_steps`.
+
+### Step 8: CDN & Edge Optimization
+
+Read `.performance-optimization/07-frontend.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Optimize CDN and edge performance for $TARGET"
+  prompt: |
+    You are a cloud infrastructure and CDN optimization expert. Optimize CDN and edge
+    performance for: $TARGET.
+
+    ## Frontend Optimizations
+    [Insert contents of .performance-optimization/07-frontend.md]
+
+    Configure CloudFlare/CloudFront for optimal caching, implement edge functions for
+    dynamic content, set up image optimization with responsive images and WebP/AVIF formats.
+    Configure HTTP/2 and HTTP/3, implement Brotli compression. Set up geographic
+    distribution for global users.
+
+    ## Deliverables
+    1. CDN configuration recommendations
+    2. Edge caching rules
+    3. Image optimization strategy
+    4. Compression setup
+    5. Geographic distribution plan
+
+    Write your complete optimization plan as a single markdown document.
+```
+
+Save output to `.performance-optimization/08-cdn.md`.
+
+Update `state.json`: set `current_step` to 9, add step 8 to `completed_steps`.
+
+### Step 9: Mobile & Progressive Web App Optimization
+
+Read `.performance-optimization/07-frontend.md` and `.performance-optimization/08-cdn.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Optimize mobile experience for $TARGET"
+  prompt: |
+    You are a mobile performance optimization expert. Optimize mobile experience for: $TARGET.
+
+    ## Frontend Optimizations
+    [Insert contents of .performance-optimization/07-frontend.md]
+
+    ## CDN Optimizations
+    [Insert contents of .performance-optimization/08-cdn.md]
+
+    Implement service workers for offline functionality, optimize for slow networks with
+    adaptive loading. Reduce JavaScript execution time for mobile CPUs. Implement virtual
+    scrolling for long lists. Optimize touch responsiveness and smooth animations. Consider
+    React Native/Flutter specific optimizations if applicable.
+
+    ## Deliverables
+    1. Mobile-optimized code recommendations
+    2. PWA implementation plan
+    3. Offline functionality strategy
+    4. Adaptive loading configuration
+    5. Expected mobile performance improvements
+
+    Write your complete optimization plan as a single markdown document.
+```
+
+Save output to `.performance-optimization/09-mobile.md`.
+
+Update `state.json`: set `current_step` to "checkpoint-3", add step 9 to `completed_steps`.
+
+---
+
+## PHASE CHECKPOINT 3 — User Approval Required
+
+Display a summary of frontend/CDN/mobile optimization plans and ask:
+
+```
+Frontend optimization plans complete. Please review:
+- .performance-optimization/07-frontend.md
+- .performance-optimization/08-cdn.md
+- .performance-optimization/09-mobile.md
+
+1. Approve — proceed to load testing & validation
+2. Request changes — tell me what to adjust
+3. Pause — save progress and stop here
+```
+
+Do NOT proceed to Phase 4 until the user approves.
+
+---
+
+## Phase 4: Load Testing & Validation (Steps 10–11)
+
+### Step 10: Comprehensive Load Testing
+
+Read `.performance-optimization/01-profiling.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "performance-engineer"
+  description: "Conduct comprehensive load testing for $TARGET"
+  prompt: |
+    Conduct comprehensive load testing for: $TARGET using k6/Gatling/Artillery.
+
+    ## Original Baselines
+    [Insert contents of .performance-optimization/01-profiling.md]
+
+    Design realistic load scenarios based on production traffic patterns. Test normal load,
+    peak load, and stress scenarios. Include API testing, browser-based testing, and WebSocket
+    testing if applicable. Measure response times, throughput, error rates, and resource
+    utilization at various load levels.
+
+    ## Deliverables
+    1. Load test scripts and configurations
+    2. Results at normal, peak, and stress loads
+    3. Response time and throughput measurements
+    4. Breaking points and scalability analysis
+    5. Comparison against original baselines
+
+    Write your complete load test report as a single markdown document.
+```
+
+Save output to `.performance-optimization/10-load-testing.md`.
+
+Update `state.json`: set `current_step` to 11, add step 10 to `completed_steps`.
+
+### Step 11: Performance Regression Testing
+
+Read `.performance-optimization/10-load-testing.md` and `.performance-optimization/01-profiling.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Create performance regression tests for $TARGET"
+  prompt: |
+    You are a test automation expert specializing in performance testing. Create automated
+    performance regression tests for: $TARGET.
+
+    ## Load Test Results
+    [Insert contents of .performance-optimization/10-load-testing.md]
+
+    ## Original Baselines
+    [Insert contents of .performance-optimization/01-profiling.md]
+
+    Set up performance budgets for key metrics, integrate with CI/CD pipeline using GitHub
+    Actions or similar. Create Lighthouse CI tests for frontend, API performance tests with
+    Artillery, and database performance benchmarks. Implement automatic rollback triggers
+    for performance regressions.
+
+    ## Deliverables
+    1. Performance test suite with scripts
+    2. CI/CD integration configuration
+    3. Performance budgets and thresholds
+    4. Regression detection rules
+    5. Automatic rollback triggers
+
+    Write your complete regression testing plan as a single markdown document.
+```
+
+Save output to `.performance-optimization/11-regression-testing.md`.
+
+Update `state.json`: set `current_step` to "checkpoint-4", add step 11 to `completed_steps`.
+
+---
+
+## PHASE CHECKPOINT 4 — User Approval Required
+
+Display a summary of testing results and ask:
+
+```
+Load testing and validation complete. Please review:
+- .performance-optimization/10-load-testing.md
+- .performance-optimization/11-regression-testing.md
+
+1. Approve — proceed to monitoring & continuous optimization
+2. Request changes — tell me what to adjust
+3. Pause — save progress and stop here
+```
+
+Do NOT proceed to Phase 5 until the user approves.
+
+---
+
+## Phase 5: Monitoring & Continuous Optimization (Steps 12–13)
+
+### Step 12: Production Monitoring Setup
+
+Read `.performance-optimization/02-observability.md` and `.performance-optimization/10-load-testing.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "observability-engineer"
+  description: "Implement production performance monitoring for $TARGET"
+  prompt: |
+    Implement production performance monitoring for: $TARGET.
+
+    ## Observability Assessment
+    [Insert contents of .performance-optimization/02-observability.md]
+
+    ## Load Test Results
+    [Insert contents of .performance-optimization/10-load-testing.md]
+
+    Set up APM with DataDog/New Relic/Dynatrace, configure distributed tracing with
+    OpenTelemetry, implement custom business metrics. Create Grafana dashboards for key
+    metrics, set up PagerDuty alerts for performance degradation. Define SLIs/SLOs for
+    critical services with error budgets.
+
+    ## Deliverables
+    1. Monitoring dashboard configurations
+    2. Alert rules and thresholds
+    3. SLI/SLO definitions
+    4. Runbooks for common performance issues
+    5. Error budget tracking setup
+
+    Write your complete monitoring plan as a single markdown document.
+```
+
+Save output to `.performance-optimization/12-monitoring.md`.
+
+Update `state.json`: set `current_step` to 13, add step 12 to `completed_steps`.
+
+### Step 13: Continuous Performance Optimization
+
+Read all previous `.performance-optimization/*.md` files.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "performance-engineer"
+  description: "Establish continuous optimization process for $TARGET"
+  prompt: |
+    Establish continuous optimization process for: $TARGET.
+
+    ## Monitoring Setup
+    [Insert contents of .performance-optimization/12-monitoring.md]
+
+    ## All Previous Optimization Work
+    [Insert summary of key findings from all previous steps]
+
+    Create performance budget tracking, implement A/B testing for performance changes,
+    set up continuous profiling in production. Document optimization opportunities backlog,
+    create capacity planning models, and establish regular performance review cycles.
+
+    ## Deliverables
+    1. Performance budget tracking system
+    2. Optimization backlog with priorities
+    3. Capacity planning model
+    4. Review cycle schedule and process
+    5. A/B testing framework for performance changes
+
+    Write your complete continuous optimization plan as a single markdown document.
+```
+
+Save output to `.performance-optimization/13-continuous.md`.
+
+Update `state.json`: set `current_step` to "complete", add step 13 to `completed_steps`.
+
+---
+
+## Completion
+
+Update `state.json`:
+
+- Set `status` to `"complete"`
+- Set `last_updated` to current timestamp
+
+Present the final summary:
+
+```
+Performance optimization complete: $TARGET
+
+## Files Created
+[List all .performance-optimization/ output files]
+
+## Optimization Summary
+- Profiling: .performance-optimization/01-profiling.md
+- Observability: .performance-optimization/02-observability.md
+- UX Analysis: .performance-optimization/03-ux-analysis.md
+- Database: .performance-optimization/04-database.md
+- Backend: .performance-optimization/05-backend.md
+- Distributed: .performance-optimization/06-distributed.md
+- Frontend: .performance-optimization/07-frontend.md
+- CDN: .performance-optimization/08-cdn.md
+- Mobile: .performance-optimization/09-mobile.md
+- Load Testing: .performance-optimization/10-load-testing.md
+- Regression Testing: .performance-optimization/11-regression-testing.md
+- Monitoring: .performance-optimization/12-monitoring.md
+- Continuous: .performance-optimization/13-continuous.md
 
 ## Success Criteria
+- Response Time: P50 < 200ms, P95 < 1s, P99 < 2s for critical endpoints
+- Core Web Vitals: LCP < 2.5s, FID < 100ms, CLS < 0.1
+- Throughput: Support 2x current peak load with <1% error rate
+- Database Performance: Query P95 < 100ms, no queries > 1s
+- Resource Utilization: CPU < 70%, Memory < 80% under normal load
+- Cost Efficiency: Performance per dollar improved by minimum 30%
+- Monitoring Coverage: 100% of critical paths instrumented with alerting
 
-- **Response Time**: P50 < 200ms, P95 < 1s, P99 < 2s for critical endpoints
-- **Core Web Vitals**: LCP < 2.5s, FID < 100ms, CLS < 0.1
-- **Throughput**: Support 2x current peak load with <1% error rate
-- **Database Performance**: Query P95 < 100ms, no queries > 1s
-- **Resource Utilization**: CPU < 70%, Memory < 80% under normal load
-- **Cost Efficiency**: Performance per dollar improved by minimum 30%
-- **Monitoring Coverage**: 100% of critical paths instrumented with alerting
-
-Performance optimization target: $ARGUMENTS
+## Next Steps
+1. Implement optimizations in priority order from each phase
+2. Run regression tests after each optimization
+3. Monitor production metrics against baselines
+4. Review performance budgets in weekly cycles
+```

plugins/arm-cortex-microcontrollers/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "arm-cortex-microcontrollers",
+  "version": "1.2.0",
+  "description": "ARM Cortex-M firmware development for Teensy, STM32, nRF52, and SAMD with peripheral drivers and memory safety patterns",
+  "author": {
+    "name": "Ryan Snodgrass",
+    "url": "https://github.com/rsnodgrass"
+  },
+  "license": "MIT"
+}

plugins/backend-api-security/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "backend-api-security",
+  "version": "1.2.0",
+  "description": "API security hardening, authentication implementation, authorization patterns, rate limiting, and input validation",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/backend-api-security/agents/backend-architect.md

@@ -44,7 +44,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Service boundaries**: Domain-Driven Design, bounded contexts, service decomposition
 - **Service communication**: Synchronous (REST, gRPC), asynchronous (message queues, events)
 - **Service discovery**: Consul, etcd, Eureka, Kubernetes service discovery
-- **API Gateway**: Kong, Ambassador, AWS API Gateway, Azure API Management
+- **API Gateway**: Kong, Ambassador, AWS API Gateway, Azure API Management, OCI API Gateway
 - **Service mesh**: Istio, Linkerd, traffic management, observability, security
 - **Backend-for-Frontend (BFF)**: Client-specific backends, API aggregation
 - **Strangler pattern**: Gradual migration, legacy system integration
@@ -54,8 +54,8 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 
 ### Event-Driven Architecture
 
-- **Message queues**: RabbitMQ, AWS SQS, Azure Service Bus, Google Pub/Sub
-- **Event streaming**: Kafka, AWS Kinesis, Azure Event Hubs, NATS
+- **Message queues**: RabbitMQ, AWS SQS, Azure Service Bus, Google Pub/Sub, OCI Queue
+- **Event streaming**: Kafka, AWS Kinesis, Azure Event Hubs, Google Pub/Sub, OCI Streaming, NATS
 - **Pub/Sub patterns**: Topic-based, content-based filtering, fan-out
 - **Event sourcing**: Event store, event replay, snapshots, projections
 - **Event-driven microservices**: Event choreography, event collaboration
@@ -86,10 +86,10 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **CSRF protection**: Token-based, SameSite cookies, double-submit patterns
 - **SQL injection prevention**: Parameterized queries, ORM usage, input validation
 - **API security**: API keys, OAuth scopes, request signing, encryption
-- **Secrets management**: Vault, AWS Secrets Manager, environment variables
+- **Secrets management**: Vault, AWS Secrets Manager, Azure Key Vault, OCI Vault, environment variables
 - **Content Security Policy**: Headers, XSS prevention, frame protection
 - **API throttling**: Quota management, burst limits, backpressure
-- **DDoS protection**: CloudFlare, AWS Shield, rate limiting, IP blocking
+- **DDoS protection**: CloudFlare, AWS Shield, Azure DDoS Protection, OCI WAF, rate limiting, IP blocking
 
 ### Resilience & Fault Tolerance
 
@@ -168,7 +168,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 ### API Gateway & Load Balancing
 
 - **Gateway patterns**: Authentication, rate limiting, request routing, transformation
-- **Gateway technologies**: Kong, Traefik, Envoy, AWS API Gateway, NGINX
+- **Gateway technologies**: Kong, Traefik, Envoy, AWS API Gateway, Azure API Management, OCI API Gateway, NGINX
 - **Load balancing**: Round-robin, least connections, consistent hashing, health-aware
 - **Service routing**: Path-based, header-based, weighted routing, A/B testing
 - **Traffic management**: Canary deployments, blue-green, traffic splitting

plugins/backend-api-security/agents/backend-security-coder.md

@@ -98,8 +98,8 @@ Expert backend security developer with comprehensive knowledge of secure coding
 
 - **Environment configuration**: Secure environment variable management, configuration encryption
 - **Container security**: Secure Docker practices, image scanning, runtime security
-- **Secrets management**: Integration with HashiCorp Vault, AWS Secrets Manager, Azure Key Vault
-- **Network security**: VPC configuration, security groups, network segmentation
+- **Secrets management**: Integration with HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, OCI Vault
+- **Network security**: VPC/VNet/VCN configuration, security groups, NSGs, network segmentation
 - **Identity and access management**: IAM roles, service account security, principle of least privilege
 
 ## Behavioral Traits
@@ -148,5 +148,6 @@ Expert backend security developer with comprehensive knowledge of secure coding
 - "Implement secure database queries with parameterization and access controls"
 - "Set up comprehensive security headers and CSP for web application"
 - "Create secure error handling that doesn't leak sensitive information"
+- "Integrate OCI Vault-backed application secrets with secure rotation and least-privilege access"
 - "Implement rate limiting and DDoS protection for public API endpoints"
 - "Design secure external service integration with allowlist validation"

plugins/backend-development/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "backend-development",
+  "version": "1.3.1",
+  "description": "Backend API design, GraphQL architecture, workflow orchestration with Temporal, and test-driven backend development",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/backend-development/agents/backend-architect.md

@@ -44,7 +44,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Service boundaries**: Domain-Driven Design, bounded contexts, service decomposition
 - **Service communication**: Synchronous (REST, gRPC), asynchronous (message queues, events)
 - **Service discovery**: Consul, etcd, Eureka, Kubernetes service discovery
-- **API Gateway**: Kong, Ambassador, AWS API Gateway, Azure API Management
+- **API Gateway**: Kong, Ambassador, AWS API Gateway, Azure API Management, OCI API Gateway
 - **Service mesh**: Istio, Linkerd, traffic management, observability, security
 - **Backend-for-Frontend (BFF)**: Client-specific backends, API aggregation
 - **Strangler pattern**: Gradual migration, legacy system integration
@@ -54,8 +54,8 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 
 ### Event-Driven Architecture
 
-- **Message queues**: RabbitMQ, AWS SQS, Azure Service Bus, Google Pub/Sub
-- **Event streaming**: Kafka, AWS Kinesis, Azure Event Hubs, NATS
+- **Message queues**: RabbitMQ, AWS SQS, Azure Service Bus, Google Pub/Sub, OCI Queue
+- **Event streaming**: Kafka, AWS Kinesis, Azure Event Hubs, Google Pub/Sub, OCI Streaming, NATS
 - **Pub/Sub patterns**: Topic-based, content-based filtering, fan-out
 - **Event sourcing**: Event store, event replay, snapshots, projections
 - **Event-driven microservices**: Event choreography, event collaboration
@@ -86,10 +86,10 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **CSRF protection**: Token-based, SameSite cookies, double-submit patterns
 - **SQL injection prevention**: Parameterized queries, ORM usage, input validation
 - **API security**: API keys, OAuth scopes, request signing, encryption
-- **Secrets management**: Vault, AWS Secrets Manager, environment variables
+- **Secrets management**: Vault, AWS Secrets Manager, Azure Key Vault, OCI Vault, environment variables
 - **Content Security Policy**: Headers, XSS prevention, frame protection
 - **API throttling**: Quota management, burst limits, backpressure
-- **DDoS protection**: CloudFlare, AWS Shield, rate limiting, IP blocking
+- **DDoS protection**: CloudFlare, AWS Shield, Azure DDoS Protection, OCI WAF, rate limiting, IP blocking
 
 ### Resilience & Fault Tolerance
 
@@ -168,7 +168,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 ### API Gateway & Load Balancing
 
 - **Gateway patterns**: Authentication, rate limiting, request routing, transformation
-- **Gateway technologies**: Kong, Traefik, Envoy, AWS API Gateway, NGINX
+- **Gateway technologies**: Kong, Traefik, Envoy, AWS API Gateway, Azure API Management, OCI API Gateway, NGINX
 - **Load balancing**: Round-robin, least connections, consistent hashing, health-aware
 - **Service routing**: Path-based, header-based, weighted routing, A/B testing
 - **Traffic management**: Canary deployments, blue-green, traffic splitting

plugins/backend-development/agents/performance-engineer.md

@@ -0,0 +1,44 @@
+---
+name: performance-engineer
+description: Profile and optimize application performance including response times, memory usage, query efficiency, and scalability. Use for performance review during feature development.
+model: sonnet
+---
+
+You are a performance engineer specializing in application optimization during feature development.
+
+## Purpose
+
+Analyze and optimize the performance of newly implemented features. Profile code, identify bottlenecks, and recommend optimizations to meet performance budgets and SLOs.
+
+## Capabilities
+
+- **Code Profiling**: CPU hotspots, memory allocation patterns, I/O bottlenecks, async/await inefficiencies
+- **Database Performance**: N+1 query detection, missing indexes, query plan analysis, connection pool sizing, ORM inefficiencies
+- **API Performance**: Response time analysis, payload optimization, compression, pagination efficiency, batch operation design
+- **Caching Strategy**: Cache-aside/read-through/write-through patterns, TTL tuning, cache invalidation, hit rate analysis
+- **Memory Management**: Memory leak detection, garbage collection pressure, object pooling, buffer management
+- **Concurrency**: Thread pool sizing, async patterns, connection pooling, resource contention, deadlock detection
+- **Frontend Performance**: Bundle size analysis, lazy loading, code splitting, render performance, network waterfall
+- **Load Testing Design**: K6/JMeter/Gatling script design, realistic load profiles, stress testing, capacity planning
+- **Scalability Analysis**: Horizontal vs vertical scaling readiness, stateless design validation, bottleneck identification
+
+## Response Approach
+
+1. **Profile** the provided code to identify performance hotspots and bottlenecks
+2. **Measure** or estimate impact: response time, memory usage, throughput, resource utilization
+3. **Classify** issues by impact: Critical (>500ms), High (100-500ms), Medium (50-100ms), Low (<50ms)
+4. **Recommend** specific optimizations with before/after code examples
+5. **Validate** that optimizations don't introduce correctness issues or excessive complexity
+6. **Benchmark** suggestions with expected improvement estimates
+
+## Output Format
+
+For each finding:
+
+- **Impact**: Critical/High/Medium/Low with estimated latency or resource cost
+- **Location**: File and line reference
+- **Issue**: What's slow and why
+- **Fix**: Specific optimization with code example
+- **Tradeoff**: Any downsides (complexity, memory for speed, etc.)
+
+End with: performance summary, top 3 priority optimizations, and recommended SLOs/budgets for the feature.

plugins/backend-development/agents/security-auditor.md

@@ -0,0 +1,41 @@
+---
+name: security-auditor
+description: Review code and architecture for security vulnerabilities, OWASP Top 10, auth flaws, and compliance issues. Use for security review during feature development.
+model: sonnet
+---
+
+You are a security auditor specializing in application security review during feature development.
+
+## Purpose
+
+Perform focused security reviews of code and architecture produced during feature development. Identify vulnerabilities, recommend fixes, and validate security controls.
+
+## Capabilities
+
+- **OWASP Top 10 Review**: Injection, broken auth, sensitive data exposure, XXE, broken access control, misconfig, XSS, insecure deserialization, vulnerable components, insufficient logging
+- **Authentication & Authorization**: JWT validation, session management, OAuth flows, RBAC/ABAC enforcement, privilege escalation vectors
+- **Input Validation**: SQL injection, command injection, path traversal, XSS, SSRF, prototype pollution
+- **Data Protection**: Encryption at rest/transit, secrets management, PII handling, credential storage
+- **API Security**: Rate limiting, CORS, CSRF, request validation, API key management
+- **Dependency Scanning**: Known CVEs in dependencies, outdated packages, supply chain risks
+- **Infrastructure Security**: Container security, network policies, secrets in env vars, TLS configuration
+
+## Response Approach
+
+1. **Scan** the provided code and architecture for vulnerabilities
+2. **Classify** findings by severity: Critical, High, Medium, Low
+3. **Explain** each finding with the attack vector and impact
+4. **Recommend** specific fixes with code examples where possible
+5. **Validate** that security controls (auth, authz, input validation) are correctly implemented
+
+## Output Format
+
+For each finding:
+
+- **Severity**: Critical/High/Medium/Low
+- **Category**: OWASP category or security domain
+- **Location**: File and line reference
+- **Issue**: What's wrong and why it matters
+- **Fix**: Specific remediation with code example
+
+End with a summary: total findings by severity, overall security posture assessment, and top 3 priority fixes.

plugins/backend-development/agents/test-automator.md

@@ -0,0 +1,41 @@
+---
+name: test-automator
+description: Create comprehensive test suites including unit, integration, and E2E tests. Supports TDD/BDD workflows. Use for test creation during feature development.
+model: sonnet
+---
+
+You are a test automation engineer specializing in creating comprehensive test suites during feature development.
+
+## Purpose
+
+Build robust, maintainable test suites for newly implemented features. Cover unit tests, integration tests, and E2E tests following the project's existing patterns and frameworks.
+
+## Capabilities
+
+- **Unit Testing**: Isolated function/method tests, mocking dependencies, edge cases, error paths
+- **Integration Testing**: API endpoint tests, database integration, service-to-service communication, middleware chains
+- **E2E Testing**: Critical user journeys, happy paths, error scenarios, browser/API-level flows
+- **TDD Support**: Red-green-refactor cycle, failing test first, minimal implementation guidance
+- **BDD Support**: Gherkin scenarios, step definitions, behavior specifications
+- **Test Data**: Factory patterns, fixtures, seed data, synthetic data generation
+- **Mocking & Stubbing**: External service mocks, database stubs, time/environment mocking
+- **Coverage Analysis**: Identify untested paths, suggest additional test cases, coverage gap analysis
+
+## Response Approach
+
+1. **Detect** the project's test framework (Jest, pytest, Go testing, etc.) and existing patterns
+2. **Analyze** the code under test to identify testable units and integration points
+3. **Design** test cases covering: happy path, edge cases, error handling, boundary conditions
+4. **Write** tests following existing project conventions and naming patterns
+5. **Verify** tests are runnable and provide clear failure messages
+6. **Report** coverage assessment and any untested risk areas
+
+## Output Format
+
+Organize tests by type:
+
+- **Unit Tests**: One test file per source file, grouped by function/method
+- **Integration Tests**: Grouped by API endpoint or service interaction
+- **E2E Tests**: Grouped by user journey or feature scenario
+
+Each test should have a descriptive name explaining what behavior is being verified. Include setup/teardown, assertions, and cleanup. Flag any areas where manual testing is recommended over automation.

plugins/backend-development/commands/feature-development.md

@@ -1,150 +1,481 @@
-Orchestrate end-to-end feature development from requirements to production deployment:
+---
+description: "Orchestrate end-to-end feature development from requirements to deployment"
+argument-hint: "<feature description> [--methodology tdd|bdd|ddd] [--complexity simple|medium|complex]"
+---
 
-[Extended thinking: This workflow orchestrates specialized agents through comprehensive feature development phases - from discovery and planning through implementation, testing, and deployment. Each phase builds on previous outputs, ensuring coherent feature delivery. The workflow supports multiple development methodologies (traditional, TDD/BDD, DDD), feature complexity levels, and modern deployment strategies including feature flags, gradual rollouts, and observability-first development. Agents receive detailed context from previous phases to maintain consistency and quality throughout the development lifecycle.]
+# Feature Development Orchestrator
 
-## Configuration Options
+## CRITICAL BEHAVIORAL RULES
 
-### Development Methodology
+You MUST follow these rules exactly. Violating any of them is a failure.
 
-- **traditional**: Sequential development with testing after implementation
-- **tdd**: Test-Driven Development with red-green-refactor cycles
-- **bdd**: Behavior-Driven Development with scenario-based testing
-- **ddd**: Domain-Driven Design with bounded contexts and aggregates
+1. **Execute steps in order.** Do NOT skip ahead, reorder, or merge steps.
+2. **Write output files.** Each step MUST produce its output file in `.feature-dev/` before the next step begins. Read from prior step files — do NOT rely on context window memory.
+3. **Stop at checkpoints.** When you reach a `PHASE CHECKPOINT`, you MUST stop and wait for explicit user approval before continuing. Use the AskUserQuestion tool with clear options.
+4. **Halt on failure.** If any step fails (agent error, test failure, missing dependency), STOP immediately. Present the error and ask the user how to proceed. Do NOT silently continue.
+5. **Use only local agents.** All `subagent_type` references use agents bundled with this plugin or `general-purpose`. No cross-plugin dependencies.
+6. **Never enter plan mode autonomously.** Do NOT use EnterPlanMode. This command IS the plan — execute it.
 
-### Feature Complexity
+## Pre-flight Checks
 
-- **simple**: Single service, minimal integration (1-2 days)
-- **medium**: Multiple services, moderate integration (3-5 days)
-- **complex**: Cross-domain, extensive integration (1-2 weeks)
-- **epic**: Major architectural changes, multiple teams (2+ weeks)
+Before starting, perform these checks:
 
-### Deployment Strategy
+### 1. Check for existing session
 
-- **direct**: Immediate rollout to all users
-- **canary**: Gradual rollout starting with 5% of traffic
-- **feature-flag**: Controlled activation via feature toggles
-- **blue-green**: Zero-downtime deployment with instant rollback
-- **a-b-test**: Split traffic for experimentation and metrics
+Check if `.feature-dev/state.json` exists:
 
-## Phase 1: Discovery & Requirements Planning
+- If it exists and `status` is `"in_progress"`: Read it, display the current step, and ask the user:
 
-1. **Business Analysis & Requirements**
-   - Use Task tool with subagent_type="business-analytics::business-analyst"
-   - Prompt: "Analyze feature requirements for: $ARGUMENTS. Define user stories, acceptance criteria, success metrics, and business value. Identify stakeholders, dependencies, and risks. Create feature specification document with clear scope boundaries."
-   - Expected output: Requirements document with user stories, success metrics, risk assessment
-   - Context: Initial feature request and business context
+  ```
+  Found an in-progress feature development session:
+  Feature: [name from state]
+  Current step: [step from state]
 
-2. **Technical Architecture Design**
-   - Use Task tool with subagent_type="comprehensive-review::architect-review"
-   - Prompt: "Design technical architecture for feature: $ARGUMENTS. Using requirements: [include business analysis from step 1]. Define service boundaries, API contracts, data models, integration points, and technology stack. Consider scalability, performance, and security requirements."
-   - Expected output: Technical design document with architecture diagrams, API specifications, data models
-   - Context: Business requirements, existing system architecture
+  1. Resume from where we left off
+  2. Start fresh (archives existing session)
+  ```
 
-3. **Feasibility & Risk Assessment**
-   - Use Task tool with subagent_type="security-scanning::security-auditor"
-   - Prompt: "Assess security implications and risks for feature: $ARGUMENTS. Review architecture: [include technical design from step 2]. Identify security requirements, compliance needs, data privacy concerns, and potential vulnerabilities."
-   - Expected output: Security assessment with risk matrix, compliance checklist, mitigation strategies
-   - Context: Technical design, regulatory requirements
+- If it exists and `status` is `"complete"`: Ask whether to archive and start fresh.
 
-## Phase 2: Implementation & Development
+### 2. Initialize state
 
-4. **Backend Services Implementation**
-   - Use Task tool with subagent_type="backend-architect"
-   - Prompt: "Implement backend services for: $ARGUMENTS. Follow technical design: [include architecture from step 2]. Build RESTful/GraphQL APIs, implement business logic, integrate with data layer, add resilience patterns (circuit breakers, retries), implement caching strategies. Include feature flags for gradual rollout."
-   - Expected output: Backend services with APIs, business logic, database integration, feature flags
-   - Context: Technical design, API contracts, data models
+Create `.feature-dev/` directory and `state.json`:
 
-5. **Frontend Implementation**
-   - Use Task tool with subagent_type="frontend-mobile-development::frontend-developer"
-   - Prompt: "Build frontend components for: $ARGUMENTS. Integrate with backend APIs: [include API endpoints from step 4]. Implement responsive UI, state management, error handling, loading states, and analytics tracking. Add feature flag integration for A/B testing capabilities."
-   - Expected output: Frontend components with API integration, state management, analytics
-   - Context: Backend APIs, UI/UX designs, user stories
+```json
+{
+  "feature": "$ARGUMENTS",
+  "status": "in_progress",
+  "methodology": "traditional",
+  "complexity": "medium",
+  "current_step": 1,
+  "current_phase": 1,
+  "completed_steps": [],
+  "files_created": [],
+  "started_at": "ISO_TIMESTAMP",
+  "last_updated": "ISO_TIMESTAMP"
+}
+```
 
-6. **Data Pipeline & Integration**
-   - Use Task tool with subagent_type="data-engineering::data-engineer"
-   - Prompt: "Build data pipelines for: $ARGUMENTS. Design ETL/ELT processes, implement data validation, create analytics events, set up data quality monitoring. Integrate with product analytics platforms for feature usage tracking."
-   - Expected output: Data pipelines, analytics events, data quality checks
-   - Context: Data requirements, analytics needs, existing data infrastructure
+Parse `$ARGUMENTS` for `--methodology` and `--complexity` flags. Use defaults if not specified.
 
-## Phase 3: Testing & Quality Assurance
+### 3. Parse feature description
 
-7. **Automated Test Suite**
-   - Use Task tool with subagent_type="unit-testing::test-automator"
-   - Prompt: "Create comprehensive test suite for: $ARGUMENTS. Write unit tests for backend: [from step 4] and frontend: [from step 5]. Add integration tests for API endpoints, E2E tests for critical user journeys, performance tests for scalability validation. Ensure minimum 80% code coverage."
-   - Expected output: Test suites with unit, integration, E2E, and performance tests
-   - Context: Implementation code, acceptance criteria, test requirements
+Extract the feature description from `$ARGUMENTS` (everything before the flags). This is referenced as `$FEATURE` in prompts below.
 
-8. **Security Validation**
-   - Use Task tool with subagent_type="security-scanning::security-auditor"
-   - Prompt: "Perform security testing for: $ARGUMENTS. Review implementation: [include backend and frontend from steps 4-5]. Run OWASP checks, penetration testing, dependency scanning, and compliance validation. Verify data encryption, authentication, and authorization."
-   - Expected output: Security test results, vulnerability report, remediation actions
-   - Context: Implementation code, security requirements
+---
 
-9. **Performance Optimization**
-   - Use Task tool with subagent_type="application-performance::performance-engineer"
-   - Prompt: "Optimize performance for: $ARGUMENTS. Analyze backend services: [from step 4] and frontend: [from step 5]. Profile code, optimize queries, implement caching, reduce bundle sizes, improve load times. Set up performance budgets and monitoring."
-   - Expected output: Performance improvements, optimization report, performance metrics
-   - Context: Implementation code, performance requirements
+## Phase 1: Discovery (Steps 1–2) — Interactive
 
-## Phase 4: Deployment & Monitoring
+### Step 1: Requirements Gathering
 
-10. **Deployment Strategy & Pipeline**
-    - Use Task tool with subagent_type="deployment-strategies::deployment-engineer"
-    - Prompt: "Prepare deployment for: $ARGUMENTS. Create CI/CD pipeline with automated tests: [from step 7]. Configure feature flags for gradual rollout, implement blue-green deployment, set up rollback procedures. Create deployment runbook and rollback plan."
-    - Expected output: CI/CD pipeline, deployment configuration, rollback procedures
-    - Context: Test suites, infrastructure requirements, deployment strategy
+Gather requirements through interactive Q&A. Ask ONE question at a time using the AskUserQuestion tool. Do NOT ask all questions at once.
 
-11. **Observability & Monitoring**
-    - Use Task tool with subagent_type="observability-monitoring::observability-engineer"
-    - Prompt: "Set up observability for: $ARGUMENTS. Implement distributed tracing, custom metrics, error tracking, and alerting. Create dashboards for feature usage, performance metrics, error rates, and business KPIs. Set up SLOs/SLIs with automated alerts."
-    - Expected output: Monitoring dashboards, alerts, SLO definitions, observability infrastructure
-    - Context: Feature implementation, success metrics, operational requirements
+**Questions to ask (in order):**
 
-12. **Documentation & Knowledge Transfer**
-    - Use Task tool with subagent_type="documentation-generation::docs-architect"
-    - Prompt: "Generate comprehensive documentation for: $ARGUMENTS. Create API documentation, user guides, deployment guides, troubleshooting runbooks. Include architecture diagrams, data flow diagrams, and integration guides. Generate automated changelog from commits."
-    - Expected output: API docs, user guides, runbooks, architecture documentation
-    - Context: All previous phases' outputs
+1. **Problem Statement**: "What problem does this feature solve? Who is the user and what's their pain point?"
+2. **Acceptance Criteria**: "What are the key acceptance criteria? When is this feature 'done'?"
+3. **Scope Boundaries**: "What is explicitly OUT of scope for this feature?"
+4. **Technical Constraints**: "Any technical constraints? (e.g., must use existing auth system, specific DB, latency requirements)"
+5. **Dependencies**: "Does this feature depend on or affect other features/services?"
 
-## Execution Parameters
+After gathering answers, write the requirements document:
 
-### Required Parameters
+**Output file:** `.feature-dev/01-requirements.md`
 
-- **--feature**: Feature name and description
-- **--methodology**: Development approach (traditional|tdd|bdd|ddd)
-- **--complexity**: Feature complexity level (simple|medium|complex|epic)
+```markdown
+# Requirements: $FEATURE
 
-### Optional Parameters
+## Problem Statement
 
-- **--deployment-strategy**: Deployment approach (direct|canary|feature-flag|blue-green|a-b-test)
-- **--test-coverage-min**: Minimum test coverage threshold (default: 80%)
-- **--performance-budget**: Performance requirements (e.g., <200ms response time)
-- **--rollout-percentage**: Initial rollout percentage for gradual deployment (default: 5%)
-- **--feature-flag-service**: Feature flag provider (launchdarkly|split|unleash|custom)
-- **--analytics-platform**: Analytics integration (segment|amplitude|mixpanel|custom)
-- **--monitoring-stack**: Observability tools (datadog|newrelic|grafana|custom)
+[From Q1]
 
-## Success Criteria
+## Acceptance Criteria
 
-- All acceptance criteria from business requirements are met
-- Test coverage exceeds minimum threshold (80% default)
-- Security scan shows no critical vulnerabilities
-- Performance meets defined budgets and SLOs
-- Feature flags configured for controlled rollout
-- Monitoring and alerting fully operational
-- Documentation complete and approved
-- Successful deployment to production with rollback capability
-- Product analytics tracking feature usage
-- A/B test metrics configured (if applicable)
+[From Q2 — formatted as checkboxes]
 
-## Rollback Strategy
+## Scope
 
-If issues arise during or after deployment:
+### In Scope
 
-1. Immediate feature flag disable (< 1 minute)
-2. Blue-green traffic switch (< 5 minutes)
-3. Full deployment rollback via CI/CD (< 15 minutes)
-4. Database migration rollback if needed (coordinate with data team)
-5. Incident post-mortem and fixes before re-deployment
+[Derived from answers]
 
-Feature description: $ARGUMENTS
+### Out of Scope
+
+[From Q3]
+
+## Technical Constraints
+
+[From Q4]
+
+## Dependencies
+
+[From Q5]
+
+## Methodology: [tdd|bdd|ddd|traditional]
+
+## Complexity: [simple|medium|complex]
+```
+
+Update `state.json`: set `current_step` to 2, add `"01-requirements.md"` to `files_created`, add step 1 to `completed_steps`.
+
+### Step 2: Architecture & Security Design
+
+Read `.feature-dev/01-requirements.md` to load requirements context.
+
+Use the Task tool to launch the architecture agent:
+
+```
+Task:
+  subagent_type: "backend-architect"
+  description: "Design architecture for $FEATURE"
+  prompt: |
+    Design the technical architecture for this feature.
+
+    ## Requirements
+    [Insert full contents of .feature-dev/01-requirements.md]
+
+    ## Deliverables
+    1. **Service/component design**: What components are needed, their responsibilities, and boundaries
+    2. **API design**: Endpoints, request/response schemas, error handling
+    3. **Data model**: Database tables/collections, relationships, migrations needed
+    4. **Security considerations**: Auth requirements, input validation, data protection, OWASP concerns
+    5. **Integration points**: How this connects to existing services/systems
+    6. **Risk assessment**: Technical risks and mitigation strategies
+
+    Write your complete architecture design as a single markdown document.
+```
+
+Save the agent's output to `.feature-dev/02-architecture.md`.
+
+Update `state.json`: set `current_step` to "checkpoint-1", add step 2 to `completed_steps`.
+
+---
+
+## PHASE CHECKPOINT 1 — User Approval Required
+
+You MUST stop here and present the architecture for review.
+
+Display a summary of the architecture from `.feature-dev/02-architecture.md` (key components, API endpoints, data model overview) and ask:
+
+```
+Architecture design is complete. Please review .feature-dev/02-architecture.md
+
+1. Approve — proceed to implementation
+2. Request changes — tell me what to adjust
+3. Pause — save progress and stop here
+```
+
+Do NOT proceed to Phase 2 until the user selects option 1. If they select option 2, revise the architecture and re-checkpoint. If option 3, update `state.json` status and stop.
+
+---
+
+## Phase 2: Implementation (Steps 3–5)
+
+### Step 3: Backend Implementation
+
+Read `.feature-dev/01-requirements.md` and `.feature-dev/02-architecture.md`.
+
+Use the Task tool to launch the backend architect for implementation:
+
+```
+Task:
+  subagent_type: "backend-architect"
+  description: "Implement backend for $FEATURE"
+  prompt: |
+    Implement the backend for this feature based on the approved architecture.
+
+    ## Requirements
+    [Insert contents of .feature-dev/01-requirements.md]
+
+    ## Architecture
+    [Insert contents of .feature-dev/02-architecture.md]
+
+    ## Instructions
+    1. Implement the API endpoints, business logic, and data access layer as designed
+    2. Include data layer components (models, migrations, repositories) as specified in the architecture
+    3. Add input validation and error handling
+    4. Follow the project's existing code patterns and conventions
+    5. If methodology is TDD: write failing tests first, then implement
+    6. Include inline comments only where logic is non-obvious
+
+    Write all code files. Report what files were created/modified.
+```
+
+Save a summary of what was implemented to `.feature-dev/03-backend.md` (list of files created/modified, key decisions, any deviations from architecture).
+
+Update `state.json`: set `current_step` to 4, add step 3 to `completed_steps`.
+
+### Step 4: Frontend Implementation
+
+Read `.feature-dev/01-requirements.md`, `.feature-dev/02-architecture.md`, and `.feature-dev/03-backend.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Implement frontend for $FEATURE"
+  prompt: |
+    You are a frontend developer. Implement the frontend components for this feature.
+
+    ## Requirements
+    [Insert contents of .feature-dev/01-requirements.md]
+
+    ## Architecture
+    [Insert contents of .feature-dev/02-architecture.md]
+
+    ## Backend Implementation
+    [Insert contents of .feature-dev/03-backend.md]
+
+    ## Instructions
+    1. Build UI components that integrate with the backend API endpoints
+    2. Implement state management, form handling, and error states
+    3. Add loading states and optimistic updates where appropriate
+    4. Follow the project's existing frontend patterns and component conventions
+    5. Ensure responsive design and accessibility basics (semantic HTML, ARIA labels, keyboard nav)
+
+    Write all code files. Report what files were created/modified.
+```
+
+Save a summary to `.feature-dev/04-frontend.md`.
+
+**Note:** If the feature has no frontend component (pure backend/API), skip this step — write a brief note in `04-frontend.md` explaining why it was skipped, and continue.
+
+Update `state.json`: set `current_step` to 5, add step 4 to `completed_steps`.
+
+### Step 5: Testing & Validation
+
+Read `.feature-dev/03-backend.md` and `.feature-dev/04-frontend.md`.
+
+Launch three agents in parallel using multiple Task tool calls in a single response:
+
+**5a. Test Suite Creation:**
+
+```
+Task:
+  subagent_type: "test-automator"
+  description: "Create test suite for $FEATURE"
+  prompt: |
+    Create a comprehensive test suite for this feature.
+
+    ## What was implemented
+    ### Backend
+    [Insert contents of .feature-dev/03-backend.md]
+
+    ### Frontend
+    [Insert contents of .feature-dev/04-frontend.md]
+
+    ## Instructions
+    1. Write unit tests for all new backend functions/methods
+    2. Write integration tests for API endpoints
+    3. Write frontend component tests if applicable
+    4. Cover: happy path, edge cases, error handling, boundary conditions
+    5. Follow existing test patterns and frameworks in the project
+    6. Target 80%+ code coverage for new code
+
+    Write all test files. Report what test files were created and what they cover.
+```
+
+**5b. Security Review:**
+
+```
+Task:
+  subagent_type: "security-auditor"
+  description: "Security review of $FEATURE"
+  prompt: |
+    Perform a security review of this feature implementation.
+
+    ## Architecture
+    [Insert contents of .feature-dev/02-architecture.md]
+
+    ## Backend Implementation
+    [Insert contents of .feature-dev/03-backend.md]
+
+    ## Frontend Implementation
+    [Insert contents of .feature-dev/04-frontend.md]
+
+    Review for: OWASP Top 10, authentication/authorization flaws, input validation gaps,
+    data protection issues, dependency vulnerabilities, and any security anti-patterns.
+
+    Provide findings with severity, location, and specific fix recommendations.
+```
+
+**5c. Performance Review:**
+
+```
+Task:
+  subagent_type: "performance-engineer"
+  description: "Performance review of $FEATURE"
+  prompt: |
+    Review the performance of this feature implementation.
+
+    ## Architecture
+    [Insert contents of .feature-dev/02-architecture.md]
+
+    ## Backend Implementation
+    [Insert contents of .feature-dev/03-backend.md]
+
+    ## Frontend Implementation
+    [Insert contents of .feature-dev/04-frontend.md]
+
+    Review for: N+1 queries, missing indexes, unoptimized queries, memory leaks,
+    missing caching opportunities, large payloads, slow rendering paths.
+
+    Provide findings with impact estimates and specific optimization recommendations.
+```
+
+After all three complete, consolidate results into `.feature-dev/05-testing.md`:
+
+```markdown
+# Testing & Validation: $FEATURE
+
+## Test Suite
+
+[Summary from 5a — files created, coverage areas]
+
+## Security Findings
+
+[Summary from 5b — findings by severity]
+
+## Performance Findings
+
+[Summary from 5c — findings by impact]
+
+## Action Items
+
+[List any critical/high findings that need to be addressed before delivery]
+```
+
+If there are Critical or High severity findings from security or performance review, address them now before proceeding. Apply fixes and re-validate.
+
+Update `state.json`: set `current_step` to "checkpoint-2", add step 5 to `completed_steps`.
+
+---
+
+## PHASE CHECKPOINT 2 — User Approval Required
+
+Display a summary of testing and validation results from `.feature-dev/05-testing.md` and ask:
+
+```
+Testing and validation complete. Please review .feature-dev/05-testing.md
+
+Test coverage: [summary]
+Security findings: [X critical, Y high, Z medium]
+Performance findings: [X critical, Y high, Z medium]
+
+1. Approve — proceed to deployment & documentation
+2. Request changes — tell me what to fix
+3. Pause — save progress and stop here
+```
+
+Do NOT proceed to Phase 3 until the user approves.
+
+---
+
+## Phase 3: Delivery (Steps 6–7)
+
+### Step 6: Deployment & Monitoring
+
+Read `.feature-dev/02-architecture.md` and `.feature-dev/05-testing.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Create deployment config for $FEATURE"
+  prompt: |
+    You are a deployment engineer. Create the deployment and monitoring configuration for this feature.
+
+    ## Architecture
+    [Insert contents of .feature-dev/02-architecture.md]
+
+    ## Testing Results
+    [Insert contents of .feature-dev/05-testing.md]
+
+    ## Instructions
+    1. Create or update CI/CD pipeline configuration for the new code
+    2. Add feature flag configuration if the feature should be gradually rolled out
+    3. Define health checks and readiness probes for new services/endpoints
+    4. Create monitoring alerts for key metrics (error rate, latency, throughput)
+    5. Write a deployment runbook with rollback steps
+    6. Follow existing deployment patterns in the project
+
+    Write all configuration files. Report what was created/modified.
+```
+
+Save output to `.feature-dev/06-deployment.md`.
+
+Update `state.json`: set `current_step` to 7, add step 6 to `completed_steps`.
+
+### Step 7: Documentation & Handoff
+
+Read all previous `.feature-dev/*.md` files.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Write documentation for $FEATURE"
+  prompt: |
+    You are a technical writer. Create documentation for this feature.
+
+    ## Feature Context
+    [Insert contents of .feature-dev/01-requirements.md]
+
+    ## Architecture
+    [Insert contents of .feature-dev/02-architecture.md]
+
+    ## Implementation Summary
+    ### Backend: [Insert contents of .feature-dev/03-backend.md]
+    ### Frontend: [Insert contents of .feature-dev/04-frontend.md]
+
+    ## Deployment
+    [Insert contents of .feature-dev/06-deployment.md]
+
+    ## Instructions
+    1. Write API documentation for new endpoints (request/response examples)
+    2. Update or create user-facing documentation if applicable
+    3. Write a brief architecture decision record (ADR) explaining key design choices
+    4. Create a handoff summary: what was built, how to test it, known limitations
+
+    Write documentation files. Report what was created/modified.
+```
+
+Save output to `.feature-dev/07-documentation.md`.
+
+Update `state.json`: set `current_step` to "complete", add step 7 to `completed_steps`.
+
+---
+
+## Completion
+
+Update `state.json`:
+
+- Set `status` to `"complete"`
+- Set `last_updated` to current timestamp
+
+Present the final summary:
+
+```
+Feature development complete: $FEATURE
+
+## Files Created
+[List all .feature-dev/ output files]
+
+## Implementation Summary
+- Requirements: .feature-dev/01-requirements.md
+- Architecture: .feature-dev/02-architecture.md
+- Backend: .feature-dev/03-backend.md
+- Frontend: .feature-dev/04-frontend.md
+- Testing: .feature-dev/05-testing.md
+- Deployment: .feature-dev/06-deployment.md
+- Documentation: .feature-dev/07-documentation.md
+
+## Next Steps
+1. Review all generated code and documentation
+2. Run the full test suite to verify everything passes
+3. Create a pull request with the implementation
+4. Deploy using the runbook in .feature-dev/06-deployment.md
+```

plugins/backend-development/skills/api-design-principles/SKILL.md

@@ -516,13 +516,3 @@ def create_context():
 - **Poor Documentation**: Undocumented APIs frustrate developers
 - **Ignoring HTTP Semantics**: POST for idempotent operations breaks expectations
 - **Tight Coupling**: API structure shouldn't mirror database schema
-
-## Resources
-
-- **references/rest-best-practices.md**: Comprehensive REST API design guide
-- **references/graphql-schema-design.md**: GraphQL schema patterns and anti-patterns
-- **references/api-versioning-strategies.md**: Versioning approaches and migration paths
-- **assets/rest-api-template.py**: FastAPI REST API template
-- **assets/graphql-schema-template.graphql**: Complete GraphQL schema example
-- **assets/api-design-checklist.md**: Pre-implementation review checklist
-- **scripts/openapi-generator.py**: Generate OpenAPI specs from code

plugins/backend-development/skills/architecture-patterns/SKILL.md

@@ -464,31 +464,3 @@ class OrderRepository:
         await self._publish_events(order._events)
         order._events.clear()
 ```
-
-## Resources
-
-- **references/clean-architecture-guide.md**: Detailed layer breakdown
-- **references/hexagonal-architecture-guide.md**: Ports and adapters patterns
-- **references/ddd-tactical-patterns.md**: Entities, value objects, aggregates
-- **assets/clean-architecture-template/**: Complete project structure
-- **assets/ddd-examples/**: Domain modeling examples
-
-## Best Practices
-
-1. **Dependency Rule**: Dependencies always point inward
-2. **Interface Segregation**: Small, focused interfaces
-3. **Business Logic in Domain**: Keep frameworks out of core
-4. **Test Independence**: Core testable without infrastructure
-5. **Bounded Contexts**: Clear domain boundaries
-6. **Ubiquitous Language**: Consistent terminology
-7. **Thin Controllers**: Delegate to use cases
-8. **Rich Domain Models**: Behavior with data
-
-## Common Pitfalls
-
-- **Anemic Domain**: Entities with only data, no behavior
-- **Framework Coupling**: Business logic depends on frameworks
-- **Fat Controllers**: Business logic in controllers
-- **Repository Leakage**: Exposing ORM objects
-- **Missing Abstractions**: Concrete dependencies in core
-- **Over-Engineering**: Clean architecture for simple CRUD

plugins/backend-development/skills/cqrs-implementation/SKILL.md

@@ -547,8 +547,3 @@ class ConsistentQueryHandler:
 - **Don't couple read/write schemas** - Independent evolution
 - **Don't over-engineer** - Start simple
 - **Don't ignore consistency SLAs** - Define acceptable lag
-
-## Resources
-
-- [CQRS Pattern](https://martinfowler.com/bliki/CQRS.html)
-- [Microsoft CQRS Guidance](https://docs.microsoft.com/en-us/azure/architecture/patterns/cqrs)

plugins/backend-development/skills/event-store-design/SKILL.md

@@ -429,9 +429,3 @@ Capacity: On-demand or provisioned based on throughput needs
 - **Don't store large payloads** - Keep events small
 - **Don't skip optimistic concurrency** - Prevents data corruption
 - **Don't ignore backpressure** - Handle slow consumers
-
-## Resources
-
-- [EventStoreDB](https://www.eventstore.com/)
-- [Marten Events](https://martendb.io/events/)
-- [Event Sourcing Pattern](https://docs.microsoft.com/en-us/azure/architecture/patterns/event-sourcing)

plugins/backend-development/skills/microservices-patterns/SKILL.md

@@ -562,34 +562,3 @@ async def call_payment_service(payment_data: dict):
         payment_data
     )
 ```
-
-## Resources
-
-- **references/service-decomposition-guide.md**: Breaking down monoliths
-- **references/communication-patterns.md**: Sync vs async patterns
-- **references/saga-implementation.md**: Distributed transactions
-- **assets/circuit-breaker.py**: Production circuit breaker
-- **assets/event-bus-template.py**: Kafka event bus implementation
-- **assets/api-gateway-template.py**: Complete API gateway
-
-## Best Practices
-
-1. **Service Boundaries**: Align with business capabilities
-2. **Database Per Service**: No shared databases
-3. **API Contracts**: Versioned, backward compatible
-4. **Async When Possible**: Events over direct calls
-5. **Circuit Breakers**: Fail fast on service failures
-6. **Distributed Tracing**: Track requests across services
-7. **Service Registry**: Dynamic service discovery
-8. **Health Checks**: Liveness and readiness probes
-
-## Common Pitfalls
-
-- **Distributed Monolith**: Tightly coupled services
-- **Chatty Services**: Too many inter-service calls
-- **Shared Databases**: Tight coupling through data
-- **No Circuit Breakers**: Cascade failures
-- **Synchronous Everything**: Tight coupling, poor resilience
-- **Premature Microservices**: Starting with microservices
-- **Ignoring Network Failures**: Assuming reliable network
-- **No Compensation Logic**: Can't undo failed transactions

plugins/backend-development/skills/projection-patterns/SKILL.md

@@ -483,8 +483,3 @@ class CustomerActivityProjection(Projection):
 - **Don't skip error handling** - Log and alert on failures
 - **Don't ignore ordering** - Events must be processed in order
 - **Don't over-normalize** - Denormalize for query patterns
-
-## Resources
-
-- [CQRS Pattern](https://docs.microsoft.com/en-us/azure/architecture/patterns/cqrs)
-- [Projection Building Blocks](https://zimarev.com/blog/event-sourcing/projections/)

plugins/backend-development/skills/saga-orchestration/SKILL.md

@@ -477,8 +477,3 @@ class TimeoutSagaOrchestrator(SagaOrchestrator):
 - **Don't skip compensation testing** - Most critical part
 - **Don't couple services** - Use async messaging
 - **Don't ignore partial failures** - Handle gracefully
-
-## Resources
-
-- [Saga Pattern](https://microservices.io/patterns/data/saga.html)
-- [Designing Data-Intensive Applications](https://dataintensive.net/)

plugins/blockchain-web3/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "blockchain-web3",
+  "version": "1.2.2",
+  "description": "Smart contract development with Solidity, DeFi protocol implementation, NFT platforms, and Web3 application architecture",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/blockchain-web3/skills/defi-protocol-templates/SKILL.md

@@ -422,33 +422,3 @@ contract FlashLoanReceiver is IFlashLoanReceiver {
     }
 }
 ```
-
-## Resources
-
-- **references/staking.md**: Staking mechanics and reward distribution
-- **references/liquidity-pools.md**: AMM mathematics and pricing
-- **references/governance-tokens.md**: Governance and voting systems
-- **references/lending-protocols.md**: Lending/borrowing implementation
-- **references/flash-loans.md**: Flash loan security and use cases
-- **assets/staking-contract.sol**: Production staking template
-- **assets/amm-contract.sol**: Full AMM implementation
-- **assets/governance-token.sol**: Governance system
-- **assets/lending-protocol.sol**: Lending platform template
-
-## Best Practices
-
-1. **Use Established Libraries**: OpenZeppelin, Solmate
-2. **Test Thoroughly**: Unit tests, integration tests, fuzzing
-3. **Audit Before Launch**: Professional security audits
-4. **Start Simple**: MVP first, add features incrementally
-5. **Monitor**: Track contract health and user activity
-6. **Upgradability**: Consider proxy patterns for upgrades
-7. **Emergency Controls**: Pause mechanisms for critical issues
-
-## Common DeFi Patterns
-
-- **Time-Weighted Average Price (TWAP)**: Price oracle resistance
-- **Liquidity Mining**: Incentivize liquidity provision
-- **Vesting**: Lock tokens with gradual release
-- **Multisig**: Require multiple signatures for critical operations
-- **Timelocks**: Delay execution of governance decisions

plugins/blockchain-web3/skills/nft-standards/SKILL.md

@@ -353,31 +353,3 @@ contract OptimizedNFT is ERC721A {
     }
 }
 ```
-
-## Resources
-
-- **references/erc721.md**: ERC-721 specification details
-- **references/erc1155.md**: ERC-1155 multi-token standard
-- **references/metadata-standards.md**: Metadata best practices
-- **references/enumeration.md**: Token enumeration patterns
-- **assets/erc721-contract.sol**: Production ERC-721 template
-- **assets/erc1155-contract.sol**: Production ERC-1155 template
-- **assets/metadata-schema.json**: Standard metadata format
-- **assets/metadata-uploader.py**: IPFS upload utility
-
-## Best Practices
-
-1. **Use OpenZeppelin**: Battle-tested implementations
-2. **Pin Metadata**: Use IPFS with pinning service
-3. **Implement Royalties**: EIP-2981 for marketplace compatibility
-4. **Gas Optimization**: Use ERC721A for batch minting
-5. **Reveal Mechanism**: Placeholder → reveal pattern
-6. **Enumeration**: Support walletOfOwner for marketplaces
-7. **Whitelist**: Merkle trees for efficient whitelisting
-
-## Marketplace Integration
-
-- OpenSea: ERC-721/1155, metadata standards
-- LooksRare: Royalty enforcement
-- Rarible: Protocol fees, lazy minting
-- Blur: Gas-optimized trading

plugins/blockchain-web3/skills/solidity-security/SKILL.md

@@ -494,32 +494,3 @@ contract WellDocumentedContract {
     }
 }
 ```
-
-## Resources
-
-- **references/reentrancy.md**: Comprehensive reentrancy prevention
-- **references/access-control.md**: Role-based access patterns
-- **references/overflow-underflow.md**: SafeMath and integer safety
-- **references/gas-optimization.md**: Gas saving techniques
-- **references/vulnerability-patterns.md**: Common vulnerability catalog
-- **assets/solidity-contracts-templates.sol**: Secure contract templates
-- **assets/security-checklist.md**: Pre-audit checklist
-- **scripts/analyze-contract.sh**: Static analysis tools
-
-## Tools for Security Analysis
-
-- **Slither**: Static analysis tool
-- **Mythril**: Security analysis tool
-- **Echidna**: Fuzzing tool
-- **Manticore**: Symbolic execution
-- **Securify**: Automated security scanner
-
-## Common Pitfalls
-
-1. **Using `tx.origin` for Authentication**: Use `msg.sender` instead
-2. **Unchecked External Calls**: Always check return values
-3. **Delegatecall to Untrusted Contracts**: Can hijack your contract
-4. **Floating Pragma**: Pin to specific Solidity version
-5. **Missing Events**: Emit events for state changes
-6. **Excessive Gas in Loops**: Can hit block gas limit
-7. **No Upgrade Path**: Consider proxy patterns if upgrades needed

plugins/blockchain-web3/skills/web3-testing/SKILL.md

@@ -388,28 +388,3 @@ jobs:
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v2
 ```
-
-## Resources
-
-- **references/hardhat-setup.md**: Hardhat configuration guide
-- **references/foundry-setup.md**: Foundry testing framework
-- **references/test-patterns.md**: Testing best practices
-- **references/mainnet-forking.md**: Fork testing strategies
-- **references/contract-verification.md**: Etherscan verification
-- **assets/hardhat-config.js**: Complete Hardhat configuration
-- **assets/test-suite.js**: Comprehensive test examples
-- **assets/foundry.toml**: Foundry configuration
-- **scripts/test-contract.sh**: Automated testing script
-
-## Best Practices
-
-1. **Test Coverage**: Aim for >90% coverage
-2. **Edge Cases**: Test boundary conditions
-3. **Gas Limits**: Verify functions don't hit block gas limit
-4. **Reentrancy**: Test for reentrancy vulnerabilities
-5. **Access Control**: Test unauthorized access attempts
-6. **Events**: Verify event emissions
-7. **Fixtures**: Use fixtures to avoid code duplication
-8. **Mainnet Fork**: Test with real contracts
-9. **Fuzzing**: Use property-based testing
-10. **CI/CD**: Automate testing on every commit

plugins/business-analytics/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "business-analytics",
+  "version": "1.2.2",
+  "description": "Business metrics analysis, KPI tracking, financial reporting, and data-driven decision making",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/business-analytics/skills/data-storytelling/SKILL.md

@@ -445,9 +445,3 @@ Present ranges:
 - **Don't use jargon** - Match audience vocabulary
 - **Don't show methodology first** - Context, then method
 - **Don't forget the narrative** - Numbers need meaning
-
-## Resources
-
-- [Storytelling with Data (Cole Nussbaumer)](https://www.storytellingwithdata.com/)
-- [The Pyramid Principle (Barbara Minto)](https://www.amazon.com/Pyramid-Principle-Logic-Writing-Thinking/dp/0273710516)
-- [Resonate (Nancy Duarte)](https://www.duarte.com/resonate/)

plugins/business-analytics/skills/kpi-dashboard-design/SKILL.md

@@ -420,9 +420,3 @@ for alert in alerts:
 - **Don't use 3D charts** - They distort perception
 - **Don't hide methodology** - Document calculations
 - **Don't ignore mobile** - Ensure responsive design
-
-## Resources
-
-- [Stephen Few's Dashboard Design](https://www.perceptualedge.com/articles/visual_business_intelligence/rules_for_using_color.pdf)
-- [Edward Tufte's Principles](https://www.edwardtufte.com/tufte/)
-- [Google Data Studio Gallery](https://datastudio.google.com/gallery)

plugins/c4-architecture/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "c4-architecture",
+  "version": "1.0.0",
+  "description": "Comprehensive C4 architecture documentation workflow with bottom-up code analysis, component synthesis, container mapping, and context diagram generation",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/c4-architecture/commands/c4-architecture.md

@@ -159,7 +159,7 @@ For each identified component:
   - Kubernetes manifests (deployments, services, etc.)
   - Docker Compose files
   - Terraform/CloudFormation configs
-  - Cloud service definitions (AWS Lambda, Azure Functions, etc.)
+  - Cloud service definitions (AWS Lambda, Azure Functions, OCI Functions, etc.)
   - CI/CD pipeline definitions
 
 ### 3.2 Map Components to Containers

plugins/cicd-automation/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "cicd-automation",
+  "version": "1.2.2",
+  "description": "CI/CD pipeline configuration, GitHub Actions/GitLab CI workflow setup, and automated deployment pipeline orchestration",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/cicd-automation/agents/cloud-architect.md

@@ -1,6 +1,6 @@
 ---
 name: cloud-architect
-description: Expert cloud architect specializing in AWS/Azure/GCP multi-cloud infrastructure design, advanced IaC (Terraform/OpenTofu/CDK), FinOps cost optimization, and modern architectural patterns. Masters serverless, microservices, security, compliance, and disaster recovery. Use PROACTIVELY for cloud architecture, cost optimization, migration planning, or multi-cloud strategies.
+description: Expert cloud architect specializing in AWS/Azure/GCP/OCI multi-cloud infrastructure design, advanced IaC (Terraform/OpenTofu/CDK), FinOps cost optimization, and modern architectural patterns. Masters serverless, microservices, security, compliance, and disaster recovery. Use PROACTIVELY for cloud architecture, cost optimization, migration planning, or multi-cloud strategies.
 model: opus
 ---
 
@@ -8,7 +8,7 @@ You are a cloud architect specializing in scalable, cost-effective, and secure m
 
 ## Purpose
 
-Expert cloud architect with deep knowledge of AWS, Azure, GCP, and emerging cloud technologies. Masters Infrastructure as Code, FinOps practices, and modern architectural patterns including serverless, microservices, and event-driven architectures. Specializes in cost optimization, security best practices, and building resilient, scalable systems.
+Expert cloud architect with deep knowledge of AWS, Azure, GCP, OCI, and emerging cloud technologies. Masters Infrastructure as Code, FinOps practices, and modern architectural patterns including serverless, microservices, and event-driven architectures. Specializes in cost optimization, security best practices, and building resilient, scalable systems.
 
 ## Capabilities
 
@@ -16,21 +16,22 @@ Expert cloud architect with deep knowledge of AWS, Azure, GCP, and emerging clou
 
 - **AWS**: EC2, Lambda, EKS, RDS, S3, VPC, IAM, CloudFormation, CDK, Well-Architected Framework
 - **Azure**: Virtual Machines, Functions, AKS, SQL Database, Blob Storage, Virtual Network, ARM templates, Bicep
-- **Google Cloud**: Compute Engine, Cloud Functions, GKE, Cloud SQL, Cloud Storage, VPC, Cloud Deployment Manager
+- **Google Cloud**: Compute Engine, Cloud Functions, GKE, Cloud SQL, Cloud Storage, VPC, Infrastructure Manager
+- **Oracle Cloud Infrastructure**: Compute, Functions, OKE, Autonomous Database, Object Storage, VCN, IAM, Resource Manager, FastConnect
 - **Multi-cloud strategies**: Cross-cloud networking, data replication, disaster recovery, vendor lock-in mitigation
 - **Edge computing**: CloudFlare, AWS CloudFront, Azure CDN, edge functions, IoT architectures
 
 ### Infrastructure as Code Mastery
 
 - **Terraform/OpenTofu**: Advanced module design, state management, workspaces, provider configurations
-- **Native IaC**: CloudFormation (AWS), ARM/Bicep (Azure), Cloud Deployment Manager (GCP)
+- **Native IaC**: CloudFormation (AWS), ARM/Bicep (Azure), Infrastructure Manager (GCP), Resource Manager (OCI)
 - **Modern IaC**: AWS CDK, Azure CDK, Pulumi with TypeScript/Python/Go
 - **GitOps**: Infrastructure automation with ArgoCD, Flux, GitHub Actions, GitLab CI/CD
-- **Policy as Code**: Open Policy Agent (OPA), AWS Config, Azure Policy, GCP Organization Policy
+- **Policy as Code**: Open Policy Agent (OPA), AWS Config, Azure Policy, GCP Organization Policy, OCI Cloud Guard
 
 ### Cost Optimization & FinOps
 
-- **Cost monitoring**: CloudWatch, Azure Cost Management, GCP Cost Management, third-party tools (CloudHealth, Cloudability)
+- **Cost monitoring**: CloudWatch, Azure Cost Management, GCP Cost Management, OCI Cost Analysis/Budgets, third-party tools (CloudHealth, Cloudability)
 - **Resource optimization**: Right-sizing recommendations, reserved instances, spot instances, committed use discounts
 - **Cost allocation**: Tagging strategies, chargeback models, showback reporting
 - **FinOps practices**: Cost anomaly detection, budget alerts, optimization automation
@@ -69,8 +70,8 @@ Expert cloud architect with deep knowledge of AWS, Azure, GCP, and emerging clou
 
 ### Modern DevOps Integration
 
-- **CI/CD pipelines**: GitHub Actions, GitLab CI, Azure DevOps, AWS CodePipeline
-- **Container orchestration**: EKS, AKS, GKE, self-managed Kubernetes
+- **CI/CD pipelines**: GitHub Actions, GitLab CI, Azure DevOps, AWS CodePipeline, OCI DevOps
+- **Container orchestration**: EKS, AKS, GKE, OKE, self-managed Kubernetes
 - **Observability**: Prometheus, Grafana, DataDog, New Relic, OpenTelemetry
 - **Infrastructure testing**: Terratest, InSpec, Checkov, Terrascan
 
@@ -94,7 +95,7 @@ Expert cloud architect with deep knowledge of AWS, Azure, GCP, and emerging clou
 
 ## Knowledge Base
 
-- AWS, Azure, GCP service catalogs and pricing models
+- AWS, Azure, GCP, OCI service catalogs and pricing models
 - Cloud provider security best practices and compliance standards
 - Infrastructure as Code tools and best practices
 - FinOps methodologies and cost optimization strategies
@@ -119,6 +120,7 @@ Expert cloud architect with deep knowledge of AWS, Azure, GCP, and emerging clou
 - "Design a multi-region, auto-scaling web application architecture on AWS with estimated monthly costs"
 - "Create a hybrid cloud strategy connecting on-premises data center with Azure"
 - "Optimize our GCP infrastructure costs while maintaining performance and availability"
+- "Design a regulated workload architecture spanning OCI and AWS with disaster recovery targets"
 - "Design a serverless event-driven architecture for real-time data processing"
 - "Plan a migration from monolithic application to microservices on Kubernetes"
 - "Implement a disaster recovery solution with 4-hour RTO across multiple cloud providers"

plugins/cicd-automation/agents/devops-troubleshooter.md

@@ -17,7 +17,7 @@ Expert DevOps troubleshooter with comprehensive knowledge of modern observabilit
 - **Logging platforms**: ELK Stack (Elasticsearch, Logstash, Kibana), Loki/Grafana, Fluentd/Fluent Bit
 - **APM solutions**: DataDog, New Relic, Dynatrace, AppDynamics, Instana, Honeycomb
 - **Metrics & monitoring**: Prometheus, Grafana, InfluxDB, VictoriaMetrics, Thanos
-- **Distributed tracing**: Jaeger, Zipkin, AWS X-Ray, OpenTelemetry, custom tracing
+- **Distributed tracing**: Jaeger, Zipkin, AWS X-Ray, OCI Application Performance Monitoring, OpenTelemetry, custom tracing
 - **Cloud-native observability**: OpenTelemetry collector, service mesh observability
 - **Synthetic monitoring**: Pingdom, Datadog Synthetics, custom health checks
 
@@ -34,7 +34,7 @@ Expert DevOps troubleshooter with comprehensive knowledge of modern observabilit
 
 - **Network analysis**: tcpdump, Wireshark, eBPF-based tools, network latency analysis
 - **DNS debugging**: dig, nslookup, DNS propagation, service discovery issues
-- **Load balancer issues**: AWS ALB/NLB, Azure Load Balancer, GCP Load Balancer debugging
+- **Load balancer issues**: AWS ALB/NLB, Azure Load Balancer, GCP Load Balancer, OCI Load Balancer debugging
 - **Firewall & security groups**: Network policies, security group misconfigurations
 - **Service mesh networking**: Traffic routing, circuit breaker issues, retry policies
 - **Cloud networking**: VPC connectivity, peering issues, NAT gateway problems
@@ -71,8 +71,9 @@ Expert DevOps troubleshooter with comprehensive knowledge of modern observabilit
 - **AWS debugging**: CloudWatch analysis, AWS CLI troubleshooting, service-specific issues
 - **Azure troubleshooting**: Azure Monitor, PowerShell debugging, resource group issues
 - **GCP debugging**: Cloud Logging, gcloud CLI, service account problems
+- **OCI troubleshooting**: OCI Logging and Monitoring, `oci` CLI debugging, compartment and IAM policy issues
 - **Multi-cloud issues**: Cross-cloud communication, identity federation problems
-- **Serverless debugging**: Lambda functions, Azure Functions, Cloud Functions issues
+- **Serverless debugging**: Lambda functions, Azure Functions, Cloud Functions, OCI Functions issues
 
 ### Security & Compliance Issues
 

plugins/cicd-automation/agents/kubernetes-architect.md

@@ -1,6 +1,6 @@
 ---
 name: kubernetes-architect
-description: Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration. Masters EKS/AKS/GKE, service mesh (Istio/Linkerd), progressive delivery, multi-tenancy, and platform engineering. Handles security, observability, cost optimization, and developer experience. Use PROACTIVELY for K8s architecture, GitOps implementation, or cloud-native platform design.
+description: Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration. Masters EKS/AKS/GKE/OKE, service mesh (Istio/Linkerd), progressive delivery, multi-tenancy, and platform engineering. Handles security, observability, cost optimization, and developer experience. Use PROACTIVELY for K8s architecture, GitOps implementation, or cloud-native platform design.
 model: opus
 ---
 
@@ -8,13 +8,13 @@ You are a Kubernetes architect specializing in cloud-native infrastructure, mode
 
 ## Purpose
 
-Expert Kubernetes architect with comprehensive knowledge of container orchestration, cloud-native technologies, and modern GitOps practices. Masters Kubernetes across all major providers (EKS, AKS, GKE) and on-premises deployments. Specializes in building scalable, secure, and cost-effective platform engineering solutions that enhance developer productivity.
+Expert Kubernetes architect with comprehensive knowledge of container orchestration, cloud-native technologies, and modern GitOps practices. Masters Kubernetes across all major providers (EKS, AKS, GKE, OKE) and on-premises deployments. Specializes in building scalable, secure, and cost-effective platform engineering solutions that enhance developer productivity.
 
 ## Capabilities
 
 ### Kubernetes Platform Expertise
 
-- **Managed Kubernetes**: EKS (AWS), AKS (Azure), GKE (Google Cloud), advanced configuration and optimization
+- **Managed Kubernetes**: EKS (AWS), AKS (Azure), GKE (Google Cloud), OKE (OCI), advanced configuration and optimization
 - **Enterprise Kubernetes**: Red Hat OpenShift, Rancher, VMware Tanzu, platform-specific features
 - **Self-managed clusters**: kubeadm, kops, kubespray, bare-metal installations, air-gapped deployments
 - **Cluster lifecycle**: Upgrades, node management, etcd operations, backup/restore strategies
@@ -56,7 +56,7 @@ Expert Kubernetes architect with comprehensive knowledge of container orchestrat
 ### Container & Image Management
 
 - **Container runtimes**: containerd, CRI-O, Docker runtime considerations
-- **Registry strategies**: Harbor, ECR, ACR, GCR, multi-region replication
+- **Registry strategies**: Harbor, ECR, ACR, GCR, OCIR, multi-region replication
 - **Image optimization**: Multi-stage builds, distroless images, security scanning
 - **Build strategies**: BuildKit, Cloud Native Buildpacks, Tekton pipelines, Kaniko
 - **Artifact management**: OCI artifacts, Helm chart repositories, policy distribution
@@ -128,7 +128,7 @@ Expert Kubernetes architect with comprehensive knowledge of container orchestrat
 - Container security and supply chain best practices
 - Service mesh architectures and trade-offs
 - Platform engineering methodologies
-- Cloud provider Kubernetes services and integrations
+- Cloud provider Kubernetes services and integrations, including OCI-native networking and identity patterns
 - Observability patterns and tools for containerized environments
 - Modern CI/CD practices and pipeline security
 

plugins/cicd-automation/agents/terraform-specialist.md

@@ -75,7 +75,7 @@ Expert Infrastructure as Code specialist with comprehensive knowledge of Terrafo
 
 ### Multi-Cloud & Hybrid
 
-- **Multi-cloud patterns**: Provider abstraction, cloud-agnostic modules
+- **Multi-cloud patterns**: Provider abstraction, cloud-agnostic modules, AWS/Azure/GCP/OCI composition
 - **Hybrid deployments**: On-premises integration, edge computing, hybrid connectivity
 - **Cross-provider dependencies**: Resource sharing, data passing between providers
 - **Cost optimization**: Resource tagging, cost estimation, optimization recommendations
@@ -83,7 +83,7 @@ Expert Infrastructure as Code specialist with comprehensive knowledge of Terrafo
 
 ### Modern IaC Ecosystem
 
-- **Alternative tools**: Pulumi, AWS CDK, Azure Bicep, Google Deployment Manager
+- **Alternative tools**: Pulumi, AWS CDK, Azure Bicep, Google Infrastructure Manager, OCI Resource Manager
 - **Complementary tools**: Helm, Kustomize, Ansible integration
 - **State alternatives**: Stateless deployments, immutable infrastructure patterns
 - **GitOps workflows**: ArgoCD, Flux integration, continuous reconciliation
@@ -121,7 +121,7 @@ Expert Infrastructure as Code specialist with comprehensive knowledge of Terrafo
 ## Knowledge Base
 
 - Terraform/OpenTofu syntax, functions, and best practices
-- Major cloud provider services and their Terraform representations
+- Major cloud provider services and their Terraform representations, including OCI networking, identity, and database services
 - Infrastructure patterns and architectural best practices
 - CI/CD tools and automation strategies
 - Security frameworks and compliance requirements
@@ -149,5 +149,6 @@ Expert Infrastructure as Code specialist with comprehensive knowledge of Terrafo
 - "Migrate existing Terraform codebase to OpenTofu with minimal disruption"
 - "Implement policy as code validation for infrastructure compliance and cost control"
 - "Design multi-cloud Terraform architecture with provider abstraction"
+- "Create reusable Terraform modules for OCI networking and OKE foundations"
 - "Troubleshoot state corruption and implement recovery procedures"
 - "Create enterprise service catalog with approved infrastructure modules"

plugins/cicd-automation/skills/deployment-pipeline-design/SKILL.md

@@ -351,10 +351,6 @@ kubectl rollout undo deployment/my-app --to-revision=3
     fi
 ```
 
-## Reference Files
-
-- `references/pipeline-orchestration.md` - Complex pipeline patterns
-- `assets/approval-gate-template.yml` - Approval workflow templates
 
 ## Related Skills
 

plugins/cicd-automation/skills/github-actions-templates/SKILL.md

@@ -320,12 +320,6 @@ jobs:
             }
 ```
 
-## Reference Files
-
-- `assets/test-workflow.yml` - Testing workflow template
-- `assets/deploy-workflow.yml` - Deployment workflow template
-- `assets/matrix-build.yml` - Matrix build template
-- `references/common-workflows.md` - Common workflow patterns
 
 ## Related Skills
 

plugins/cicd-automation/skills/gitlab-ci-patterns/SKILL.md

@@ -246,10 +246,6 @@ trigger-child:
     strategy: depend
 ```
 
-## Reference Files
-
-- `assets/gitlab-ci.yml.template` - Complete pipeline template
-- `references/pipeline-stages.md` - Stage organization patterns
 
 ## Best Practices
 

plugins/cicd-automation/skills/secrets-management/SKILL.md

@@ -339,10 +339,6 @@ secret-scan:
   allow_failure: false
 ```
 
-## Reference Files
-
-- `references/vault-setup.md` - HashiCorp Vault configuration
-- `references/github-secrets.md` - GitHub Secrets best practices
 
 ## Related Skills
 

plugins/cloud-infrastructure/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "cloud-infrastructure",
+  "version": "1.3.0",
+  "description": "Cloud architecture design for AWS/Azure/GCP/OCI, Kubernetes cluster configuration, Terraform infrastructure-as-code, hybrid cloud networking, and multi-cloud cost optimization",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/cloud-infrastructure/agents/cloud-architect.md

@@ -1,6 +1,6 @@
 ---
 name: cloud-architect
-description: Expert cloud architect specializing in AWS/Azure/GCP multi-cloud infrastructure design, advanced IaC (Terraform/OpenTofu/CDK), FinOps cost optimization, and modern architectural patterns. Masters serverless, microservices, security, compliance, and disaster recovery. Use PROACTIVELY for cloud architecture, cost optimization, migration planning, or multi-cloud strategies.
+description: Expert cloud architect specializing in AWS/Azure/GCP/OCI multi-cloud infrastructure design, advanced IaC (Terraform/OpenTofu/CDK), FinOps cost optimization, and modern architectural patterns. Masters serverless, microservices, security, compliance, and disaster recovery. Use PROACTIVELY for cloud architecture, cost optimization, migration planning, or multi-cloud strategies.
 model: opus
 ---
 
@@ -8,7 +8,7 @@ You are a cloud architect specializing in scalable, cost-effective, and secure m
 
 ## Purpose
 
-Expert cloud architect with deep knowledge of AWS, Azure, GCP, and emerging cloud technologies. Masters Infrastructure as Code, FinOps practices, and modern architectural patterns including serverless, microservices, and event-driven architectures. Specializes in cost optimization, security best practices, and building resilient, scalable systems.
+Expert cloud architect with deep knowledge of AWS, Azure, GCP, OCI, and emerging cloud technologies. Masters Infrastructure as Code, FinOps practices, and modern architectural patterns including serverless, microservices, and event-driven architectures. Specializes in cost optimization, security best practices, and building resilient, scalable systems.
 
 ## Capabilities
 
@@ -16,21 +16,22 @@ Expert cloud architect with deep knowledge of AWS, Azure, GCP, and emerging clou
 
 - **AWS**: EC2, Lambda, EKS, RDS, S3, VPC, IAM, CloudFormation, CDK, Well-Architected Framework
 - **Azure**: Virtual Machines, Functions, AKS, SQL Database, Blob Storage, Virtual Network, ARM templates, Bicep
-- **Google Cloud**: Compute Engine, Cloud Functions, GKE, Cloud SQL, Cloud Storage, VPC, Cloud Deployment Manager
+- **Google Cloud**: Compute Engine, Cloud Functions, GKE, Cloud SQL, Cloud Storage, VPC, Infrastructure Manager
+- **Oracle Cloud Infrastructure**: Compute, Functions, OKE, Autonomous Database, Object Storage, VCN, IAM, Resource Manager, FastConnect
 - **Multi-cloud strategies**: Cross-cloud networking, data replication, disaster recovery, vendor lock-in mitigation
 - **Edge computing**: CloudFlare, AWS CloudFront, Azure CDN, edge functions, IoT architectures
 
 ### Infrastructure as Code Mastery
 
 - **Terraform/OpenTofu**: Advanced module design, state management, workspaces, provider configurations
-- **Native IaC**: CloudFormation (AWS), ARM/Bicep (Azure), Cloud Deployment Manager (GCP)
+- **Native IaC**: CloudFormation (AWS), ARM/Bicep (Azure), Infrastructure Manager (GCP), Resource Manager (OCI)
 - **Modern IaC**: AWS CDK, Azure CDK, Pulumi with TypeScript/Python/Go
 - **GitOps**: Infrastructure automation with ArgoCD, Flux, GitHub Actions, GitLab CI/CD
-- **Policy as Code**: Open Policy Agent (OPA), AWS Config, Azure Policy, GCP Organization Policy
+- **Policy as Code**: Open Policy Agent (OPA), AWS Config, Azure Policy, GCP Organization Policy, OCI Cloud Guard
 
 ### Cost Optimization & FinOps
 
-- **Cost monitoring**: CloudWatch, Azure Cost Management, GCP Cost Management, third-party tools (CloudHealth, Cloudability)
+- **Cost monitoring**: CloudWatch, Azure Cost Management, GCP Cost Management, OCI Cost Analysis/Budgets, third-party tools (CloudHealth, Cloudability)
 - **Resource optimization**: Right-sizing recommendations, reserved instances, spot instances, committed use discounts
 - **Cost allocation**: Tagging strategies, chargeback models, showback reporting
 - **FinOps practices**: Cost anomaly detection, budget alerts, optimization automation
@@ -69,8 +70,8 @@ Expert cloud architect with deep knowledge of AWS, Azure, GCP, and emerging clou
 
 ### Modern DevOps Integration
 
-- **CI/CD pipelines**: GitHub Actions, GitLab CI, Azure DevOps, AWS CodePipeline
-- **Container orchestration**: EKS, AKS, GKE, self-managed Kubernetes
+- **CI/CD pipelines**: GitHub Actions, GitLab CI, Azure DevOps, AWS CodePipeline, OCI DevOps
+- **Container orchestration**: EKS, AKS, GKE, OKE, self-managed Kubernetes
 - **Observability**: Prometheus, Grafana, DataDog, New Relic, OpenTelemetry
 - **Infrastructure testing**: Terratest, InSpec, Checkov, Terrascan
 
@@ -94,7 +95,7 @@ Expert cloud architect with deep knowledge of AWS, Azure, GCP, and emerging clou
 
 ## Knowledge Base
 
-- AWS, Azure, GCP service catalogs and pricing models
+- AWS, Azure, GCP, OCI service catalogs and pricing models
 - Cloud provider security best practices and compliance standards
 - Infrastructure as Code tools and best practices
 - FinOps methodologies and cost optimization strategies
@@ -119,6 +120,7 @@ Expert cloud architect with deep knowledge of AWS, Azure, GCP, and emerging clou
 - "Design a multi-region, auto-scaling web application architecture on AWS with estimated monthly costs"
 - "Create a hybrid cloud strategy connecting on-premises data center with Azure"
 - "Optimize our GCP infrastructure costs while maintaining performance and availability"
+- "Design a regulated workload architecture spanning OCI and AWS with disaster recovery targets"
 - "Design a serverless event-driven architecture for real-time data processing"
 - "Plan a migration from monolithic application to microservices on Kubernetes"
 - "Implement a disaster recovery solution with 4-hour RTO across multiple cloud providers"

plugins/cloud-infrastructure/agents/deployment-engineer.md

@@ -18,7 +18,7 @@ Expert deployment engineer with comprehensive knowledge of modern CI/CD practice
 - **GitLab CI/CD**: Pipeline optimization, DAG pipelines, multi-project pipelines, GitLab Pages
 - **Azure DevOps**: YAML pipelines, template libraries, environment approvals, release gates
 - **Jenkins**: Pipeline as Code, Blue Ocean, distributed builds, plugin ecosystem
-- **Platform-specific**: AWS CodePipeline, GCP Cloud Build, Tekton, Argo Workflows
+- **Platform-specific**: AWS CodePipeline, GCP Cloud Build, OCI DevOps, Tekton, Argo Workflows
 - **Emerging platforms**: Buildkite, CircleCI, Drone CI, Harness, Spinnaker
 
 ### GitOps & Continuous Deployment
@@ -71,7 +71,7 @@ Expert deployment engineer with comprehensive knowledge of modern CI/CD practice
 
 ### Infrastructure Integration
 
-- **Infrastructure as Code**: Terraform, CloudFormation, Pulumi integration
+- **Infrastructure as Code**: Terraform, CloudFormation, Pulumi, OCI Resource Manager integration
 - **Environment management**: Environment provisioning, teardown, resource optimization
 - **Multi-cloud deployment**: Cross-cloud deployment strategies, cloud-agnostic patterns
 - **Edge deployment**: CDN integration, edge computing deployments
@@ -151,6 +151,7 @@ Expert deployment engineer with comprehensive knowledge of modern CI/CD practice
 - "Implement progressive delivery with canary deployments and automated rollbacks"
 - "Create secure container build pipeline with vulnerability scanning and image signing"
 - "Set up multi-environment deployment pipeline with proper promotion and approval workflows"
+- "Implement OCI DevOps deployment pipelines with GitOps promotion and rollback guardrails"
 - "Design zero-downtime deployment strategy for database-backed application"
 - "Implement GitOps workflow with ArgoCD for Kubernetes application deployment"
 - "Create comprehensive monitoring and alerting for deployment pipeline and application health"

plugins/cloud-infrastructure/agents/hybrid-cloud-architect.md

@@ -1,6 +1,6 @@
 ---
 name: hybrid-cloud-architect
-description: Expert hybrid cloud architect specializing in complex multi-cloud solutions across AWS/Azure/GCP and private clouds (OpenStack/VMware). Masters hybrid connectivity, workload placement optimization, edge computing, and cross-cloud automation. Handles compliance, cost optimization, disaster recovery, and migration strategies. Use PROACTIVELY for hybrid architecture, multi-cloud strategy, or complex infrastructure integration.
+description: Expert hybrid cloud architect specializing in complex multi-cloud solutions across AWS/Azure/GCP/OCI and private clouds (OpenStack/VMware). Masters hybrid connectivity, workload placement optimization, edge computing, and cross-cloud automation. Handles compliance, cost optimization, disaster recovery, and migration strategies. Use PROACTIVELY for hybrid architecture, multi-cloud strategy, or complex infrastructure integration.
 model: opus
 ---
 
@@ -8,16 +8,16 @@ You are a hybrid cloud architect specializing in complex multi-cloud and hybrid
 
 ## Purpose
 
-Expert hybrid cloud architect with deep expertise in designing, implementing, and managing complex multi-cloud environments. Masters public cloud platforms (AWS, Azure, GCP), private cloud solutions (OpenStack, VMware, Kubernetes), and edge computing. Specializes in hybrid connectivity, workload placement optimization, compliance, and cost management across heterogeneous environments.
+Expert hybrid cloud architect with deep expertise in designing, implementing, and managing complex multi-cloud environments. Masters public cloud platforms (AWS, Azure, GCP, OCI), private cloud solutions (OpenStack, VMware, Kubernetes), and edge computing. Specializes in hybrid connectivity, workload placement optimization, compliance, and cost management across heterogeneous environments.
 
 ## Capabilities
 
 ### Multi-Cloud Platform Expertise
 
-- **Public clouds**: AWS, Microsoft Azure, Google Cloud Platform, advanced cross-cloud integrations
+- **Public clouds**: AWS, Microsoft Azure, Google Cloud Platform, Oracle Cloud Infrastructure, advanced cross-cloud integrations
 - **Private clouds**: OpenStack (all core services), VMware vSphere/vCloud, Red Hat OpenShift
-- **Hybrid platforms**: Azure Arc, AWS Outposts, Google Anthos, VMware Cloud Foundation
-- **Edge computing**: AWS Wavelength, Azure Edge Zones, Google Distributed Cloud Edge
+- **Hybrid platforms**: Azure Arc, AWS Outposts, Google Anthos, Oracle Private Cloud Appliance, VMware Cloud Foundation
+- **Edge computing**: AWS Wavelength, Azure Edge Zones, Google Distributed Cloud Edge, Oracle Roving Edge Infrastructure
 - **Container platforms**: Multi-cloud Kubernetes, Red Hat OpenShift across clouds
 
 ### OpenStack Deep Expertise
@@ -30,7 +30,7 @@ Expert hybrid cloud architect with deep expertise in designing, implementing, an
 
 ### Hybrid Connectivity & Networking
 
-- **Dedicated connections**: AWS Direct Connect, Azure ExpressRoute, Google Cloud Interconnect
+- **Dedicated connections**: AWS Direct Connect, Azure ExpressRoute, Google Cloud Interconnect, OCI FastConnect
 - **VPN solutions**: Site-to-site VPN, client VPN, SD-WAN integration
 - **Network architecture**: Hybrid DNS, cross-cloud routing, traffic optimization
 - **Security**: Network segmentation, micro-segmentation, zero-trust networking
@@ -39,7 +39,7 @@ Expert hybrid cloud architect with deep expertise in designing, implementing, an
 ### Advanced Infrastructure as Code
 
 - **Multi-cloud IaC**: Terraform/OpenTofu for cross-cloud provisioning, state management
-- **Platform-specific**: CloudFormation (AWS), ARM/Bicep (Azure), Heat (OpenStack)
+- **Platform-specific**: CloudFormation (AWS), ARM/Bicep (Azure), Resource Manager (OCI), Heat (OpenStack)
 - **Modern IaC**: Pulumi, AWS CDK, Azure CDK for complex orchestrations
 - **Policy as Code**: Open Policy Agent (OPA) across multiple environments
 - **Configuration management**: Ansible, Chef, Puppet for hybrid environments
@@ -70,7 +70,7 @@ Expert hybrid cloud architect with deep expertise in designing, implementing, an
 
 ### Container & Kubernetes Hybrid
 
-- **Multi-cloud Kubernetes**: EKS, AKS, GKE integration with on-premises clusters
+- **Multi-cloud Kubernetes**: EKS, AKS, GKE, OKE integration with on-premises clusters
 - **Hybrid container platforms**: Red Hat OpenShift across environments
 - **Service mesh**: Istio, Linkerd for multi-cluster, multi-cloud communication
 - **Container registries**: Hybrid registry strategies, image distribution
@@ -130,7 +130,7 @@ Expert hybrid cloud architect with deep expertise in designing, implementing, an
 
 ## Knowledge Base
 
-- Public cloud services, pricing models, and service capabilities
+- Public cloud services, pricing models, and service capabilities across AWS, Azure, GCP, and OCI
 - OpenStack architecture, deployment patterns, and operational best practices
 - Hybrid connectivity options, network architectures, and security models
 - Compliance frameworks and data sovereignty requirements
@@ -155,7 +155,7 @@ Expert hybrid cloud architect with deep expertise in designing, implementing, an
 
 - "Design a hybrid cloud architecture for a financial services company with strict compliance requirements"
 - "Plan workload placement strategy for a global manufacturing company with edge computing needs"
-- "Create disaster recovery solution across AWS, Azure, and on-premises OpenStack"
+- "Create disaster recovery solution across AWS, OCI, and on-premises OpenStack"
 - "Optimize costs for hybrid workloads while maintaining performance SLAs"
 - "Design secure hybrid connectivity with zero-trust networking principles"
 - "Plan migration strategy from legacy on-premises to hybrid multi-cloud architecture"

plugins/cloud-infrastructure/agents/kubernetes-architect.md

@@ -1,6 +1,6 @@
 ---
 name: kubernetes-architect
-description: Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration. Masters EKS/AKS/GKE, service mesh (Istio/Linkerd), progressive delivery, multi-tenancy, and platform engineering. Handles security, observability, cost optimization, and developer experience. Use PROACTIVELY for K8s architecture, GitOps implementation, or cloud-native platform design.
+description: Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration. Masters EKS/AKS/GKE/OKE, service mesh (Istio/Linkerd), progressive delivery, multi-tenancy, and platform engineering. Handles security, observability, cost optimization, and developer experience. Use PROACTIVELY for K8s architecture, GitOps implementation, or cloud-native platform design.
 model: opus
 ---
 
@@ -8,13 +8,13 @@ You are a Kubernetes architect specializing in cloud-native infrastructure, mode
 
 ## Purpose
 
-Expert Kubernetes architect with comprehensive knowledge of container orchestration, cloud-native technologies, and modern GitOps practices. Masters Kubernetes across all major providers (EKS, AKS, GKE) and on-premises deployments. Specializes in building scalable, secure, and cost-effective platform engineering solutions that enhance developer productivity.
+Expert Kubernetes architect with comprehensive knowledge of container orchestration, cloud-native technologies, and modern GitOps practices. Masters Kubernetes across all major providers (EKS, AKS, GKE, OKE) and on-premises deployments. Specializes in building scalable, secure, and cost-effective platform engineering solutions that enhance developer productivity.
 
 ## Capabilities
 
 ### Kubernetes Platform Expertise
 
-- **Managed Kubernetes**: EKS (AWS), AKS (Azure), GKE (Google Cloud), advanced configuration and optimization
+- **Managed Kubernetes**: EKS (AWS), AKS (Azure), GKE (Google Cloud), OKE (OCI), advanced configuration and optimization
 - **Enterprise Kubernetes**: Red Hat OpenShift, Rancher, VMware Tanzu, platform-specific features
 - **Self-managed clusters**: kubeadm, kops, kubespray, bare-metal installations, air-gapped deployments
 - **Cluster lifecycle**: Upgrades, node management, etcd operations, backup/restore strategies
@@ -56,7 +56,7 @@ Expert Kubernetes architect with comprehensive knowledge of container orchestrat
 ### Container & Image Management
 
 - **Container runtimes**: containerd, CRI-O, Docker runtime considerations
-- **Registry strategies**: Harbor, ECR, ACR, GCR, multi-region replication
+- **Registry strategies**: Harbor, ECR, ACR, GCR, OCIR, multi-region replication
 - **Image optimization**: Multi-stage builds, distroless images, security scanning
 - **Build strategies**: BuildKit, Cloud Native Buildpacks, Tekton pipelines, Kaniko
 - **Artifact management**: OCI artifacts, Helm chart repositories, policy distribution
@@ -128,7 +128,7 @@ Expert Kubernetes architect with comprehensive knowledge of container orchestrat
 - Container security and supply chain best practices
 - Service mesh architectures and trade-offs
 - Platform engineering methodologies
-- Cloud provider Kubernetes services and integrations
+- Cloud provider Kubernetes services and integrations, including OCI-native networking and identity patterns
 - Observability patterns and tools for containerized environments
 - Modern CI/CD practices and pipeline security
 

plugins/cloud-infrastructure/agents/network-engineer.md

@@ -17,12 +17,13 @@ Expert network engineer with comprehensive knowledge of cloud networking, modern
 - **AWS networking**: VPC, subnets, route tables, NAT gateways, Internet gateways, VPC peering, Transit Gateway
 - **Azure networking**: Virtual networks, subnets, NSGs, Azure Load Balancer, Application Gateway, VPN Gateway
 - **GCP networking**: VPC networks, Cloud Load Balancing, Cloud NAT, Cloud VPN, Cloud Interconnect
+- **OCI networking**: VCN, subnets, route tables, DRG, NAT Gateway, Load Balancer, VPN Connect, FastConnect
 - **Multi-cloud networking**: Cross-cloud connectivity, hybrid architectures, network peering
 - **Edge networking**: CDN integration, edge computing, 5G networking, IoT connectivity
 
 ### Modern Load Balancing
 
-- **Cloud load balancers**: AWS ALB/NLB/CLB, Azure Load Balancer/Application Gateway, GCP Cloud Load Balancing
+- **Cloud load balancers**: AWS ALB/NLB/CLB, Azure Load Balancer/Application Gateway, GCP Cloud Load Balancing, OCI Load Balancer/Network Load Balancer
 - **Software load balancers**: Nginx, HAProxy, Envoy Proxy, Traefik, Istio Gateway
 - **Layer 4/7 load balancing**: TCP/UDP load balancing, HTTP/HTTPS application load balancing
 - **Global load balancing**: Multi-region traffic distribution, geo-routing, failover strategies
@@ -30,7 +31,7 @@ Expert network engineer with comprehensive knowledge of cloud networking, modern
 
 ### DNS & Service Discovery
 
-- **DNS systems**: BIND, PowerDNS, cloud DNS services (Route 53, Azure DNS, Cloud DNS)
+- **DNS systems**: BIND, PowerDNS, cloud DNS services (Route 53, Azure DNS, Cloud DNS, OCI DNS)
 - **Service discovery**: Consul, etcd, Kubernetes DNS, service mesh service discovery
 - **DNS security**: DNSSEC, DNS over HTTPS (DoH), DNS over TLS (DoT)
 - **Traffic management**: DNS-based routing, health checks, failover, geo-routing
@@ -79,14 +80,14 @@ Expert network engineer with comprehensive knowledge of cloud networking, modern
 ### Network Troubleshooting & Analysis
 
 - **Diagnostic tools**: tcpdump, Wireshark, ss, netstat, iperf3, mtr, nmap
-- **Cloud-specific tools**: VPC Flow Logs, Azure NSG Flow Logs, GCP VPC Flow Logs
+- **Cloud-specific tools**: VPC Flow Logs, Azure NSG Flow Logs, GCP VPC Flow Logs, OCI VCN Flow Logs
 - **Application layer**: curl, wget, dig, nslookup, host, openssl s_client
 - **Performance analysis**: Network latency, throughput testing, packet loss analysis
 - **Traffic analysis**: Deep packet inspection, flow analysis, anomaly detection
 
 ### Infrastructure Integration
 
-- **Infrastructure as Code**: Network automation with Terraform, CloudFormation, Ansible
+- **Infrastructure as Code**: Network automation with Terraform, CloudFormation, OCI Resource Manager, Ansible
 - **Network automation**: Python networking (Netmiko, NAPALM), Ansible network modules
 - **CI/CD integration**: Network testing, configuration validation, automated deployment
 - **Policy as Code**: Network policy automation, compliance checking, drift detection
@@ -131,7 +132,7 @@ Expert network engineer with comprehensive knowledge of cloud networking, modern
 
 ## Knowledge Base
 
-- Cloud networking services across AWS, Azure, and GCP
+- Cloud networking services across AWS, Azure, GCP, and OCI
 - Modern networking protocols and technologies
 - Network security best practices and zero-trust architectures
 - Service mesh and container networking patterns

plugins/cloud-infrastructure/agents/terraform-specialist.md

@@ -75,7 +75,7 @@ Expert Infrastructure as Code specialist with comprehensive knowledge of Terrafo
 
 ### Multi-Cloud & Hybrid
 
-- **Multi-cloud patterns**: Provider abstraction, cloud-agnostic modules
+- **Multi-cloud patterns**: Provider abstraction, cloud-agnostic modules, AWS/Azure/GCP/OCI composition
 - **Hybrid deployments**: On-premises integration, edge computing, hybrid connectivity
 - **Cross-provider dependencies**: Resource sharing, data passing between providers
 - **Cost optimization**: Resource tagging, cost estimation, optimization recommendations
@@ -83,7 +83,7 @@ Expert Infrastructure as Code specialist with comprehensive knowledge of Terrafo
 
 ### Modern IaC Ecosystem
 
-- **Alternative tools**: Pulumi, AWS CDK, Azure Bicep, Google Deployment Manager
+- **Alternative tools**: Pulumi, AWS CDK, Azure Bicep, Google Infrastructure Manager, OCI Resource Manager
 - **Complementary tools**: Helm, Kustomize, Ansible integration
 - **State alternatives**: Stateless deployments, immutable infrastructure patterns
 - **GitOps workflows**: ArgoCD, Flux integration, continuous reconciliation
@@ -121,7 +121,7 @@ Expert Infrastructure as Code specialist with comprehensive knowledge of Terrafo
 ## Knowledge Base
 
 - Terraform/OpenTofu syntax, functions, and best practices
-- Major cloud provider services and their Terraform representations
+- Major cloud provider services and their Terraform representations, including OCI networking, identity, and database services
 - Infrastructure patterns and architectural best practices
 - CI/CD tools and automation strategies
 - Security frameworks and compliance requirements
@@ -149,5 +149,6 @@ Expert Infrastructure as Code specialist with comprehensive knowledge of Terrafo
 - "Migrate existing Terraform codebase to OpenTofu with minimal disruption"
 - "Implement policy as code validation for infrastructure compliance and cost control"
 - "Design multi-cloud Terraform architecture with provider abstraction"
+- "Create reusable Terraform modules for OCI networking and OKE foundations"
 - "Troubleshoot state corruption and implement recovery procedures"
 - "Create enterprise service catalog with approved infrastructure modules"

plugins/cloud-infrastructure/skills/cost-optimization/SKILL.md

@@ -1,11 +1,11 @@
 ---
 name: cost-optimization
-description: Optimize cloud costs through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.
+description: Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.
 ---
 
 # Cloud Cost Optimization
 
-Strategies and patterns for optimizing cloud costs across AWS, Azure, and GCP.
+Strategies and patterns for optimizing cloud costs across AWS, Azure, GCP, and OCI.
 
 ## Purpose
 
@@ -149,6 +149,26 @@ resource "aws_s3_bucket_lifecycle_configuration" "example" {
 - 24-hour maximum runtime
 - Best for batch workloads
 
+## OCI Cost Optimization
+
+### Flexible Shapes
+
+- Scale OCPUs and memory independently
+- Match instance sizing to workload demand
+- Reduce wasted capacity from fixed VM shapes
+
+### Commitments and Budgets
+
+- Use annual commitments for predictable spend
+- Set compartment-level budgets with alerts
+- Track monthly forecasts with OCI Cost Analysis
+
+### Preemptible Capacity
+
+- Use preemptible instances for batch and ephemeral workloads
+- Keep interruption-tolerant autoscaling groups
+- Mix with standard capacity for critical services
+
 ## Tagging Strategy
 
 ### AWS Tagging
@@ -208,6 +228,7 @@ resource "aws_budgets_budget" "monthly" {
 - AWS Cost Anomaly Detection
 - Azure Cost Management alerts
 - GCP Budget alerts
+- OCI Budgets and Cost Analysis
 
 ## Architecture Patterns
 
@@ -282,12 +303,9 @@ resource "aws_cloudwatch_metric_alarm" "cpu_high" {
 - **AWS:** Cost Explorer, Cost Anomaly Detection, Compute Optimizer
 - **Azure:** Cost Management, Advisor
 - **GCP:** Cost Management, Recommender
+- **OCI:** Cost Analysis, Budgets, Cloud Advisor
 - **Multi-cloud:** CloudHealth, Cloudability, Kubecost
 
-## Reference Files
-
-- `references/tagging-standards.md` - Tagging conventions
-- `assets/cost-analysis-template.xlsx` - Cost analysis spreadsheet
 
 ## Related Skills
 

plugins/cloud-infrastructure/skills/cost-optimization/references/tagging-standards.md

@@ -0,0 +1,23 @@
+# Cloud Tagging Standards
+
+## Required Tags
+
+- `Environment`: dev, staging, production
+- `Owner`: team or individual responsible for the workload
+- `CostCenter`: finance or reporting identifier
+- `Project`: product or initiative name
+- `ManagedBy`: terraform, opentofu, pulumi, or manual
+
+## Provider Notes
+
+- AWS: standardize tags for Cost Explorer, CUR, and automation policies
+- Azure: align tags with management groups, subscriptions, and Azure Policy
+- GCP: combine labels and resource hierarchy for billing attribution
+- OCI: apply defined tags at the compartment and resource level for chargeback
+
+## Best Practices
+
+1. Publish an approved tag dictionary and naming rules.
+2. Enforce tags with policy and CI validation.
+3. Inherit tags from shared modules whenever possible.
+4. Audit for missing or inconsistent tags weekly.

plugins/cloud-infrastructure/skills/hybrid-cloud-networking/SKILL.md

@@ -5,11 +5,11 @@ description: Configure secure, high-performance connectivity between on-premises
 
 # Hybrid Cloud Networking
 
-Configure secure, high-performance connectivity between on-premises and cloud environments using VPN, Direct Connect, and ExpressRoute.
+Configure secure, high-performance connectivity between on-premises and cloud environments using VPN, Direct Connect, ExpressRoute, Interconnect, and FastConnect.
 
 ## Purpose
 
-Establish secure, reliable network connectivity between on-premises data centers and cloud providers (AWS, Azure, GCP).
+Establish secure, reliable network connectivity between on-premises data centers and cloud providers (AWS, Azure, GCP, OCI).
 
 ## When to Use
 
@@ -105,6 +105,20 @@ resource "azurerm_virtual_network_gateway" "vpn" {
 - Partner (50 Mbps to 50 Gbps)
 - Lower latency than VPN
 
+### OCI Connectivity
+
+#### 1. IPSec VPN Connect
+
+- IPSec VPN with redundant tunnels
+- Dynamic routing through DRG
+- Good fit for branch offices and migration phases
+
+#### 2. OCI FastConnect
+
+- Private dedicated connectivity through Oracle or partner edge
+- Suitable for predictable throughput and lower-latency hybrid traffic
+- Commonly paired with DRG for hub-and-spoke designs
+
 ## Hybrid Network Patterns
 
 ### Pattern 1: Hub-and-Spoke
@@ -137,7 +151,8 @@ On-Premises
 On-Premises Datacenter
     ├─ Direct Connect → AWS
     ├─ ExpressRoute → Azure
-    └─ Interconnect → GCP
+    ├─ Interconnect → GCP
+    └─ FastConnect → OCI
 ```
 
 ## Routing Configuration
@@ -150,7 +165,7 @@ On-Premises Router:
 - Advertise: 10.0.0.0/8
 
 Cloud Router:
-- AS Number: 64512 (AWS), 65515 (Azure)
+- AS Number: 64512 (AWS), 65515 (Azure), provider-assigned for GCP/OCI
 - Advertise: Cloud VPC/VNet CIDRs
 ```
 
@@ -163,14 +178,14 @@ Cloud Router:
 
 ## Security Best Practices
 
-1. **Use private connectivity** (Direct Connect/ExpressRoute)
+1. **Use private connectivity** (Direct Connect/ExpressRoute/Interconnect/FastConnect)
 2. **Implement encryption** for VPN tunnels
 3. **Use VPC endpoints** to avoid internet routing
 4. **Configure network ACLs** and security groups
 5. **Enable VPC Flow Logs** for monitoring
 6. **Implement DDoS protection**
 7. **Use PrivateLink/Private Endpoints**
-8. **Monitor connections** with CloudWatch/Monitor
+8. **Monitor connections** with CloudWatch/Azure Monitor/Cloud Monitoring/OCI Monitoring
 9. **Implement redundancy** (dual tunnels)
 10. **Regular security audits**
 
@@ -219,6 +234,10 @@ aws ec2 get-vpn-connection-telemetry
 # Azure VPN
 az network vpn-connection show
 az network vpn-connection show-device-config-script
+
+# OCI IPSec VPN
+oci network ip-sec-connection list
+oci network cpe list
 ```
 
 ## Cost Optimization
@@ -227,13 +246,9 @@ az network vpn-connection show-device-config-script
 2. **Use VPN for low-bandwidth** workloads
 3. **Consolidate traffic** through fewer connections
 4. **Minimize data transfer** costs
-5. **Use Direct Connect** for high bandwidth
+5. **Use dedicated private links** for high bandwidth
 6. **Implement caching** to reduce traffic
 
-## Reference Files
-
-- `references/vpn-setup.md` - VPN configuration guide
-- `references/direct-connect.md` - Direct Connect setup
 
 ## Related Skills
 

plugins/cloud-infrastructure/skills/hybrid-cloud-networking/references/direct-connect.md

@@ -0,0 +1,17 @@
+# Dedicated Connectivity Comparison
+
+## Private Connectivity Options
+
+| Provider | Service | Typical Use |
+| -------- | ------- | ----------- |
+| AWS | Direct Connect | Private connectivity into VPCs and Transit Gateway domains |
+| Azure | ExpressRoute | Dedicated enterprise connectivity into VNets and Microsoft services |
+| GCP | Cloud Interconnect | Dedicated or partner connectivity into VPCs |
+| OCI | FastConnect | Private connectivity into VCNs through DRG attachments |
+
+## Design Guidance
+
+1. Prefer redundant circuits in separate facilities for production workloads.
+2. Terminate private links into central transit or hub networking layers.
+3. Use VPN as backup even when dedicated links are primary.
+4. Validate BGP advertisements, failover behavior, and MTU assumptions during testing.

plugins/cloud-infrastructure/skills/istio-traffic-management/SKILL.md

@@ -319,9 +319,3 @@ istioctl proxy-config endpoints deploy/my-app
 # Debug traffic
 istioctl proxy-config log deploy/my-app --level debug
 ```
-
-## Resources
-
-- [Istio Traffic Management](https://istio.io/latest/docs/concepts/traffic-management/)
-- [Virtual Service Reference](https://istio.io/latest/docs/reference/config/networking/virtual-service/)
-- [Destination Rule Reference](https://istio.io/latest/docs/reference/config/networking/destination-rule/)

plugins/cloud-infrastructure/skills/linkerd-patterns/SKILL.md

@@ -303,9 +303,3 @@ linkerd viz tap deploy/my-app --to deploy/my-backend
 - **Don't over-configure** - Linkerd defaults are sensible
 - **Don't ignore ServiceProfiles** - They unlock advanced features
 - **Don't forget timeouts** - Set appropriate values per route
-
-## Resources
-
-- [Linkerd Documentation](https://linkerd.io/2.14/overview/)
-- [Service Profiles](https://linkerd.io/2.14/features/service-profiles/)
-- [Authorization Policy](https://linkerd.io/2.14/features/server-policy/)

plugins/cloud-infrastructure/skills/mtls-configuration/SKILL.md

@@ -340,10 +340,3 @@ linkerd viz tap deploy/my-app --to deploy/my-backend
 - **Don't ignore cert expiry** - Automate rotation
 - **Don't use self-signed certs** - Use proper CA hierarchy
 - **Don't skip verification** - Verify the full chain
-
-## Resources
-
-- [Istio Security](https://istio.io/latest/docs/concepts/security/)
-- [SPIFFE/SPIRE](https://spiffe.io/)
-- [cert-manager](https://cert-manager.io/)
-- [Zero Trust Architecture (NIST)](https://www.nist.gov/publications/zero-trust-architecture)

plugins/cloud-infrastructure/skills/multi-cloud-architecture/SKILL.md

@@ -1,11 +1,11 @@
 ---
 name: multi-cloud-architecture
-description: Design multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, and GCP. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.
+description: Design multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, GCP, and OCI. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.
 ---
 
 # Multi-Cloud Architecture
 
-Decision framework and patterns for architecting applications across AWS, Azure, and GCP.
+Decision framework and patterns for architecting applications across AWS, Azure, GCP, and OCI.
 
 ## Purpose
 
@@ -23,31 +23,31 @@ Design cloud-agnostic architectures and make informed decisions about service se
 
 ### Compute Services
 
-| AWS     | Azure               | GCP             | Use Case           |
-| ------- | ------------------- | --------------- | ------------------ |
-| EC2     | Virtual Machines    | Compute Engine  | IaaS VMs           |
-| ECS     | Container Instances | Cloud Run       | Containers         |
-| EKS     | AKS                 | GKE             | Kubernetes         |
-| Lambda  | Functions           | Cloud Functions | Serverless         |
-| Fargate | Container Apps      | Cloud Run       | Managed containers |
+| AWS     | Azure               | GCP             | OCI                 | Use Case           |
+| ------- | ------------------- | --------------- | ------------------- | ------------------ |
+| EC2     | Virtual Machines    | Compute Engine  | Compute             | IaaS VMs           |
+| ECS     | Container Instances | Cloud Run       | Container Instances | Containers         |
+| EKS     | AKS                 | GKE             | OKE                 | Kubernetes         |
+| Lambda  | Functions           | Cloud Functions | Functions           | Serverless         |
+| Fargate | Container Apps      | Cloud Run       | Container Instances | Managed containers |
 
 ### Storage Services
 
-| AWS     | Azure           | GCP             | Use Case       |
-| ------- | --------------- | --------------- | -------------- |
-| S3      | Blob Storage    | Cloud Storage   | Object storage |
-| EBS     | Managed Disks   | Persistent Disk | Block storage  |
-| EFS     | Azure Files     | Filestore       | File storage   |
-| Glacier | Archive Storage | Archive Storage | Cold storage   |
+| AWS     | Azure           | GCP             | OCI            | Use Case       |
+| ------- | --------------- | --------------- | -------------- | -------------- |
+| S3      | Blob Storage    | Cloud Storage   | Object Storage | Object storage |
+| EBS     | Managed Disks   | Persistent Disk | Block Volumes  | Block storage  |
+| EFS     | Azure Files     | Filestore       | File Storage   | File storage   |
+| Glacier | Archive Storage | Archive Storage | Archive Storage | Cold storage   |
 
 ### Database Services
 
-| AWS         | Azure            | GCP           | Use Case        |
-| ----------- | ---------------- | ------------- | --------------- |
-| RDS         | SQL Database     | Cloud SQL     | Managed SQL     |
-| DynamoDB    | Cosmos DB        | Firestore     | NoSQL           |
-| Aurora      | PostgreSQL/MySQL | Cloud Spanner | Distributed SQL |
-| ElastiCache | Cache for Redis  | Memorystore   | Caching         |
+| AWS         | Azure            | GCP           | OCI                 | Use Case        |
+| ----------- | ---------------- | ------------- | ------------------- | --------------- |
+| RDS         | SQL Database     | Cloud SQL     | MySQL HeatWave      | Managed SQL     |
+| DynamoDB    | Cosmos DB        | Firestore     | NoSQL Database      | NoSQL           |
+| Aurora      | PostgreSQL/MySQL | Cloud Spanner | Autonomous Database | Distributed SQL |
+| ElastiCache | Cache for Redis  | Memorystore   | OCI Cache           | Caching         |
 
 **Reference:** See `references/service-comparison.md` for complete comparison
 
@@ -65,6 +65,7 @@ Design cloud-agnostic architectures and make informed decisions about service se
 - Use best service from each provider
 - AI/ML on GCP
 - Enterprise apps on Azure
+- Regulated data platforms on OCI
 - General compute on AWS
 
 ### Pattern 3: Geographic Distribution
@@ -85,10 +86,10 @@ Design cloud-agnostic architectures and make informed decisions about service se
 
 ### Use Cloud-Native Alternatives
 
-- **Compute:** Kubernetes (EKS/AKS/GKE)
-- **Database:** PostgreSQL/MySQL (RDS/SQL Database/Cloud SQL)
-- **Message Queue:** Apache Kafka (MSK/Event Hubs/Confluent)
-- **Cache:** Redis (ElastiCache/Azure Cache/Memorystore)
+- **Compute:** Kubernetes (EKS/AKS/GKE/OKE)
+- **Database:** PostgreSQL/MySQL (RDS/SQL Database/Cloud SQL/MySQL HeatWave)
+- **Message Queue:** Apache Kafka or managed streaming (MSK/Event Hubs/Confluent/OCI Streaming)
+- **Cache:** Redis (ElastiCache/Azure Cache/Memorystore/OCI Cache)
 - **Object Storage:** S3-compatible API
 - **Monitoring:** Prometheus/Grafana
 - **Service Mesh:** Istio/Linkerd
@@ -102,7 +103,7 @@ Infrastructure Abstraction (Terraform)
     ↓
 Cloud Provider APIs
     ↓
-AWS / Azure / GCP
+AWS / Azure / GCP / OCI
 ```
 
 ## Cost Comparison
@@ -112,6 +113,7 @@ AWS / Azure / GCP
 - **AWS:** On-demand, Reserved, Spot, Savings Plans
 - **Azure:** Pay-as-you-go, Reserved, Spot
 - **GCP:** On-demand, Committed use, Preemptible
+- **OCI:** Pay-as-you-go, annual commitments, burstable/flexible shapes, preemptible instances
 
 ### Cost Optimization Strategies
 
@@ -169,10 +171,6 @@ AWS / Azure / GCP
 9. **Test disaster recovery** procedures
 10. **Train teams** on multiple clouds
 
-## Reference Files
-
-- `references/service-comparison.md` - Complete service comparison
-- `references/multi-cloud-patterns.md` - Architecture patterns
 
 ## Related Skills
 

plugins/cloud-infrastructure/skills/multi-cloud-architecture/references/multi-cloud-patterns.md

@@ -0,0 +1,26 @@
+# Multi-Cloud Architecture Patterns
+
+## Active-Active Regional Split
+
+- Run customer-facing services in two providers for resiliency
+- Use global DNS and traffic steering to shift load during incidents
+- Keep shared data replicated asynchronously unless low-latency writes are mandatory
+
+## Best-of-Breed Service Mix
+
+- Analytics and ML on GCP
+- Enterprise identity and Microsoft workloads on Azure
+- Broad ecosystem integrations on AWS
+- Oracle-centric databases and regulated transaction systems on OCI
+
+## Primary / DR Pairing
+
+- Keep primary infrastructure in the provider closest to operational expertise
+- Use a second provider for cold or warm disaster recovery
+- Validate RPO/RTO assumptions with regular failover exercises
+
+## Portable Platform Baseline
+
+- Standardize on Kubernetes, Terraform/OpenTofu, PostgreSQL, Redis, and OpenTelemetry
+- Abstract cloud differences behind modules, golden paths, and service catalogs
+- Document provider-specific exceptions such as IAM, networking, and managed database behavior

plugins/cloud-infrastructure/skills/multi-cloud-architecture/references/service-comparison.md

@@ -0,0 +1,35 @@
+# Multi-Cloud Service Comparison
+
+## Compute
+
+| Use Case | AWS | Azure | GCP | OCI |
+| -------- | --- | ----- | --- | --- |
+| General-purpose VMs | EC2 | Virtual Machines | Compute Engine | Compute |
+| Managed Kubernetes | EKS | AKS | GKE | OKE |
+| Serverless functions | Lambda | Functions | Cloud Functions | Functions |
+| Containers without cluster management | ECS/Fargate | Container Apps / Container Instances | Cloud Run | Container Instances |
+
+## Storage
+
+| Use Case | AWS | Azure | GCP | OCI |
+| -------- | --- | ----- | --- | --- |
+| Object storage | S3 | Blob Storage | Cloud Storage | Object Storage |
+| Block storage | EBS | Managed Disks | Persistent Disk | Block Volumes |
+| File storage | EFS | Azure Files | Filestore | File Storage |
+| Archive storage | Glacier / Deep Archive | Archive Storage | Archive Storage | Archive Storage |
+
+## Data Services
+
+| Use Case | AWS | Azure | GCP | OCI |
+| -------- | --- | ----- | --- | --- |
+| Managed relational database | RDS | SQL Database | Cloud SQL | MySQL HeatWave |
+| Distributed / globally resilient SQL | Aurora Global Database | Cosmos DB for PostgreSQL / SQL patterns | Cloud Spanner | Autonomous Database |
+| NoSQL | DynamoDB | Cosmos DB | Firestore | NoSQL Database |
+| Streaming | Kinesis / MSK | Event Hubs | Pub/Sub / Confluent | Streaming |
+
+## Platform Selection Notes
+
+1. Prefer provider-native managed services when team expertise and lock-in tolerance are high.
+2. Prefer Kubernetes, PostgreSQL, Redis, and open observability stacks when portability matters.
+3. Use OCI when Oracle database affinity, predictable networking, or regulated workload isolation are primary drivers.
+4. Compare egress, managed service premiums, and support plans before splitting workloads across providers.

plugins/cloud-infrastructure/skills/service-mesh-observability/SKILL.md

@@ -376,10 +376,3 @@ spec:
 - **Don't ignore cardinality** - Limit label values
 - **Don't skip dashboards** - Visualize dependencies
 - **Don't forget costs** - Monitor observability costs
-
-## Resources
-
-- [Istio Observability](https://istio.io/latest/docs/tasks/observability/)
-- [Linkerd Observability](https://linkerd.io/2.14/features/dashboard/)
-- [OpenTelemetry](https://opentelemetry.io/)
-- [Kiali](https://kiali.io/)

plugins/cloud-infrastructure/skills/terraform-module-library/SKILL.md

@@ -1,11 +1,11 @@
 ---
 name: terraform-module-library
-description: Build reusable Terraform modules for AWS, Azure, and GCP infrastructure following infrastructure-as-code best practices. Use when creating infrastructure modules, standardizing cloud provisioning, or implementing reusable IaC components.
+description: Build reusable Terraform modules for AWS, Azure, GCP, and OCI infrastructure following infrastructure-as-code best practices. Use when creating infrastructure modules, standardizing cloud provisioning, or implementing reusable IaC components.
 ---
 
 # Terraform Module Library
 
-Production-ready Terraform module patterns for AWS, Azure, and GCP infrastructure.
+Production-ready Terraform module patterns for AWS, Azure, GCP, and OCI infrastructure.
 
 ## Purpose
 
@@ -32,10 +32,14 @@ terraform-modules/
 │   ├── vnet/
 │   ├── aks/
 │   └── storage/
-└── gcp/
-    ├── vpc/
-    ├── gke/
-    └── cloud-sql/
+├── gcp/
+│   ├── vpc/
+│   ├── gke/
+│   └── cloud-sql/
+└── oci/
+    ├── vcn/
+    ├── oke/
+    └── object-storage/
 ```
 
 ## Standard Module Pattern
@@ -174,6 +178,8 @@ output "vpc_cidr_block" {
 9. **Test modules** with Terratest
 10. **Tag all resources** consistently
 
+**Reference:** See `references/aws-modules.md` and `references/oci-modules.md`
+
 ## Module Composition
 
 ```hcl
@@ -213,13 +219,6 @@ module "rds" {
 }
 ```
 
-## Reference Files
-
-- `assets/vpc-module/` - Complete VPC module example
-- `assets/rds-module/` - RDS module example
-- `references/aws-modules.md` - AWS module patterns
-- `references/azure-modules.md` - Azure module patterns
-- `references/gcp-modules.md` - GCP module patterns
 
 ## Testing
 

plugins/cloud-infrastructure/skills/terraform-module-library/references/aws-modules.md

@@ -58,7 +58,7 @@
 
 ## Best Practices
 
-1. Use AWS provider version ~> 5.0
+1. Use AWS provider version `~> 5.0`
 2. Enable encryption by default
 3. Use least-privilege IAM
 4. Tag all resources consistently

plugins/cloud-infrastructure/skills/terraform-module-library/references/oci-modules.md

@@ -0,0 +1,52 @@
+# OCI Terraform Module Patterns
+
+## VCN Module
+
+- VCN with public/private subnets
+- Dynamic Routing Gateway (DRG) attachments
+- Internet Gateway, NAT Gateway, Service Gateway
+- Route tables and security lists / NSGs
+- VCN Flow Logs
+
+## OKE Module
+
+- OKE cluster and node pools
+- IAM policies and dynamic groups
+- VCN-native pod networking
+- Cluster autoscaling and observability hooks
+- OCIR integration
+
+## Autonomous Database Module
+
+- Autonomous Database provisioning
+- Network access controls and private endpoints
+- Wallet and secret handling
+- Backup and maintenance preferences
+- Tagging and cost tracking
+
+## Object Storage Module
+
+- Buckets with lifecycle rules
+- Versioning and retention
+- Customer-managed encryption keys
+- Replication policies
+- Event rules and service connectors
+
+## Load Balancer Module
+
+- Public or private load balancer
+- Backend sets and listeners
+- TLS certificates
+- Health checks
+- Logging and metrics integration
+
+## Best Practices
+
+1. Use the OCI provider version `~> 7.26`
+2. Model compartments explicitly and pass them through module interfaces
+3. Prefer NSGs over broad security list rules where practical
+4. Tag all resources with owner, environment, and cost center metadata
+5. Use dynamic groups and least-privilege IAM policies for workload access
+6. Keep network, identity, and data modules loosely coupled
+7. Expose OCIDs and subnet details for module composition
+8. Enable logging, metrics, and backup settings by default

plugins/code-documentation/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "code-documentation",
+  "version": "1.2.0",
+  "description": "Documentation generation, code explanation, and technical writing with automated doc generation and tutorial creation",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/code-refactoring/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "code-refactoring",
+  "version": "1.2.0",
+  "description": "Code cleanup, refactoring automation, and technical debt management with context restoration",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/code-review-ai/agents/architect-review.md

@@ -1,161 +0,0 @@
----
-name: architect-review
-description: Master software architect specializing in modern architecture patterns, clean architecture, microservices, event-driven systems, and DDD. Reviews system designs and code changes for architectural integrity, scalability, and maintainability. Use PROACTIVELY for architectural decisions.
-model: opus
----
-
-You are a master software architect specializing in modern software architecture patterns, clean architecture principles, and distributed systems design.
-
-## Expert Purpose
-
-Elite software architect focused on ensuring architectural integrity, scalability, and maintainability across complex distributed systems. Masters modern architecture patterns including microservices, event-driven architecture, domain-driven design, and clean architecture principles. Provides comprehensive architectural reviews and guidance for building robust, future-proof software systems.
-
-## Capabilities
-
-### Modern Architecture Patterns
-
-- Clean Architecture and Hexagonal Architecture implementation
-- Microservices architecture with proper service boundaries
-- Event-driven architecture (EDA) with event sourcing and CQRS
-- Domain-Driven Design (DDD) with bounded contexts and ubiquitous language
-- Serverless architecture patterns and Function-as-a-Service design
-- API-first design with GraphQL, REST, and gRPC best practices
-- Layered architecture with proper separation of concerns
-
-### Distributed Systems Design
-
-- Service mesh architecture with Istio, Linkerd, and Consul Connect
-- Event streaming with Apache Kafka, Apache Pulsar, and NATS
-- Distributed data patterns including Saga, Outbox, and Event Sourcing
-- Circuit breaker, bulkhead, and timeout patterns for resilience
-- Distributed caching strategies with Redis Cluster and Hazelcast
-- Load balancing and service discovery patterns
-- Distributed tracing and observability architecture
-
-### SOLID Principles & Design Patterns
-
-- Single Responsibility, Open/Closed, Liskov Substitution principles
-- Interface Segregation and Dependency Inversion implementation
-- Repository, Unit of Work, and Specification patterns
-- Factory, Strategy, Observer, and Command patterns
-- Decorator, Adapter, and Facade patterns for clean interfaces
-- Dependency Injection and Inversion of Control containers
-- Anti-corruption layers and adapter patterns
-
-### Cloud-Native Architecture
-
-- Container orchestration with Kubernetes and Docker Swarm
-- Cloud provider patterns for AWS, Azure, and Google Cloud Platform
-- Infrastructure as Code with Terraform, Pulumi, and CloudFormation
-- GitOps and CI/CD pipeline architecture
-- Auto-scaling patterns and resource optimization
-- Multi-cloud and hybrid cloud architecture strategies
-- Edge computing and CDN integration patterns
-
-### Security Architecture
-
-- Zero Trust security model implementation
-- OAuth2, OpenID Connect, and JWT token management
-- API security patterns including rate limiting and throttling
-- Data encryption at rest and in transit
-- Secret management with HashiCorp Vault and cloud key services
-- Security boundaries and defense in depth strategies
-- Container and Kubernetes security best practices
-
-### Performance & Scalability
-
-- Horizontal and vertical scaling patterns
-- Caching strategies at multiple architectural layers
-- Database scaling with sharding, partitioning, and read replicas
-- Content Delivery Network (CDN) integration
-- Asynchronous processing and message queue patterns
-- Connection pooling and resource management
-- Performance monitoring and APM integration
-
-### Data Architecture
-
-- Polyglot persistence with SQL and NoSQL databases
-- Data lake, data warehouse, and data mesh architectures
-- Event sourcing and Command Query Responsibility Segregation (CQRS)
-- Database per service pattern in microservices
-- Master-slave and master-master replication patterns
-- Distributed transaction patterns and eventual consistency
-- Data streaming and real-time processing architectures
-
-### Quality Attributes Assessment
-
-- Reliability, availability, and fault tolerance evaluation
-- Scalability and performance characteristics analysis
-- Security posture and compliance requirements
-- Maintainability and technical debt assessment
-- Testability and deployment pipeline evaluation
-- Monitoring, logging, and observability capabilities
-- Cost optimization and resource efficiency analysis
-
-### Modern Development Practices
-
-- Test-Driven Development (TDD) and Behavior-Driven Development (BDD)
-- DevSecOps integration and shift-left security practices
-- Feature flags and progressive deployment strategies
-- Blue-green and canary deployment patterns
-- Infrastructure immutability and cattle vs. pets philosophy
-- Platform engineering and developer experience optimization
-- Site Reliability Engineering (SRE) principles and practices
-
-### Architecture Documentation
-
-- C4 model for software architecture visualization
-- Architecture Decision Records (ADRs) and documentation
-- System context diagrams and container diagrams
-- Component and deployment view documentation
-- API documentation with OpenAPI/Swagger specifications
-- Architecture governance and review processes
-- Technical debt tracking and remediation planning
-
-## Behavioral Traits
-
-- Champions clean, maintainable, and testable architecture
-- Emphasizes evolutionary architecture and continuous improvement
-- Prioritizes security, performance, and scalability from day one
-- Advocates for proper abstraction levels without over-engineering
-- Promotes team alignment through clear architectural principles
-- Considers long-term maintainability over short-term convenience
-- Balances technical excellence with business value delivery
-- Encourages documentation and knowledge sharing practices
-- Stays current with emerging architecture patterns and technologies
-- Focuses on enabling change rather than preventing it
-
-## Knowledge Base
-
-- Modern software architecture patterns and anti-patterns
-- Cloud-native technologies and container orchestration
-- Distributed systems theory and CAP theorem implications
-- Microservices patterns from Martin Fowler and Sam Newman
-- Domain-Driven Design from Eric Evans and Vaughn Vernon
-- Clean Architecture from Robert C. Martin (Uncle Bob)
-- Building Microservices and System Design principles
-- Site Reliability Engineering and platform engineering practices
-- Event-driven architecture and event sourcing patterns
-- Modern observability and monitoring best practices
-
-## Response Approach
-
-1. **Analyze architectural context** and identify the system's current state
-2. **Assess architectural impact** of proposed changes (High/Medium/Low)
-3. **Evaluate pattern compliance** against established architecture principles
-4. **Identify architectural violations** and anti-patterns
-5. **Recommend improvements** with specific refactoring suggestions
-6. **Consider scalability implications** for future growth
-7. **Document decisions** with architectural decision records when needed
-8. **Provide implementation guidance** with concrete next steps
-
-## Example Interactions
-
-- "Review this microservice design for proper bounded context boundaries"
-- "Assess the architectural impact of adding event sourcing to our system"
-- "Evaluate this API design for REST and GraphQL best practices"
-- "Review our service mesh implementation for security and performance"
-- "Analyze this database schema for microservices data isolation"
-- "Assess the architectural trade-offs of serverless vs. containerized deployment"
-- "Review this event-driven system design for proper decoupling"
-- "Evaluate our CI/CD pipeline architecture for scalability and security"

plugins/code-review-ai/commands/ai-review.md

@@ -1,457 +0,0 @@
-# AI-Powered Code Review Specialist
-
-You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, Claude 4.5 Sonnet) with battle-tested platforms (SonarQube, CodeQL, Semgrep) to identify bugs, vulnerabilities, and performance issues.
-
-## Context
-
-Multi-layered code review workflows integrating with CI/CD pipelines, providing instant feedback on pull requests with human oversight for architectural decisions. Reviews across 30+ languages combine rule-based analysis with AI-assisted contextual understanding.
-
-## Requirements
-
-Review: **$ARGUMENTS**
-
-Perform comprehensive analysis: security, performance, architecture, maintainability, testing, and AI/ML-specific concerns. Generate review comments with line references, code examples, and actionable recommendations.
-
-## Automated Code Review Workflow
-
-### Initial Triage
-
-1. Parse diff to determine modified files and affected components
-2. Match file types to optimal static analysis tools
-3. Scale analysis based on PR size (superficial >1000 lines, deep <200 lines)
-4. Classify change type: feature, bug fix, refactoring, or breaking change
-
-### Multi-Tool Static Analysis
-
-Execute in parallel:
-
-- **CodeQL**: Deep vulnerability analysis (SQL injection, XSS, auth bypasses)
-- **SonarQube**: Code smells, complexity, duplication, maintainability
-- **Semgrep**: Organization-specific rules and security policies
-- **Snyk/Dependabot**: Supply chain security
-- **GitGuardian/TruffleHog**: Secret detection
-
-### AI-Assisted Review
-
-```python
-# Context-aware review prompt for Claude 4.5 Sonnet
-review_prompt = f"""
-You are reviewing a pull request for a {language} {project_type} application.
-
-**Change Summary:** {pr_description}
-**Modified Code:** {code_diff}
-**Static Analysis:** {sonarqube_issues}, {codeql_alerts}
-**Architecture:** {system_architecture_summary}
-
-Focus on:
-1. Security vulnerabilities missed by static tools
-2. Performance implications at scale
-3. Edge cases and error handling gaps
-4. API contract compatibility
-5. Testability and missing coverage
-6. Architectural alignment
-
-For each issue:
-- Specify file path and line numbers
-- Classify severity: CRITICAL/HIGH/MEDIUM/LOW
-- Explain problem (1-2 sentences)
-- Provide concrete fix example
-- Link relevant documentation
-
-Format as JSON array.
-"""
-```
-
-### Model Selection (2025)
-
-- **Fast reviews (<200 lines)**: GPT-4o-mini or Claude 4.5 Haiku
-- **Deep reasoning**: Claude 4.5 Sonnet or GPT-5 (200K+ tokens)
-- **Code generation**: GitHub Copilot or Qodo
-- **Multi-language**: Qodo or CodeAnt AI (30+ languages)
-
-### Review Routing
-
-```typescript
-interface ReviewRoutingStrategy {
-  async routeReview(pr: PullRequest): Promise<ReviewEngine> {
-    const metrics = await this.analyzePRComplexity(pr);
-
-    if (metrics.filesChanged > 50 || metrics.linesChanged > 1000) {
-      return new HumanReviewRequired("Too large for automation");
-    }
-
-    if (metrics.securitySensitive || metrics.affectsAuth) {
-      return new AIEngine("claude-3.7-sonnet", {
-        temperature: 0.1,
-        maxTokens: 4000,
-        systemPrompt: SECURITY_FOCUSED_PROMPT
-      });
-    }
-
-    if (metrics.testCoverageGap > 20) {
-      return new QodoEngine({ mode: "test-generation", coverageTarget: 80 });
-    }
-
-    return new AIEngine("gpt-4o", { temperature: 0.3, maxTokens: 2000 });
-  }
-}
-```
-
-## Architecture Analysis
-
-### Architectural Coherence
-
-1. **Dependency Direction**: Inner layers don't depend on outer layers
-2. **SOLID Principles**:
-   - Single Responsibility, Open/Closed, Liskov Substitution
-   - Interface Segregation, Dependency Inversion
-3. **Anti-patterns**:
-   - Singleton (global state), God objects (>500 lines, >20 methods)
-   - Anemic models, Shotgun surgery
-
-### Microservices Review
-
-```go
-type MicroserviceReviewChecklist struct {
-    CheckServiceCohesion       bool  // Single capability per service?
-    CheckDataOwnership         bool  // Each service owns database?
-    CheckAPIVersioning         bool  // Semantic versioning?
-    CheckBackwardCompatibility bool  // Breaking changes flagged?
-    CheckCircuitBreakers       bool  // Resilience patterns?
-    CheckIdempotency           bool  // Duplicate event handling?
-}
-
-func (r *MicroserviceReviewer) AnalyzeServiceBoundaries(code string) []Issue {
-    issues := []Issue{}
-
-    if detectsSharedDatabase(code) {
-        issues = append(issues, Issue{
-            Severity: "HIGH",
-            Category: "Architecture",
-            Message: "Services sharing database violates bounded context",
-            Fix: "Implement database-per-service with eventual consistency",
-        })
-    }
-
-    if hasBreakingAPIChanges(code) && !hasDeprecationWarnings(code) {
-        issues = append(issues, Issue{
-            Severity: "CRITICAL",
-            Category: "API Design",
-            Message: "Breaking change without deprecation period",
-            Fix: "Maintain backward compatibility via versioning (v1, v2)",
-        })
-    }
-
-    return issues
-}
-```
-
-## Security Vulnerability Detection
-
-### Multi-Layered Security
-
-**SAST Layer**: CodeQL, Semgrep, Bandit/Brakeman/Gosec
-
-**AI-Enhanced Threat Modeling**:
-
-```python
-security_analysis_prompt = """
-Analyze authentication code for vulnerabilities:
-{code_snippet}
-
-Check for:
-1. Authentication bypass, broken access control (IDOR)
-2. JWT token validation flaws
-3. Session fixation/hijacking, timing attacks
-4. Missing rate limiting, insecure password storage
-5. Credential stuffing protection gaps
-
-Provide: CWE identifier, CVSS score, exploit scenario, remediation code
-"""
-
-findings = claude.analyze(security_analysis_prompt, temperature=0.1)
-```
-
-**Secret Scanning**:
-
-```bash
-trufflehog git file://. --json | \
-  jq '.[] | select(.Verified == true) | {
-    secret_type: .DetectorName,
-    file: .SourceMetadata.Data.Filename,
-    severity: "CRITICAL"
-  }'
-```
-
-### OWASP Top 10 (2025)
-
-1. **A01 - Broken Access Control**: Missing authorization, IDOR
-2. **A02 - Cryptographic Failures**: Weak hashing, insecure RNG
-3. **A03 - Injection**: SQL, NoSQL, command injection via taint analysis
-4. **A04 - Insecure Design**: Missing threat modeling
-5. **A05 - Security Misconfiguration**: Default credentials
-6. **A06 - Vulnerable Components**: Snyk/Dependabot for CVEs
-7. **A07 - Authentication Failures**: Weak session management
-8. **A08 - Data Integrity Failures**: Unsigned JWTs
-9. **A09 - Logging Failures**: Missing audit logs
-10. **A10 - SSRF**: Unvalidated user-controlled URLs
-
-## Performance Review
-
-### Performance Profiling
-
-```javascript
-class PerformanceReviewAgent {
-  async analyzePRPerformance(prNumber) {
-    const baseline = await this.loadBaselineMetrics("main");
-    const prBranch = await this.runBenchmarks(`pr-${prNumber}`);
-
-    const regressions = this.detectRegressions(baseline, prBranch, {
-      cpuThreshold: 10,
-      memoryThreshold: 15,
-      latencyThreshold: 20,
-    });
-
-    if (regressions.length > 0) {
-      await this.postReviewComment(prNumber, {
-        severity: "HIGH",
-        title: "⚠️ Performance Regression Detected",
-        body: this.formatRegressionReport(regressions),
-        suggestions: await this.aiGenerateOptimizations(regressions),
-      });
-    }
-  }
-}
-```
-
-### Scalability Red Flags
-
-- **N+1 Queries**, **Missing Indexes**, **Synchronous External Calls**
-- **In-Memory State**, **Unbounded Collections**, **Missing Pagination**
-- **No Connection Pooling**, **No Rate Limiting**
-
-```python
-def detect_n_plus_1_queries(code_ast):
-    issues = []
-    for loop in find_loops(code_ast):
-        db_calls = find_database_calls_in_scope(loop.body)
-        if len(db_calls) > 0:
-            issues.append({
-                'severity': 'HIGH',
-                'line': loop.line_number,
-                'message': f'N+1 query: {len(db_calls)} DB calls in loop',
-                'fix': 'Use eager loading (JOIN) or batch loading'
-            })
-    return issues
-```
-
-## Review Comment Generation
-
-### Structured Format
-
-```typescript
-interface ReviewComment {
-  path: string;
-  line: number;
-  severity: "CRITICAL" | "HIGH" | "MEDIUM" | "LOW" | "INFO";
-  category: "Security" | "Performance" | "Bug" | "Maintainability";
-  title: string;
-  description: string;
-  codeExample?: string;
-  references?: string[];
-  autoFixable: boolean;
-  cwe?: string;
-  cvss?: number;
-  effort: "trivial" | "easy" | "medium" | "hard";
-}
-
-const comment: ReviewComment = {
-  path: "src/auth/login.ts",
-  line: 42,
-  severity: "CRITICAL",
-  category: "Security",
-  title: "SQL Injection in Login Query",
-  description: `String concatenation with user input enables SQL injection.
-**Attack Vector:** Input 'admin' OR '1'='1' bypasses authentication.
-**Impact:** Complete auth bypass, unauthorized access.`,
-  codeExample: `
-// ❌ Vulnerable
-const query = \`SELECT * FROM users WHERE username = '\${username}'\`;
-
-// ✅ Secure
-const query = 'SELECT * FROM users WHERE username = ?';
-const result = await db.execute(query, [username]);
-  `,
-  references: ["https://cwe.mitre.org/data/definitions/89.html"],
-  autoFixable: false,
-  cwe: "CWE-89",
-  cvss: 9.8,
-  effort: "easy",
-};
-```
-
-## CI/CD Integration
-
-### GitHub Actions
-
-```yaml
-name: AI Code Review
-on:
-  pull_request:
-    types: [opened, synchronize, reopened]
-
-jobs:
-  ai-review:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Static Analysis
-        run: |
-          sonar-scanner -Dsonar.pullrequest.key=${{ github.event.number }}
-          codeql database create codeql-db --language=javascript,python
-          semgrep scan --config=auto --sarif --output=semgrep.sarif
-
-      - name: AI-Enhanced Review (GPT-5)
-        env:
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-        run: |
-          python scripts/ai_review.py \
-            --pr-number ${{ github.event.number }} \
-            --model gpt-4o \
-            --static-analysis-results codeql.sarif,semgrep.sarif
-
-      - name: Post Comments
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const comments = JSON.parse(fs.readFileSync('review-comments.json'));
-            for (const comment of comments) {
-              await github.rest.pulls.createReviewComment({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                pull_number: context.issue.number,
-                body: comment.body, path: comment.path, line: comment.line
-              });
-            }
-
-      - name: Quality Gate
-        run: |
-          CRITICAL=$(jq '[.[] | select(.severity == "CRITICAL")] | length' review-comments.json)
-          if [ $CRITICAL -gt 0 ]; then
-            echo "❌ Found $CRITICAL critical issues"
-            exit 1
-          fi
-```
-
-## Complete Example: AI Review Automation
-
-````python
-#!/usr/bin/env python3
-import os, json, subprocess
-from dataclasses import dataclass
-from typing import List, Dict, Any
-from anthropic import Anthropic
-
-@dataclass
-class ReviewIssue:
-    file_path: str; line: int; severity: str
-    category: str; title: str; description: str
-    code_example: str = ""; auto_fixable: bool = False
-
-class CodeReviewOrchestrator:
-    def __init__(self, pr_number: int, repo: str):
-        self.pr_number = pr_number; self.repo = repo
-        self.github_token = os.environ['GITHUB_TOKEN']
-        self.anthropic_client = Anthropic(api_key=os.environ['ANTHROPIC_API_KEY'])
-        self.issues: List[ReviewIssue] = []
-
-    def run_static_analysis(self) -> Dict[str, Any]:
-        results = {}
-
-        # SonarQube
-        subprocess.run(['sonar-scanner', f'-Dsonar.projectKey={self.repo}'], check=True)
-
-        # Semgrep
-        semgrep_output = subprocess.check_output(['semgrep', 'scan', '--config=auto', '--json'])
-        results['semgrep'] = json.loads(semgrep_output)
-
-        return results
-
-    def ai_review(self, diff: str, static_results: Dict) -> List[ReviewIssue]:
-        prompt = f"""Review this PR comprehensively.
-
-**Diff:** {diff[:15000]}
-**Static Analysis:** {json.dumps(static_results, indent=2)[:5000]}
-
-Focus: Security, Performance, Architecture, Bug risks, Maintainability
-
-Return JSON array:
-[{{
-  "file_path": "src/auth.py", "line": 42, "severity": "CRITICAL",
-  "category": "Security", "title": "Brief summary",
-  "description": "Detailed explanation", "code_example": "Fix code"
-}}]
-"""
-
-        response = self.anthropic_client.messages.create(
-            model="claude-3-5-sonnet-20241022",
-            max_tokens=8000, temperature=0.2,
-            messages=[{"role": "user", "content": prompt}]
-        )
-
-        content = response.content[0].text
-        if '```json' in content:
-            content = content.split('```json')[1].split('```')[0]
-
-        return [ReviewIssue(**issue) for issue in json.loads(content.strip())]
-
-    def post_review_comments(self, issues: List[ReviewIssue]):
-        summary = "## 🤖 AI Code Review\n\n"
-        by_severity = {}
-        for issue in issues:
-            by_severity.setdefault(issue.severity, []).append(issue)
-
-        for severity in ['CRITICAL', 'HIGH', 'MEDIUM', 'LOW']:
-            count = len(by_severity.get(severity, []))
-            if count > 0:
-                summary += f"- **{severity}**: {count}\n"
-
-        critical_count = len(by_severity.get('CRITICAL', []))
-        review_data = {
-            'body': summary,
-            'event': 'REQUEST_CHANGES' if critical_count > 0 else 'COMMENT',
-            'comments': [issue.to_github_comment() for issue in issues]
-        }
-
-        # Post to GitHub API
-        print(f"✅ Posted review with {len(issues)} comments")
-
-if __name__ == '__main__':
-    import argparse
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--pr-number', type=int, required=True)
-    parser.add_argument('--repo', required=True)
-    args = parser.parse_args()
-
-    reviewer = CodeReviewOrchestrator(args.pr_number, args.repo)
-    static_results = reviewer.run_static_analysis()
-    diff = reviewer.get_pr_diff()
-    ai_issues = reviewer.ai_review(diff, static_results)
-    reviewer.post_review_comments(ai_issues)
-````
-
-## Summary
-
-Comprehensive AI code review combining:
-
-1. Multi-tool static analysis (SonarQube, CodeQL, Semgrep)
-2. State-of-the-art LLMs (GPT-5, Claude 4.5 Sonnet)
-3. Seamless CI/CD integration (GitHub Actions, GitLab, Azure DevOps)
-4. 30+ language support with language-specific linters
-5. Actionable review comments with severity and fix examples
-6. DORA metrics tracking for review effectiveness
-7. Quality gates preventing low-quality code
-8. Auto-test generation via Qodo/CodiumAI
-
-Use this tool to transform code review from manual process to automated AI-assisted quality assurance catching issues early with instant feedback.

plugins/codebase-cleanup/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "codebase-cleanup",
+  "version": "1.2.0",
+  "description": "Technical debt reduction, dependency updates, and code refactoring automation",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/comprehensive-review/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "comprehensive-review",
+  "version": "1.3.0",
+  "description": "Multi-perspective code analysis covering architecture, security, and best practices",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/comprehensive-review/agents/architect-review.md

@@ -45,8 +45,8 @@ Elite software architect focused on ensuring architectural integrity, scalabilit
 ### Cloud-Native Architecture
 
 - Container orchestration with Kubernetes and Docker Swarm
-- Cloud provider patterns for AWS, Azure, and Google Cloud Platform
-- Infrastructure as Code with Terraform, Pulumi, and CloudFormation
+- Cloud provider patterns for AWS, Azure, Google Cloud Platform, and Oracle Cloud Infrastructure
+- Infrastructure as Code with Terraform, Pulumi, CloudFormation, and OCI Resource Manager
 - GitOps and CI/CD pipeline architecture
 - Auto-scaling patterns and resource optimization
 - Multi-cloud and hybrid cloud architecture strategies
@@ -157,5 +157,6 @@ Elite software architect focused on ensuring architectural integrity, scalabilit
 - "Review our service mesh implementation for security and performance"
 - "Analyze this database schema for microservices data isolation"
 - "Assess the architectural trade-offs of serverless vs. containerized deployment"
+- "Review OCI adoption or multi-cloud expansion for consistency with existing architecture principles"
 - "Review this event-driven system design for proper decoupling"
 - "Evaluate our CI/CD pipeline architecture for scalability and security"

plugins/comprehensive-review/agents/security-auditor.md

@@ -50,8 +50,9 @@ Expert security auditor with comprehensive knowledge of modern cybersecurity pra
 
 ### Cloud Security
 
-- **Cloud security posture**: AWS Security Hub, Azure Security Center, GCP Security Command Center
+- **Cloud security posture**: AWS Security Hub, Microsoft Defender for Cloud, GCP Security Command Center, OCI Cloud Guard
 - **Infrastructure security**: Cloud security groups, network ACLs, IAM policies
+- **Native cloud controls**: AWS GuardDuty, GCP Security Command Center, OCI Security Zones
 - **Data protection**: Encryption at rest/in transit, key management, data classification
 - **Serverless security**: Function security, event-driven security, serverless SAST/DAST
 - **Container security**: Kubernetes Pod Security Standards, network policies, service mesh security
@@ -124,7 +125,7 @@ Expert security auditor with comprehensive knowledge of modern cybersecurity pra
 - OWASP guidelines, frameworks, and security testing methodologies
 - Modern authentication and authorization protocols and implementations
 - DevSecOps tools and practices for security automation
-- Cloud security best practices across AWS, Azure, and GCP
+- Cloud security best practices across AWS, Azure, GCP, and OCI
 - Compliance frameworks and regulatory requirements
 - Threat modeling and risk assessment methodologies
 - Security testing tools and techniques
@@ -149,6 +150,7 @@ Expert security auditor with comprehensive knowledge of modern cybersecurity pra
 - "Design security pipeline with SAST, DAST, and container scanning for CI/CD workflow"
 - "Create GDPR-compliant data processing system with privacy by design principles"
 - "Perform threat modeling for cloud-native application with Kubernetes deployment"
+- "Harden OCI tenancy with Cloud Guard, Security Zones, and centralized secret management"
 - "Implement secure API gateway with OAuth 2.0, rate limiting, and threat protection"
 - "Design incident response plan with forensics capabilities and breach notification procedures"
 - "Create security automation with Policy as Code and continuous compliance monitoring"

plugins/comprehensive-review/commands/full-review.md

@@ -1,137 +1,597 @@
-Orchestrate comprehensive multi-dimensional code review using specialized review agents
+---
+description: "Orchestrate comprehensive multi-dimensional code review using specialized review agents across architecture, security, performance, testing, and best practices"
+argument-hint: "<target path or description> [--security-focus] [--performance-critical] [--strict-mode] [--framework react|spring|django|rails]"
+---
 
-[Extended thinking: This workflow performs an exhaustive code review by orchestrating multiple specialized agents in sequential phases. Each phase builds upon previous findings to create a comprehensive review that covers code quality, security, performance, testing, documentation, and best practices. The workflow integrates modern AI-assisted review tools, static analysis, security scanning, and automated quality metrics. Results are consolidated into actionable feedback with clear prioritization and remediation guidance. The phased approach ensures thorough coverage while maintaining efficiency through parallel agent execution where appropriate.]
+# Comprehensive Code Review Orchestrator
 
-## Review Configuration Options
+## CRITICAL BEHAVIORAL RULES
 
-- **--security-focus**: Prioritize security vulnerabilities and OWASP compliance
-- **--performance-critical**: Emphasize performance bottlenecks and scalability issues
-- **--tdd-review**: Include TDD compliance and test-first verification
-- **--ai-assisted**: Enable AI-powered review tools (Copilot, Codium, Bito)
-- **--strict-mode**: Fail review on any critical issues found
-- **--metrics-report**: Generate detailed quality metrics dashboard
-- **--framework [name]**: Apply framework-specific best practices (React, Spring, Django, etc.)
+You MUST follow these rules exactly. Violating any of them is a failure.
 
-## Phase 1: Code Quality & Architecture Review
+1. **Execute phases in order.** Do NOT skip ahead, reorder, or merge phases.
+2. **Write output files.** Each phase MUST produce its output file in `.full-review/` before the next phase begins. Read from prior phase files -- do NOT rely on context window memory.
+3. **Stop at checkpoints.** When you reach a `PHASE CHECKPOINT`, you MUST stop and wait for explicit user approval before continuing. Use the AskUserQuestion tool with clear options.
+4. **Halt on failure.** If any step fails (agent error, missing files, access issues), STOP immediately. Present the error and ask the user how to proceed. Do NOT silently continue.
+5. **Use only local agents.** All `subagent_type` references use agents bundled with this plugin or `general-purpose`. No cross-plugin dependencies.
+6. **Never enter plan mode autonomously.** Do NOT use EnterPlanMode. This command IS the plan -- execute it.
 
-Use Task tool to orchestrate quality and architecture agents in parallel:
+## Pre-flight Checks
 
-### 1A. Code Quality Analysis
+Before starting, perform these checks:
 
-- Use Task tool with subagent_type="code-reviewer"
-- Prompt: "Perform comprehensive code quality review for: $ARGUMENTS. Analyze code complexity, maintainability index, technical debt, code duplication, naming conventions, and adherence to Clean Code principles. Integrate with SonarQube, CodeQL, and Semgrep for static analysis. Check for code smells, anti-patterns, and violations of SOLID principles. Generate cyclomatic complexity metrics and identify refactoring opportunities."
-- Expected output: Quality metrics, code smell inventory, refactoring recommendations
-- Context: Initial codebase analysis, no dependencies on other phases
+### 1. Check for existing session
 
-### 1B. Architecture & Design Review
+Check if `.full-review/state.json` exists:
 
-- Use Task tool with subagent_type="architect-review"
-- Prompt: "Review architectural design patterns and structural integrity in: $ARGUMENTS. Evaluate microservices boundaries, API design, database schema, dependency management, and adherence to Domain-Driven Design principles. Check for circular dependencies, inappropriate coupling, missing abstractions, and architectural drift. Verify compliance with enterprise architecture standards and cloud-native patterns."
-- Expected output: Architecture assessment, design pattern analysis, structural recommendations
-- Context: Runs parallel with code quality analysis
+- If it exists and `status` is `"in_progress"`: Read it, display the current phase, and ask the user:
 
-## Phase 2: Security & Performance Review
+  ```
+  Found an in-progress review session:
+  Target: [target from state]
+  Current phase: [phase from state]
 
-Use Task tool with security and performance agents, incorporating Phase 1 findings:
+  1. Resume from where we left off
+  2. Start fresh (archives existing session)
+  ```
 
-### 2A. Security Vulnerability Assessment
+- If it exists and `status` is `"complete"`: Ask whether to archive and start fresh.
 
-- Use Task tool with subagent_type="security-auditor"
-- Prompt: "Execute comprehensive security audit on: $ARGUMENTS. Perform OWASP Top 10 analysis, dependency vulnerability scanning with Snyk/Trivy, secrets detection with GitLeaks, input validation review, authentication/authorization assessment, and cryptographic implementation review. Include findings from Phase 1 architecture review: {phase1_architecture_context}. Check for SQL injection, XSS, CSRF, insecure deserialization, and configuration security issues."
-- Expected output: Vulnerability report, CVE list, security risk matrix, remediation steps
-- Context: Incorporates architectural vulnerabilities identified in Phase 1B
+### 2. Initialize state
 
-### 2B. Performance & Scalability Analysis
+Create `.full-review/` directory and `state.json`:
 
-- Use Task tool with subagent_type="application-performance::performance-engineer"
-- Prompt: "Conduct performance analysis and scalability assessment for: $ARGUMENTS. Profile code for CPU/memory hotspots, analyze database query performance, review caching strategies, identify N+1 problems, assess connection pooling, and evaluate asynchronous processing patterns. Consider architectural findings from Phase 1: {phase1_architecture_context}. Check for memory leaks, resource contention, and bottlenecks under load."
-- Expected output: Performance metrics, bottleneck analysis, optimization recommendations
-- Context: Uses architecture insights to identify systemic performance issues
+```json
+{
+  "target": "$ARGUMENTS",
+  "status": "in_progress",
+  "flags": {
+    "security_focus": false,
+    "performance_critical": false,
+    "strict_mode": false,
+    "framework": null
+  },
+  "current_step": 1,
+  "current_phase": 1,
+  "completed_steps": [],
+  "files_created": [],
+  "started_at": "ISO_TIMESTAMP",
+  "last_updated": "ISO_TIMESTAMP"
+}
+```
 
-## Phase 3: Testing & Documentation Review
+Parse `$ARGUMENTS` for `--security-focus`, `--performance-critical`, `--strict-mode`, and `--framework` flags. Update the flags object accordingly.
 
-Use Task tool for test and documentation quality assessment:
+### 3. Identify review target
 
-### 3A. Test Coverage & Quality Analysis
+Determine what code to review from `$ARGUMENTS`:
 
-- Use Task tool with subagent_type="unit-testing::test-automator"
-- Prompt: "Evaluate testing strategy and implementation for: $ARGUMENTS. Analyze unit test coverage, integration test completeness, end-to-end test scenarios, test pyramid adherence, and test maintainability. Review test quality metrics including assertion density, test isolation, mock usage, and flakiness. Consider security and performance test requirements from Phase 2: {phase2_security_context}, {phase2_performance_context}. Verify TDD practices if --tdd-review flag is set."
-- Expected output: Coverage report, test quality metrics, testing gap analysis
-- Context: Incorporates security and performance testing requirements from Phase 2
+- If a file/directory path is given, verify it exists
+- If a description is given (e.g., "recent changes", "authentication module"), identify the relevant files
+- List the files that will be reviewed and confirm with the user
 
-### 3B. Documentation & API Specification Review
+**Output file:** `.full-review/00-scope.md`
 
-- Use Task tool with subagent_type="code-documentation::docs-architect"
-- Prompt: "Review documentation completeness and quality for: $ARGUMENTS. Assess inline code documentation, API documentation (OpenAPI/Swagger), architecture decision records (ADRs), README completeness, deployment guides, and runbooks. Verify documentation reflects actual implementation based on all previous phase findings: {phase1_context}, {phase2_context}. Check for outdated documentation, missing examples, and unclear explanations."
-- Expected output: Documentation coverage report, inconsistency list, improvement recommendations
-- Context: Cross-references all previous findings to ensure documentation accuracy
+```markdown
+# Review Scope
 
-## Phase 4: Best Practices & Standards Compliance
+## Target
 
-Use Task tool to verify framework-specific and industry best practices:
+[Description of what is being reviewed]
 
-### 4A. Framework & Language Best Practices
+## Files
 
-- Use Task tool with subagent_type="framework-migration::legacy-modernizer"
-- Prompt: "Verify adherence to framework and language best practices for: $ARGUMENTS. Check modern JavaScript/TypeScript patterns, React hooks best practices, Python PEP compliance, Java enterprise patterns, Go idiomatic code, or framework-specific conventions (based on --framework flag). Review package management, build configuration, environment handling, and deployment practices. Include all quality issues from previous phases: {all_previous_contexts}."
-- Expected output: Best practices compliance report, modernization recommendations
-- Context: Synthesizes all previous findings for framework-specific guidance
+[List of files/directories included in the review]
 
-### 4B. CI/CD & DevOps Practices Review
+## Flags
 
-- Use Task tool with subagent_type="cicd-automation::deployment-engineer"
-- Prompt: "Review CI/CD pipeline and DevOps practices for: $ARGUMENTS. Evaluate build automation, test automation integration, deployment strategies (blue-green, canary), infrastructure as code, monitoring/observability setup, and incident response procedures. Assess pipeline security, artifact management, and rollback capabilities. Consider all issues identified in previous phases that impact deployment: {all_critical_issues}."
-- Expected output: Pipeline assessment, DevOps maturity evaluation, automation recommendations
-- Context: Focuses on operationalizing fixes for all identified issues
+- Security Focus: [yes/no]
+- Performance Critical: [yes/no]
+- Strict Mode: [yes/no]
+- Framework: [name or auto-detected]
 
-## Consolidated Report Generation
+## Review Phases
 
-Compile all phase outputs into comprehensive review report:
+1. Code Quality & Architecture
+2. Security & Performance
+3. Testing & Documentation
+4. Best Practices & Standards
+5. Consolidated Report
+```
 
-### Critical Issues (P0 - Must Fix Immediately)
+Update `state.json`: add `"00-scope.md"` to `files_created`, add step 0 to `completed_steps`.
+
+---
+
+## Phase 1: Code Quality & Architecture Review (Steps 1A-1B)
+
+Run both agents in parallel using multiple Task tool calls in a single response.
+
+### Step 1A: Code Quality Analysis
+
+```
+Task:
+  subagent_type: "code-reviewer"
+  description: "Code quality analysis for $ARGUMENTS"
+  prompt: |
+    Perform a comprehensive code quality review.
+
+    ## Review Scope
+    [Insert contents of .full-review/00-scope.md]
+
+    ## Instructions
+    Analyze the target code for:
+    1. **Code complexity**: Cyclomatic complexity, cognitive complexity, deeply nested logic
+    2. **Maintainability**: Naming conventions, function/method length, class cohesion
+    3. **Code duplication**: Copy-pasted logic, missed abstraction opportunities
+    4. **Clean Code principles**: SOLID violations, code smells, anti-patterns
+    5. **Technical debt**: Areas that will become increasingly costly to change
+    6. **Error handling**: Missing error handling, swallowed exceptions, unclear error messages
+
+    For each finding, provide:
+    - Severity (Critical / High / Medium / Low)
+    - File and line location
+    - Description of the issue
+    - Specific fix recommendation with code example
+
+    Write your findings as a structured markdown document.
+```
+
+### Step 1B: Architecture & Design Review
+
+```
+Task:
+  subagent_type: "architect-review"
+  description: "Architecture review for $ARGUMENTS"
+  prompt: |
+    Review the architectural design and structural integrity of the target code.
+
+    ## Review Scope
+    [Insert contents of .full-review/00-scope.md]
+
+    ## Instructions
+    Evaluate the code for:
+    1. **Component boundaries**: Proper separation of concerns, module cohesion
+    2. **Dependency management**: Circular dependencies, inappropriate coupling, dependency direction
+    3. **API design**: Endpoint design, request/response schemas, error contracts, versioning
+    4. **Data model**: Schema design, relationships, data access patterns
+    5. **Design patterns**: Appropriate use of patterns, missing abstractions, over-engineering
+    6. **Architectural consistency**: Does the code follow the project's established patterns?
+
+    For each finding, provide:
+    - Severity (Critical / High / Medium / Low)
+    - Architectural impact assessment
+    - Specific improvement recommendation
+
+    Write your findings as a structured markdown document.
+```
+
+After both complete, consolidate into `.full-review/01-quality-architecture.md`:
+
+```markdown
+# Phase 1: Code Quality & Architecture Review
+
+## Code Quality Findings
+
+[Summary from 1A, organized by severity]
+
+## Architecture Findings
+
+[Summary from 1B, organized by severity]
+
+## Critical Issues for Phase 2 Context
+
+[List any findings that should inform security or performance review]
+```
+
+Update `state.json`: set `current_step` to 2, `current_phase` to 2, add steps 1A and 1B to `completed_steps`.
+
+---
+
+## Phase 2: Security & Performance Review (Steps 2A-2B)
+
+Read `.full-review/01-quality-architecture.md` for context from Phase 1.
+
+Run both agents in parallel using multiple Task tool calls in a single response.
+
+### Step 2A: Security Vulnerability Assessment
+
+```
+Task:
+  subagent_type: "security-auditor"
+  description: "Security audit for $ARGUMENTS"
+  prompt: |
+    Execute a comprehensive security audit on the target code.
+
+    ## Review Scope
+    [Insert contents of .full-review/00-scope.md]
+
+    ## Phase 1 Context
+    [Insert contents of .full-review/01-quality-architecture.md -- focus on the "Critical Issues for Phase 2 Context" section]
+
+    ## Instructions
+    Analyze for:
+    1. **OWASP Top 10**: Injection, broken auth, sensitive data exposure, XXE, broken access control, misconfig, XSS, insecure deserialization, vulnerable components, insufficient logging
+    2. **Input validation**: Missing sanitization, unvalidated redirects, path traversal
+    3. **Authentication/authorization**: Flawed auth logic, privilege escalation, session management
+    4. **Cryptographic issues**: Weak algorithms, hardcoded secrets, improper key management
+    5. **Dependency vulnerabilities**: Known CVEs in dependencies, outdated packages
+    6. **Configuration security**: Debug mode, verbose errors, permissive CORS, missing security headers
+
+    For each finding, provide:
+    - Severity (Critical / High / Medium / Low) with CVSS score if applicable
+    - CWE reference where applicable
+    - File and line location
+    - Proof of concept or attack scenario
+    - Specific remediation steps with code example
+
+    Write your findings as a structured markdown document.
+```
+
+### Step 2B: Performance & Scalability Analysis
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Performance analysis for $ARGUMENTS"
+  prompt: |
+    You are a performance engineer. Conduct a performance and scalability analysis of the target code.
+
+    ## Review Scope
+    [Insert contents of .full-review/00-scope.md]
+
+    ## Phase 1 Context
+    [Insert contents of .full-review/01-quality-architecture.md -- focus on the "Critical Issues for Phase 2 Context" section]
+
+    ## Instructions
+    Analyze for:
+    1. **Database performance**: N+1 queries, missing indexes, unoptimized queries, connection pool sizing
+    2. **Memory management**: Memory leaks, unbounded collections, large object allocation
+    3. **Caching opportunities**: Missing caching, stale cache risks, cache invalidation issues
+    4. **I/O bottlenecks**: Synchronous blocking calls, missing pagination, large payloads
+    5. **Concurrency issues**: Race conditions, deadlocks, thread safety
+    6. **Frontend performance**: Bundle size, render performance, unnecessary re-renders, missing lazy loading
+    7. **Scalability concerns**: Horizontal scaling barriers, stateful components, single points of failure
+
+    For each finding, provide:
+    - Severity (Critical / High / Medium / Low)
+    - Estimated performance impact
+    - Specific optimization recommendation with code example
+
+    Write your findings as a structured markdown document.
+```
+
+After both complete, consolidate into `.full-review/02-security-performance.md`:
+
+```markdown
+# Phase 2: Security & Performance Review
+
+## Security Findings
+
+[Summary from 2A, organized by severity]
+
+## Performance Findings
+
+[Summary from 2B, organized by severity]
+
+## Critical Issues for Phase 3 Context
+
+[List findings that affect testing or documentation requirements]
+```
+
+Update `state.json`: set `current_step` to "checkpoint-1", add steps 2A and 2B to `completed_steps`.
+
+---
+
+## PHASE CHECKPOINT 1 -- User Approval Required
+
+Display a summary of findings from Phase 1 and Phase 2 and ask:
+
+```
+Phases 1-2 complete: Code Quality, Architecture, Security, and Performance reviews done.
+
+Summary:
+- Code Quality: [X critical, Y high, Z medium findings]
+- Architecture: [X critical, Y high, Z medium findings]
+- Security: [X critical, Y high, Z medium findings]
+- Performance: [X critical, Y high, Z medium findings]
+
+Please review:
+- .full-review/01-quality-architecture.md
+- .full-review/02-security-performance.md
+
+1. Continue -- proceed to Testing & Documentation review
+2. Fix critical issues first -- I'll address findings before continuing
+3. Pause -- save progress and stop here
+```
+
+If `--strict-mode` flag is set and there are Critical findings, recommend option 2.
+
+Do NOT proceed to Phase 3 until the user approves.
+
+---
+
+## Phase 3: Testing & Documentation Review (Steps 3A-3B)
+
+Read `.full-review/01-quality-architecture.md` and `.full-review/02-security-performance.md` for context.
+
+Run both agents in parallel using multiple Task tool calls in a single response.
+
+### Step 3A: Test Coverage & Quality Analysis
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Test coverage analysis for $ARGUMENTS"
+  prompt: |
+    You are a test automation engineer. Evaluate the testing strategy and coverage for the target code.
+
+    ## Review Scope
+    [Insert contents of .full-review/00-scope.md]
+
+    ## Prior Phase Context
+    [Insert security and performance findings from .full-review/02-security-performance.md that affect testing requirements]
+
+    ## Instructions
+    Analyze:
+    1. **Test coverage**: Which code paths have tests? Which critical paths are untested?
+    2. **Test quality**: Are tests testing behavior or implementation? Assertion quality?
+    3. **Test pyramid adherence**: Unit vs integration vs E2E test ratio
+    4. **Edge cases**: Are boundary conditions, error paths, and concurrent scenarios tested?
+    5. **Test maintainability**: Test isolation, mock usage, flaky test indicators
+    6. **Security test gaps**: Are security-critical paths tested? Auth, input validation, etc.
+    7. **Performance test gaps**: Are performance-critical paths tested? Load testing?
+
+    For each finding, provide:
+    - Severity (Critical / High / Medium / Low)
+    - What is untested or poorly tested
+    - Specific test recommendations with example test code
+
+    Write your findings as a structured markdown document.
+```
+
+### Step 3B: Documentation & API Review
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Documentation review for $ARGUMENTS"
+  prompt: |
+    You are a technical documentation architect. Review documentation completeness and accuracy.
+
+    ## Review Scope
+    [Insert contents of .full-review/00-scope.md]
+
+    ## Prior Phase Context
+    [Insert key findings from .full-review/01-quality-architecture.md and .full-review/02-security-performance.md]
+
+    ## Instructions
+    Evaluate:
+    1. **Inline documentation**: Are complex algorithms and business logic explained?
+    2. **API documentation**: Are endpoints documented with examples? Request/response schemas?
+    3. **Architecture documentation**: ADRs, system diagrams, component documentation
+    4. **README completeness**: Setup instructions, development workflow, deployment guide
+    5. **Accuracy**: Does documentation match the actual implementation?
+    6. **Changelog/migration guides**: Are breaking changes documented?
+
+    For each finding, provide:
+    - Severity (Critical / High / Medium / Low)
+    - What is missing or inaccurate
+    - Specific documentation recommendation
+
+    Write your findings as a structured markdown document.
+```
+
+After both complete, consolidate into `.full-review/03-testing-documentation.md`:
+
+```markdown
+# Phase 3: Testing & Documentation Review
+
+## Test Coverage Findings
+
+[Summary from 3A, organized by severity]
+
+## Documentation Findings
+
+[Summary from 3B, organized by severity]
+```
+
+Update `state.json`: set `current_step` to 4, `current_phase` to 4, add steps 3A and 3B to `completed_steps`.
+
+---
+
+## Phase 4: Best Practices & Standards (Steps 4A-4B)
+
+Read all previous `.full-review/*.md` files for full context.
+
+Run both agents in parallel using multiple Task tool calls in a single response.
+
+### Step 4A: Framework & Language Best Practices
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Framework best practices review for $ARGUMENTS"
+  prompt: |
+    You are an expert in modern framework and language best practices. Verify adherence to current standards.
+
+    ## Review Scope
+    [Insert contents of .full-review/00-scope.md]
+
+    ## All Prior Findings
+    [Insert a concise summary of critical/high findings from all prior phases]
+
+    ## Instructions
+    Check for:
+    1. **Language idioms**: Is the code idiomatic for its language? Modern syntax and features?
+    2. **Framework patterns**: Does it follow the framework's recommended patterns? (e.g., React hooks, Django views, Spring beans)
+    3. **Deprecated APIs**: Are any deprecated functions/libraries/patterns used?
+    4. **Modernization opportunities**: Where could modern language/framework features simplify code?
+    5. **Package management**: Are dependencies up-to-date? Unnecessary dependencies?
+    6. **Build configuration**: Is the build optimized? Development vs production settings?
+
+    For each finding, provide:
+    - Severity (Critical / High / Medium / Low)
+    - Current pattern vs recommended pattern
+    - Migration/fix recommendation with code example
+
+    Write your findings as a structured markdown document.
+```
+
+### Step 4B: CI/CD & DevOps Practices Review
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "CI/CD and DevOps practices review for $ARGUMENTS"
+  prompt: |
+    You are a DevOps engineer. Review CI/CD pipeline and operational practices.
+
+    ## Review Scope
+    [Insert contents of .full-review/00-scope.md]
+
+    ## Critical Issues from Prior Phases
+    [Insert critical/high findings from all prior phases that impact deployment or operations]
+
+    ## Instructions
+    Evaluate:
+    1. **CI/CD pipeline**: Build automation, test gates, deployment stages, security scanning
+    2. **Deployment strategy**: Blue-green, canary, rollback capabilities
+    3. **Infrastructure as Code**: Are infrastructure configs version-controlled and reviewed?
+    4. **Monitoring & observability**: Logging, metrics, alerting, dashboards
+    5. **Incident response**: Runbooks, on-call procedures, rollback plans
+    6. **Environment management**: Config separation, secret management, parity between environments
+
+    For each finding, provide:
+    - Severity (Critical / High / Medium / Low)
+    - Operational risk assessment
+    - Specific improvement recommendation
+
+    Write your findings as a structured markdown document.
+```
+
+After both complete, consolidate into `.full-review/04-best-practices.md`:
+
+```markdown
+# Phase 4: Best Practices & Standards
+
+## Framework & Language Findings
+
+[Summary from 4A, organized by severity]
+
+## CI/CD & DevOps Findings
+
+[Summary from 4B, organized by severity]
+```
+
+Update `state.json`: set `current_step` to 5, `current_phase` to 5, add steps 4A and 4B to `completed_steps`.
+
+---
+
+## Phase 5: Consolidated Report (Step 5)
+
+Read all `.full-review/*.md` files. Generate the final consolidated report.
+
+**Output file:** `.full-review/05-final-report.md`
+
+```markdown
+# Comprehensive Code Review Report
+
+## Review Target
+
+[From 00-scope.md]
+
+## Executive Summary
+
+[2-3 sentence overview of overall code health and key concerns]
+
+## Findings by Priority
+
+### Critical Issues (P0 -- Must Fix Immediately)
+
+[All Critical findings from all phases, with source phase reference]
 
 - Security vulnerabilities with CVSS > 7.0
 - Data loss or corruption risks
 - Authentication/authorization bypasses
 - Production stability threats
-- Compliance violations (GDPR, PCI DSS, SOC2)
 
-### High Priority (P1 - Fix Before Next Release)
+### High Priority (P1 -- Fix Before Next Release)
+
+[All High findings from all phases]
 
 - Performance bottlenecks impacting user experience
 - Missing critical test coverage
 - Architectural anti-patterns causing technical debt
 - Outdated dependencies with known vulnerabilities
-- Code quality issues affecting maintainability
 
-### Medium Priority (P2 - Plan for Next Sprint)
+### Medium Priority (P2 -- Plan for Next Sprint)
+
+[All Medium findings from all phases]
 
 - Non-critical performance optimizations
-- Documentation gaps and inconsistencies
+- Documentation gaps
 - Code refactoring opportunities
 - Test quality improvements
-- DevOps automation enhancements
 
-### Low Priority (P3 - Track in Backlog)
+### Low Priority (P3 -- Track in Backlog)
+
+[All Low findings from all phases]
 
 - Style guide violations
 - Minor code smell issues
-- Nice-to-have documentation updates
-- Cosmetic improvements
+- Nice-to-have improvements
 
-## Success Criteria
+## Findings by Category
 
-Review is considered successful when:
+- **Code Quality**: [count] findings ([breakdown by severity])
+- **Architecture**: [count] findings ([breakdown by severity])
+- **Security**: [count] findings ([breakdown by severity])
+- **Performance**: [count] findings ([breakdown by severity])
+- **Testing**: [count] findings ([breakdown by severity])
+- **Documentation**: [count] findings ([breakdown by severity])
+- **Best Practices**: [count] findings ([breakdown by severity])
+- **CI/CD & DevOps**: [count] findings ([breakdown by severity])
 
-- All critical security vulnerabilities are identified and documented
-- Performance bottlenecks are profiled with remediation paths
-- Test coverage gaps are mapped with priority recommendations
-- Architecture risks are assessed with mitigation strategies
-- Documentation reflects actual implementation state
-- Framework best practices compliance is verified
-- CI/CD pipeline supports safe deployment of reviewed code
-- Clear, actionable feedback is provided for all findings
-- Metrics dashboard shows improvement trends
-- Team has clear prioritized action plan for remediation
+## Recommended Action Plan
 
-Target: $ARGUMENTS
+1. [Ordered list of recommended actions, starting with critical/high items]
+2. [Group related fixes where possible]
+3. [Estimate relative effort: small/medium/large]
+
+## Review Metadata
+
+- Review date: [timestamp]
+- Phases completed: [list]
+- Flags applied: [list active flags]
+```
+
+Update `state.json`: set `status` to `"complete"`, `last_updated` to current timestamp.
+
+---
+
+## Completion
+
+Present the final summary:
+
+```
+Comprehensive code review complete for: $ARGUMENTS
+
+## Review Output Files
+- Scope: .full-review/00-scope.md
+- Quality & Architecture: .full-review/01-quality-architecture.md
+- Security & Performance: .full-review/02-security-performance.md
+- Testing & Documentation: .full-review/03-testing-documentation.md
+- Best Practices: .full-review/04-best-practices.md
+- Final Report: .full-review/05-final-report.md
+
+## Summary
+- Total findings: [count]
+- Critical: [X] | High: [Y] | Medium: [Z] | Low: [W]
+
+## Next Steps
+1. Review the full report at .full-review/05-final-report.md
+2. Address Critical (P0) issues immediately
+3. Plan High (P1) fixes for current sprint
+4. Add Medium (P2) and Low (P3) items to backlog
+```

plugins/conductor/.claude-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "conductor",
-  "version": "1.2.0",
+  "version": "1.2.1",
   "description": "Context-Driven Development plugin that transforms Claude Code into a project management tool with structured workflow: Context → Spec & Plan → Implement",
   "author": {
     "name": "Seth Hobson",

plugins/conductor/skills/context-driven-development/SKILL.md

@@ -1,6 +1,12 @@
 ---
 name: context-driven-development
-description: Use this skill when working with Conductor's context-driven development methodology, managing project context artifacts, or understanding the relationship between product.md, tech-stack.md, and workflow.md files.
+description: >-
+  Creates and maintains project context artifacts (product.md, tech-stack.md, workflow.md, tracks.md)
+  in a `conductor/` directory. Scaffolds new projects from scratch, extracts context from existing
+  codebases, validates artifact consistency before implementation, and synchronizes documents as the
+  project evolves. Use when setting up a project, creating or updating product docs, managing a tech
+  stack file, defining development workflows, tracking work units, onboarding to an existing codebase,
+  or running project scaffolding.
 version: 1.0.0
 ---
 
@@ -133,6 +139,8 @@ Update when:
 - Track status changes
 - Tracks are completed or archived
 
+See [references/artifact-templates.md](references/artifact-templates.md) for copy-paste starter templates.
+
 ## Context Maintenance Principles
 
 ### Keep Artifacts Synchronized

plugins/conductor/skills/context-driven-development/references/artifact-templates.md

@@ -0,0 +1,154 @@
+# Artifact Templates
+
+Starter templates for each Conductor context artifact. Copy and fill in for new projects.
+
+> Contributed by [@fernandezbaptiste](https://github.com/fernandezbaptiste) ([#437](https://github.com/wshobson/agents/pull/437))
+
+## product.md
+
+```markdown
+# [Product Name]
+
+> One-line description of what this product does.
+
+## Problem
+
+What problem does this solve and for whom?
+
+## Solution
+
+High-level approach to solving the problem.
+
+## Target Users
+
+| Persona | Needs | Pain Points |
+|---|---|---|
+| Persona 1 | What they need | What frustrates them |
+
+## Core Features
+
+| Feature | Status | Description |
+|---|---|---|
+| Feature A | planned | What it does |
+| Feature B | implemented | What it does |
+
+## Success Metrics
+
+| Metric | Target | Current |
+|---|---|---|
+| Metric 1 | target value | - |
+
+## Roadmap
+
+- **Phase 1**: scope
+- **Phase 2**: scope
+```
+
+## tech-stack.md
+
+```markdown
+# Tech Stack
+
+## Languages & Frameworks
+
+| Technology | Version | Purpose |
+|---|---|---|
+| Python | 3.12 | Backend API |
+| React | 18.x | Frontend UI |
+
+## Key Dependencies
+
+| Package | Version | Rationale |
+|---|---|---|
+| FastAPI | 0.100+ | REST API framework |
+| SQLAlchemy | 2.x | ORM and database access |
+
+## Infrastructure
+
+| Component | Choice | Notes |
+|---|---|---|
+| Hosting | AWS ECS | Production containers |
+| Database | PostgreSQL 16 | Primary data store |
+| CI/CD | GitHub Actions | Build and deploy |
+
+## Dev Tools
+
+| Tool | Purpose | Config |
+|---|---|---|
+| pytest | Testing (target: 80% coverage) | pyproject.toml |
+| ruff | Linting + formatting | ruff.toml |
+```
+
+## workflow.md
+
+```markdown
+# Workflow
+
+## Methodology
+
+TDD with trunk-based development.
+
+## Git Conventions
+
+- **Branch naming**: `feature/<track-id>-description`
+- **Commit format**: `type(scope): message`
+- **PR requirements**: 1 approval, all checks green
+
+## Quality Gates
+
+| Gate | Requirement |
+|---|---|
+| Tests | All pass, coverage >= 80% |
+| Lint | Zero errors |
+| Review | At least 1 approval |
+| Types | No type errors |
+
+## Deployment
+
+1. PR merged to main
+2. CI runs tests + build
+3. Auto-deploy to staging
+4. Manual promotion to production
+```
+
+## tracks.md
+
+```markdown
+# Tracks
+
+## Active
+
+| ID | Title | Status | Priority | Assignee |
+|---|---|---|---|---|
+| TRACK-001 | Feature name | in-progress | high | @person |
+
+## Completed
+
+| ID | Title | Completed |
+|---|---|---|
+| TRACK-000 | Initial setup | 2024-01-15 |
+```
+
+## product-guidelines.md
+
+```markdown
+# Product Guidelines
+
+## Voice & Tone
+
+- Professional but approachable
+- Direct and concise
+- Technical where needed, plain language by default
+
+## Terminology
+
+| Term | Use | Don't Use |
+|---|---|---|
+| workspace | preferred | project, repo |
+| track | preferred | ticket, issue |
+
+## Error Messages
+
+Format: `[Component] What happened. What to do next.`
+Example: `[Auth] Session expired. Please sign in again.`
+```

plugins/content-marketing/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "content-marketing",
+  "version": "1.2.0",
+  "description": "Content marketing strategy, web research, and information synthesis for marketing operations",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/context-management/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "context-management",
+  "version": "1.2.0",
+  "description": "Context persistence, restoration, and long-running conversation management",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/customer-sales-automation/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "customer-sales-automation",
+  "version": "1.2.0",
+  "description": "Customer support workflow automation, sales pipeline management, email campaigns, and CRM integration",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/data-engineering/.claude-plugin/plugin.json

@@ -0,0 +1,10 @@
+{
+  "name": "data-engineering",
+  "version": "1.3.1",
+  "description": "ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development",
+  "author": {
+    "name": "Seth Hobson",
+    "email": "seth@major7apps.com"
+  },
+  "license": "MIT"
+}

plugins/data-engineering/agents/backend-architect.md

@@ -44,7 +44,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Service boundaries**: Domain-Driven Design, bounded contexts, service decomposition
 - **Service communication**: Synchronous (REST, gRPC), asynchronous (message queues, events)
 - **Service discovery**: Consul, etcd, Eureka, Kubernetes service discovery
-- **API Gateway**: Kong, Ambassador, AWS API Gateway, Azure API Management
+- **API Gateway**: Kong, Ambassador, AWS API Gateway, Azure API Management, OCI API Gateway
 - **Service mesh**: Istio, Linkerd, traffic management, observability, security
 - **Backend-for-Frontend (BFF)**: Client-specific backends, API aggregation
 - **Strangler pattern**: Gradual migration, legacy system integration
@@ -54,8 +54,8 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 
 ### Event-Driven Architecture
 
-- **Message queues**: RabbitMQ, AWS SQS, Azure Service Bus, Google Pub/Sub
-- **Event streaming**: Kafka, AWS Kinesis, Azure Event Hubs, NATS
+- **Message queues**: RabbitMQ, AWS SQS, Azure Service Bus, Google Pub/Sub, OCI Queue
+- **Event streaming**: Kafka, AWS Kinesis, Azure Event Hubs, Google Pub/Sub, OCI Streaming, NATS
 - **Pub/Sub patterns**: Topic-based, content-based filtering, fan-out
 - **Event sourcing**: Event store, event replay, snapshots, projections
 - **Event-driven microservices**: Event choreography, event collaboration
@@ -86,10 +86,10 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **CSRF protection**: Token-based, SameSite cookies, double-submit patterns
 - **SQL injection prevention**: Parameterized queries, ORM usage, input validation
 - **API security**: API keys, OAuth scopes, request signing, encryption
-- **Secrets management**: Vault, AWS Secrets Manager, environment variables
+- **Secrets management**: Vault, AWS Secrets Manager, Azure Key Vault, OCI Vault, environment variables
 - **Content Security Policy**: Headers, XSS prevention, frame protection
 - **API throttling**: Quota management, burst limits, backpressure
-- **DDoS protection**: CloudFlare, AWS Shield, rate limiting, IP blocking
+- **DDoS protection**: CloudFlare, AWS Shield, Azure DDoS Protection, OCI WAF, rate limiting, IP blocking
 
 ### Resilience & Fault Tolerance
 
@@ -168,7 +168,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 ### API Gateway & Load Balancing
 
 - **Gateway patterns**: Authentication, rate limiting, request routing, transformation
-- **Gateway technologies**: Kong, Traefik, Envoy, AWS API Gateway, NGINX
+- **Gateway technologies**: Kong, Traefik, Envoy, AWS API Gateway, Azure API Management, OCI API Gateway, NGINX
 - **Load balancing**: Round-robin, least connections, consistent hashing, health-aware
 - **Service routing**: Path-based, header-based, weighted routing, A/B testing
 - **Traffic management**: Canary deployments, blue-green, traffic splitting

plugins/data-engineering/agents/data-engineer.md

@@ -16,7 +16,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 
 - Data lakehouse architectures with Delta Lake, Apache Iceberg, and Apache Hudi
 - Cloud data warehouses: Snowflake, BigQuery, Redshift, Databricks SQL
-- Data lakes: AWS S3, Azure Data Lake, Google Cloud Storage with structured organization
+- Data lakes: AWS S3, Azure Data Lake, Google Cloud Storage, OCI Object Storage with structured organization
 - Modern data stack integration: Fivetran/Airbyte + dbt + Snowflake/BigQuery + BI tools
 - Data mesh architectures with domain-driven data ownership
 - Real-time analytics with Apache Pinot, ClickHouse, Apache Druid
@@ -28,7 +28,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - dbt Core/Cloud for data transformations with version control and testing
 - Apache Airflow for complex workflow orchestration and dependency management
 - Databricks for unified analytics platform with collaborative notebooks
-- AWS Glue, Azure Synapse Analytics, Google Dataflow for cloud ETL
+- AWS Glue, Azure Synapse Analytics, Google Dataflow, OCI Data Integration/Data Flow for cloud ETL
 - Custom Python/Scala data processing with pandas, Polars, Ray
 - Data validation and quality monitoring with Great Expectations
 - Data profiling and discovery with Apache Atlas, DataHub, Amundsen
@@ -38,7 +38,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Apache Kafka and Confluent Platform for event streaming
 - Apache Pulsar for geo-replicated messaging and multi-tenancy
 - Apache Flink and Kafka Streams for complex event processing
-- AWS Kinesis, Azure Event Hubs, Google Pub/Sub for cloud streaming
+- AWS Kinesis, Azure Event Hubs, Google Pub/Sub, OCI Streaming for cloud streaming
 - Real-time data pipelines with change data capture (CDC)
 - Stream processing with windowing, aggregations, and joins
 - Event-driven architectures with schema evolution and compatibility
@@ -49,7 +49,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Apache Airflow with custom operators and dynamic DAG generation
 - Prefect for modern workflow orchestration with dynamic execution
 - Dagster for asset-based data pipeline orchestration
-- Azure Data Factory and AWS Step Functions for cloud workflows
+- Azure Data Factory, AWS Step Functions, and OCI Data Integration/Functions for cloud workflows
 - GitHub Actions and GitLab CI/CD for data pipeline automation
 - Kubernetes CronJobs and Argo Workflows for container-native scheduling
 - Pipeline monitoring, alerting, and failure recovery mechanisms
@@ -101,6 +101,17 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Cloud Dataproc for managed Hadoop and Spark clusters
 - Looker integration for business intelligence
 
+#### OCI Data Engineering Stack
+
+- OCI Object Storage for durable data lake storage
+- OCI Data Flow for serverless Spark processing
+- OCI Data Integration for managed ETL and orchestration
+- OCI Streaming for Kafka-compatible event ingestion
+- Autonomous Data Warehouse and MySQL HeatWave for analytics workloads
+- OCI Data Catalog for metadata discovery and governance
+- OCI GoldenGate for CDC and database replication
+- Oracle Analytics Cloud integration for business intelligence
+
 ### Data Quality & Governance
 
 - Data quality frameworks with Great Expectations and custom validators
@@ -136,7 +147,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 
 ### Infrastructure & DevOps for Data
 
-- Infrastructure as Code with Terraform, CloudFormation, Bicep
+- Infrastructure as Code with Terraform, CloudFormation, Bicep, OCI Resource Manager
 - Containerization with Docker and Kubernetes for data applications
 - CI/CD pipelines for data infrastructure and code deployment
 - Version control strategies for data code, schemas, and configurations

plugins/data-engineering/commands/data-driven-feature.md

@@ -1,176 +1,784 @@
-# Data-Driven Feature Development
+---
+description: "Build features guided by data insights, A/B testing, and continuous measurement"
+argument-hint: "<feature description> [--experiment-type ab|multivariate|bandit] [--confidence 0.90|0.95|0.99]"
+---
 
-Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.
+# Data-Driven Feature Development Orchestrator
 
-[Extended thinking: This workflow orchestrates a comprehensive data-driven development process from initial data analysis and hypothesis formulation through feature implementation with integrated analytics, A/B testing infrastructure, and post-launch analysis. Each phase leverages specialized agents to ensure features are built based on data insights, properly instrumented for measurement, and validated through controlled experiments. The workflow emphasizes modern product analytics practices, statistical rigor in testing, and continuous learning from user behavior.]
+## CRITICAL BEHAVIORAL RULES
 
-## Phase 1: Data Analysis and Hypothesis Formation
+You MUST follow these rules exactly. Violating any of them is a failure.
 
-### 1. Exploratory Data Analysis
+1. **Execute steps in order.** Do NOT skip ahead, reorder, or merge steps.
+2. **Write output files.** Each step MUST produce its output file in `.data-driven-feature/` before the next step begins. Read from prior step files — do NOT rely on context window memory.
+3. **Stop at checkpoints.** When you reach a `PHASE CHECKPOINT`, you MUST stop and wait for explicit user approval before continuing. Use the AskUserQuestion tool with clear options.
+4. **Halt on failure.** If any step fails (agent error, test failure, missing dependency), STOP immediately. Present the error and ask the user how to proceed. Do NOT silently continue.
+5. **Use only local agents.** All `subagent_type` references use agents bundled with this plugin or `general-purpose`. No cross-plugin dependencies.
+6. **Never enter plan mode autonomously.** Do NOT use EnterPlanMode. This command IS the plan — execute it.
 
-- Use Task tool with subagent_type="machine-learning-ops::data-scientist"
-- Prompt: "Perform exploratory data analysis for feature: $ARGUMENTS. Analyze existing user behavior data, identify patterns and opportunities, segment users by behavior, and calculate baseline metrics. Use modern analytics tools (Amplitude, Mixpanel, Segment) to understand current user journeys, conversion funnels, and engagement patterns."
-- Output: EDA report with visualizations, user segments, behavioral patterns, baseline metrics
+## Pre-flight Checks
 
-### 2. Business Hypothesis Development
+Before starting, perform these checks:
 
-- Use Task tool with subagent_type="business-analytics::business-analyst"
-- Context: Data scientist's EDA findings and behavioral patterns
-- Prompt: "Formulate business hypotheses for feature: $ARGUMENTS based on data analysis. Define clear success metrics, expected impact on key business KPIs, target user segments, and minimum detectable effects. Create measurable hypotheses using frameworks like ICE scoring or RICE prioritization."
-- Output: Hypothesis document, success metrics definition, expected ROI calculations
+### 1. Check for existing session
 
-### 3. Statistical Experiment Design
+Check if `.data-driven-feature/state.json` exists:
 
-- Use Task tool with subagent_type="machine-learning-ops::data-scientist"
-- Context: Business hypotheses and success metrics
-- Prompt: "Design statistical experiment for feature: $ARGUMENTS. Calculate required sample size for statistical power, define control and treatment groups, specify randomization strategy, and plan for multiple testing corrections. Consider Bayesian A/B testing approaches for faster decision making. Design for both primary and guardrail metrics."
-- Output: Experiment design document, power analysis, statistical test plan
+- If it exists and `status` is `"in_progress"`: Read it, display the current step, and ask the user:
 
-## Phase 2: Feature Architecture and Analytics Design
+  ```
+  Found an in-progress data-driven feature session:
+  Feature: [name from state]
+  Current step: [step from state]
 
-### 4. Feature Architecture Planning
+  1. Resume from where we left off
+  2. Start fresh (archives existing session)
+  ```
 
-- Use Task tool with subagent_type="data-engineering::backend-architect"
-- Context: Business requirements and experiment design
-- Prompt: "Design feature architecture for: $ARGUMENTS with A/B testing capability. Include feature flag integration (LaunchDarkly, Split.io, or Optimizely), gradual rollout strategy, circuit breakers for safety, and clean separation between control and treatment logic. Ensure architecture supports real-time configuration updates."
-- Output: Architecture diagrams, feature flag schema, rollout strategy
+- If it exists and `status` is `"complete"`: Ask whether to archive and start fresh.
 
-### 5. Analytics Instrumentation Design
+### 2. Initialize state
 
-- Use Task tool with subagent_type="data-engineering::data-engineer"
-- Context: Feature architecture and success metrics
-- Prompt: "Design comprehensive analytics instrumentation for: $ARGUMENTS. Define event schemas for user interactions, specify properties for segmentation and analysis, design funnel tracking and conversion events, plan cohort analysis capabilities. Implement using modern SDKs (Segment, Amplitude, Mixpanel) with proper event taxonomy."
-- Output: Event tracking plan, analytics schema, instrumentation guide
+Create `.data-driven-feature/` directory and `state.json`:
 
-### 6. Data Pipeline Architecture
-
-- Use Task tool with subagent_type="data-engineering::data-engineer"
-- Context: Analytics requirements and existing data infrastructure
-- Prompt: "Design data pipelines for feature: $ARGUMENTS. Include real-time streaming for live metrics (Kafka, Kinesis), batch processing for detailed analysis, data warehouse integration (Snowflake, BigQuery), and feature store for ML if applicable. Ensure proper data governance and GDPR compliance."
-- Output: Pipeline architecture, ETL/ELT specifications, data flow diagrams
-
-## Phase 3: Implementation with Instrumentation
-
-### 7. Backend Implementation
-
-- Use Task tool with subagent_type="backend-development::backend-architect"
-- Context: Architecture design and feature requirements
-- Prompt: "Implement backend for feature: $ARGUMENTS with full instrumentation. Include feature flag checks at decision points, comprehensive event tracking for all user actions, performance metrics collection, error tracking and monitoring. Implement proper logging for experiment analysis."
-- Output: Backend code with analytics, feature flag integration, monitoring setup
-
-### 8. Frontend Implementation
-
-- Use Task tool with subagent_type="frontend-mobile-development::frontend-developer"
-- Context: Backend APIs and analytics requirements
-- Prompt: "Build frontend for feature: $ARGUMENTS with analytics tracking. Implement event tracking for all user interactions, session recording integration if applicable, performance metrics (Core Web Vitals), and proper error boundaries. Ensure consistent experience between control and treatment groups."
-- Output: Frontend code with analytics, A/B test variants, performance monitoring
-
-### 9. ML Model Integration (if applicable)
-
-- Use Task tool with subagent_type="machine-learning-ops::ml-engineer"
-- Context: Feature requirements and data pipelines
-- Prompt: "Integrate ML models for feature: $ARGUMENTS if needed. Implement online inference with low latency, A/B testing between model versions, model performance tracking, and automatic fallback mechanisms. Set up model monitoring for drift detection."
-- Output: ML pipeline, model serving infrastructure, monitoring setup
-
-## Phase 4: Pre-Launch Validation
-
-### 10. Analytics Validation
-
-- Use Task tool with subagent_type="data-engineering::data-engineer"
-- Context: Implemented tracking and event schemas
-- Prompt: "Validate analytics implementation for: $ARGUMENTS. Test all event tracking in staging, verify data quality and completeness, validate funnel definitions, ensure proper user identification and session tracking. Run end-to-end tests for data pipeline."
-- Output: Validation report, data quality metrics, tracking coverage analysis
-
-### 11. Experiment Setup
-
-- Use Task tool with subagent_type="cloud-infrastructure::deployment-engineer"
-- Context: Feature flags and experiment design
-- Prompt: "Configure experiment infrastructure for: $ARGUMENTS. Set up feature flags with proper targeting rules, configure traffic allocation (start with 5-10%), implement kill switches, set up monitoring alerts for key metrics. Test randomization and assignment logic."
-- Output: Experiment configuration, monitoring dashboards, rollout plan
-
-## Phase 5: Launch and Experimentation
-
-### 12. Gradual Rollout
-
-- Use Task tool with subagent_type="cloud-infrastructure::deployment-engineer"
-- Context: Experiment configuration and monitoring setup
-- Prompt: "Execute gradual rollout for feature: $ARGUMENTS. Start with internal dogfooding, then beta users (1-5%), gradually increase to target traffic. Monitor error rates, performance metrics, and early indicators. Implement automated rollback on anomalies."
-- Output: Rollout execution, monitoring alerts, health metrics
-
-### 13. Real-time Monitoring
-
-- Use Task tool with subagent_type="observability-monitoring::observability-engineer"
-- Context: Deployed feature and success metrics
-- Prompt: "Set up comprehensive monitoring for: $ARGUMENTS. Create real-time dashboards for experiment metrics, configure alerts for statistical significance, monitor guardrail metrics for negative impacts, track system performance and error rates. Use tools like Datadog, New Relic, or custom dashboards."
-- Output: Monitoring dashboards, alert configurations, SLO definitions
-
-## Phase 6: Analysis and Decision Making
-
-### 14. Statistical Analysis
-
-- Use Task tool with subagent_type="machine-learning-ops::data-scientist"
-- Context: Experiment data and original hypotheses
-- Prompt: "Analyze A/B test results for: $ARGUMENTS. Calculate statistical significance with confidence intervals, check for segment-level effects, analyze secondary metrics impact, investigate any unexpected patterns. Use both frequentist and Bayesian approaches. Account for multiple testing if applicable."
-- Output: Statistical analysis report, significance tests, segment analysis
-
-### 15. Business Impact Assessment
-
-- Use Task tool with subagent_type="business-analytics::business-analyst"
-- Context: Statistical analysis and business metrics
-- Prompt: "Assess business impact of feature: $ARGUMENTS. Calculate actual vs expected ROI, analyze impact on key business metrics, evaluate cost-benefit including operational overhead, project long-term value. Make recommendation on full rollout, iteration, or rollback."
-- Output: Business impact report, ROI analysis, recommendation document
-
-### 16. Post-Launch Optimization
-
-- Use Task tool with subagent_type="machine-learning-ops::data-scientist"
-- Context: Launch results and user feedback
-- Prompt: "Identify optimization opportunities for: $ARGUMENTS based on data. Analyze user behavior patterns in treatment group, identify friction points in user journey, suggest improvements based on data, plan follow-up experiments. Use cohort analysis for long-term impact."
-- Output: Optimization recommendations, follow-up experiment plans
-
-## Configuration Options
-
-```yaml
-experiment_config:
-  min_sample_size: 10000
-  confidence_level: 0.95
-  runtime_days: 14
-  traffic_allocation: "gradual" # gradual, fixed, or adaptive
-
-analytics_platforms:
-  - amplitude
-  - segment
-  - mixpanel
-
-feature_flags:
-  provider: "launchdarkly" # launchdarkly, split, optimizely, unleash
-
-statistical_methods:
-  - frequentist
-  - bayesian
-
-monitoring:
-  - real_time_metrics: true
-  - anomaly_detection: true
-  - automatic_rollback: true
+```json
+{
+  "feature": "$ARGUMENTS",
+  "status": "in_progress",
+  "experiment_type": "ab",
+  "confidence_level": 0.95,
+  "current_step": 1,
+  "current_phase": 1,
+  "completed_steps": [],
+  "files_created": [],
+  "started_at": "ISO_TIMESTAMP",
+  "last_updated": "ISO_TIMESTAMP"
+}
 ```
 
-## Success Criteria
+Parse `$ARGUMENTS` for `--experiment-type` and `--confidence` flags. Use defaults if not specified.
 
-- **Data Coverage**: 100% of user interactions tracked with proper event schema
-- **Experiment Validity**: Proper randomization, sufficient statistical power, no sample ratio mismatch
-- **Statistical Rigor**: Clear significance testing, proper confidence intervals, multiple testing corrections
-- **Business Impact**: Measurable improvement in target metrics without degrading guardrail metrics
-- **Technical Performance**: No degradation in p95 latency, error rates below 0.1%
-- **Decision Speed**: Clear go/no-go decision within planned experiment runtime
-- **Learning Outcomes**: Documented insights for future feature development
+### 3. Parse feature description
 
-## Coordination Notes
+Extract the feature description from `$ARGUMENTS` (everything before the flags). This is referenced as `$FEATURE` in prompts below.
 
-- Data scientists and business analysts collaborate on hypothesis formation
-- Engineers implement with analytics as first-class requirement, not afterthought
-- Feature flags enable safe experimentation without full deployments
-- Real-time monitoring allows for quick iteration and rollback if needed
-- Statistical rigor balanced with business practicality and speed to market
-- Continuous learning loop feeds back into next feature development cycle
+---
 
-Feature to develop with data-driven approach: $ARGUMENTS
+## Phase 1: Data Analysis & Hypothesis (Steps 1–3) — Interactive
+
+### Step 1: Exploratory Data Analysis
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Perform exploratory data analysis for $FEATURE"
+  prompt: |
+    You are a data scientist specializing in product analytics. Perform exploratory data analysis for feature: $FEATURE.
+
+    ## Instructions
+    1. Analyze existing user behavior data, identify patterns and opportunities
+    2. Segment users by behavior and engagement patterns
+    3. Calculate baseline metrics for key indicators
+    4. Use modern analytics tools (Amplitude, Mixpanel, Segment) to understand current user journeys, conversion funnels, and engagement patterns
+    5. Identify data quality issues or gaps that need addressing
+
+    Provide an EDA report with user segments, behavioral patterns, and baseline metrics.
+```
+
+Save the agent's output to `.data-driven-feature/01-eda-report.md`.
+
+Update `state.json`: set `current_step` to 2, add `"01-eda-report.md"` to `files_created`, add step 1 to `completed_steps`.
+
+### Step 2: Business Hypothesis Development
+
+Read `.data-driven-feature/01-eda-report.md` to load EDA context.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Formulate business hypotheses for $FEATURE"
+  prompt: |
+    You are a business analyst specializing in data-driven product development. Formulate business hypotheses for feature: $FEATURE based on the data analysis below.
+
+    ## EDA Findings
+    [Insert full contents of .data-driven-feature/01-eda-report.md]
+
+    ## Instructions
+    1. Define clear success metrics and expected impact on key business KPIs
+    2. Identify target user segments and minimum detectable effects
+    3. Create measurable hypotheses using ICE or RICE prioritization frameworks
+    4. Calculate expected ROI and business value
+
+    Provide a hypothesis document with success metrics definition and expected ROI calculations.
+```
+
+Save the agent's output to `.data-driven-feature/02-hypotheses.md`.
+
+Update `state.json`: set `current_step` to 3, add step 2 to `completed_steps`.
+
+### Step 3: Statistical Experiment Design
+
+Read `.data-driven-feature/02-hypotheses.md` to load hypothesis context.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Design statistical experiment for $FEATURE"
+  prompt: |
+    You are a data scientist specializing in experimentation and statistical analysis. Design the statistical experiment for feature: $FEATURE.
+
+    ## Business Hypotheses
+    [Insert full contents of .data-driven-feature/02-hypotheses.md]
+
+    ## Experiment Type: [from state.json]
+    ## Confidence Level: [from state.json]
+
+    ## Instructions
+    1. Calculate required sample size for statistical power
+    2. Define control and treatment groups with randomization strategy
+    3. Plan for multiple testing corrections if needed
+    4. Consider Bayesian A/B testing approaches for faster decision making
+    5. Design for both primary and guardrail metrics
+    6. Specify experiment runtime and stopping rules
+
+    Provide an experiment design document with power analysis and statistical test plan.
+```
+
+Save the agent's output to `.data-driven-feature/03-experiment-design.md`.
+
+Update `state.json`: set `current_step` to "checkpoint-1", add step 3 to `completed_steps`.
+
+---
+
+## PHASE CHECKPOINT 1 — User Approval Required
+
+You MUST stop here and present the analysis and experiment design for review.
+
+Display a summary of the hypotheses from `.data-driven-feature/02-hypotheses.md` and experiment design from `.data-driven-feature/03-experiment-design.md` (key metrics, target segments, sample size, experiment type) and ask:
+
+```
+Data analysis and experiment design complete. Please review:
+- .data-driven-feature/01-eda-report.md
+- .data-driven-feature/02-hypotheses.md
+- .data-driven-feature/03-experiment-design.md
+
+1. Approve — proceed to architecture and implementation
+2. Request changes — tell me what to adjust
+3. Pause — save progress and stop here
+```
+
+Do NOT proceed to Phase 2 until the user selects option 1. If they select option 2, revise and re-checkpoint. If option 3, update `state.json` status and stop.
+
+---
+
+## Phase 2: Architecture & Instrumentation (Steps 4–6)
+
+### Step 4: Feature Architecture Planning
+
+Read `.data-driven-feature/02-hypotheses.md` and `.data-driven-feature/03-experiment-design.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "backend-architect"
+  description: "Design feature architecture for $FEATURE with A/B testing capability"
+  prompt: |
+    Design the feature architecture for: $FEATURE with A/B testing capability.
+
+    ## Business Hypotheses
+    [Insert contents of .data-driven-feature/02-hypotheses.md]
+
+    ## Experiment Design
+    [Insert contents of .data-driven-feature/03-experiment-design.md]
+
+    ## Instructions
+    1. Include feature flag integration (LaunchDarkly, Split.io, or Optimizely)
+    2. Design gradual rollout strategy with circuit breakers for safety
+    3. Ensure clean separation between control and treatment logic
+    4. Support real-time configuration updates
+    5. Design for proper data collection at each decision point
+
+    Provide architecture diagrams, feature flag schema, and rollout strategy.
+```
+
+Save the agent's output to `.data-driven-feature/04-architecture.md`.
+
+Update `state.json`: set `current_step` to 5, add step 4 to `completed_steps`.
+
+### Step 5: Analytics Instrumentation Design
+
+Read `.data-driven-feature/04-architecture.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "data-engineer"
+  description: "Design analytics instrumentation for $FEATURE"
+  prompt: |
+    Design comprehensive analytics instrumentation for: $FEATURE.
+
+    ## Architecture
+    [Insert contents of .data-driven-feature/04-architecture.md]
+
+    ## Experiment Design
+    [Insert contents of .data-driven-feature/03-experiment-design.md]
+
+    ## Instructions
+    1. Define event schemas for user interactions with proper taxonomy
+    2. Specify properties for segmentation and analysis
+    3. Design funnel tracking and conversion events
+    4. Plan cohort analysis capabilities
+    5. Implement using modern SDKs (Segment, Amplitude, Mixpanel) with proper event taxonomy
+
+    Provide an event tracking plan, analytics schema, and instrumentation guide.
+```
+
+Save the agent's output to `.data-driven-feature/05-analytics-design.md`.
+
+Update `state.json`: set `current_step` to 6, add step 5 to `completed_steps`.
+
+### Step 6: Data Pipeline Architecture
+
+Read `.data-driven-feature/05-analytics-design.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "data-engineer"
+  description: "Design data pipelines for $FEATURE"
+  prompt: |
+    Design data pipelines for feature: $FEATURE.
+
+    ## Analytics Design
+    [Insert contents of .data-driven-feature/05-analytics-design.md]
+
+    ## Architecture
+    [Insert contents of .data-driven-feature/04-architecture.md]
+
+    ## Instructions
+    1. Include real-time streaming for live metrics (Kafka, Kinesis)
+    2. Design batch processing for detailed analysis
+    3. Plan data warehouse integration (Snowflake, BigQuery)
+    4. Include feature store for ML if applicable
+    5. Ensure proper data governance and GDPR compliance
+    6. Define data retention and archival policies
+
+    Provide pipeline architecture, ETL/ELT specifications, and data flow diagrams.
+```
+
+Save the agent's output to `.data-driven-feature/06-data-pipelines.md`.
+
+Update `state.json`: set `current_step` to "checkpoint-2", add step 6 to `completed_steps`.
+
+---
+
+## PHASE CHECKPOINT 2 — User Approval Required
+
+Display a summary of the architecture, analytics design, and data pipelines and ask:
+
+```
+Architecture and instrumentation design complete. Please review:
+- .data-driven-feature/04-architecture.md
+- .data-driven-feature/05-analytics-design.md
+- .data-driven-feature/06-data-pipelines.md
+
+1. Approve — proceed to implementation
+2. Request changes — tell me what to adjust
+3. Pause — save progress and stop here
+```
+
+Do NOT proceed to Phase 3 until the user approves.
+
+---
+
+## Phase 3: Implementation (Steps 7–9)
+
+### Step 7: Backend Implementation
+
+Read `.data-driven-feature/04-architecture.md` and `.data-driven-feature/05-analytics-design.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "backend-architect"
+  description: "Implement backend for $FEATURE with full instrumentation"
+  prompt: |
+    Implement the backend for feature: $FEATURE with full instrumentation.
+
+    ## Architecture
+    [Insert contents of .data-driven-feature/04-architecture.md]
+
+    ## Analytics Design
+    [Insert contents of .data-driven-feature/05-analytics-design.md]
+
+    ## Instructions
+    1. Include feature flag checks at decision points
+    2. Implement comprehensive event tracking for all user actions
+    3. Add performance metrics collection
+    4. Implement error tracking and monitoring
+    5. Add proper logging for experiment analysis
+    6. Follow the project's existing code patterns and conventions
+
+    Write all code files. Report what files were created/modified.
+```
+
+Save a summary to `.data-driven-feature/07-backend.md`.
+
+Update `state.json`: set `current_step` to 8, add step 7 to `completed_steps`.
+
+### Step 8: Frontend Implementation
+
+Read `.data-driven-feature/04-architecture.md`, `.data-driven-feature/05-analytics-design.md`, and `.data-driven-feature/07-backend.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Implement frontend for $FEATURE with analytics tracking"
+  prompt: |
+    You are a frontend developer. Build the frontend for feature: $FEATURE with analytics tracking.
+
+    ## Architecture
+    [Insert contents of .data-driven-feature/04-architecture.md]
+
+    ## Analytics Design
+    [Insert contents of .data-driven-feature/05-analytics-design.md]
+
+    ## Backend Implementation
+    [Insert contents of .data-driven-feature/07-backend.md]
+
+    ## Instructions
+    1. Implement event tracking for all user interactions
+    2. Build A/B test variants with proper variant assignment
+    3. Add session recording integration if applicable
+    4. Track performance metrics (Core Web Vitals)
+    5. Add proper error boundaries
+    6. Ensure consistent experience between control and treatment groups
+    7. Follow the project's existing frontend patterns and conventions
+
+    Write all code files. Report what files were created/modified.
+```
+
+Save a summary to `.data-driven-feature/08-frontend.md`.
+
+**Note:** If the feature has no frontend component (pure backend/API/pipeline), skip this step — write a brief note in `08-frontend.md` explaining why it was skipped, and continue.
+
+Update `state.json`: set `current_step` to 9, add step 8 to `completed_steps`.
+
+### Step 9: ML Model Integration (if applicable)
+
+Read `.data-driven-feature/04-architecture.md` and `.data-driven-feature/06-data-pipelines.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Integrate ML models for $FEATURE"
+  prompt: |
+    You are an ML engineer. Integrate ML models for feature: $FEATURE if needed.
+
+    ## Architecture
+    [Insert contents of .data-driven-feature/04-architecture.md]
+
+    ## Data Pipelines
+    [Insert contents of .data-driven-feature/06-data-pipelines.md]
+
+    ## Instructions
+    1. Implement online inference with low latency
+    2. Set up A/B testing between model versions
+    3. Add model performance tracking and drift detection
+    4. Implement automatic fallback mechanisms
+    5. Set up model monitoring dashboards
+
+    If no ML component is needed for this feature, explain why and skip.
+    Write all code files. Report what files were created/modified.
+```
+
+Save a summary to `.data-driven-feature/09-ml-integration.md`.
+
+Update `state.json`: set `current_step` to "checkpoint-3", add step 9 to `completed_steps`.
+
+---
+
+## PHASE CHECKPOINT 3 — User Approval Required
+
+Display a summary of the implementation and ask:
+
+```
+Implementation complete. Please review:
+- .data-driven-feature/07-backend.md
+- .data-driven-feature/08-frontend.md
+- .data-driven-feature/09-ml-integration.md
+
+1. Approve — proceed to validation and launch
+2. Request changes — tell me what to fix
+3. Pause — save progress and stop here
+```
+
+Do NOT proceed to Phase 4 until the user approves.
+
+---
+
+## Phase 4: Validation & Launch (Steps 10–13)
+
+### Step 10: Analytics Validation
+
+Read `.data-driven-feature/05-analytics-design.md`, `.data-driven-feature/07-backend.md`, and `.data-driven-feature/08-frontend.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "data-engineer"
+  description: "Validate analytics implementation for $FEATURE"
+  prompt: |
+    Validate the analytics implementation for: $FEATURE.
+
+    ## Analytics Design
+    [Insert contents of .data-driven-feature/05-analytics-design.md]
+
+    ## Backend Implementation
+    [Insert contents of .data-driven-feature/07-backend.md]
+
+    ## Frontend Implementation
+    [Insert contents of .data-driven-feature/08-frontend.md]
+
+    ## Instructions
+    1. Test all event tracking in staging environment
+    2. Verify data quality and completeness
+    3. Validate funnel definitions and conversion tracking
+    4. Ensure proper user identification and session tracking
+    5. Run end-to-end tests for data pipeline
+    6. Check for tracking gaps or inconsistencies
+
+    Provide a validation report with data quality metrics and tracking coverage analysis.
+```
+
+Save the agent's output to `.data-driven-feature/10-analytics-validation.md`.
+
+Update `state.json`: set `current_step` to 11, add step 10 to `completed_steps`.
+
+### Step 11: Experiment Setup & Deployment
+
+Read `.data-driven-feature/03-experiment-design.md` and `.data-driven-feature/04-architecture.md`.
+
+Launch two agents in parallel using multiple Task tool calls in a single response:
+
+**11a. Experiment Infrastructure:**
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Configure experiment infrastructure for $FEATURE"
+  prompt: |
+    You are a deployment engineer specializing in experimentation platforms. Configure experiment infrastructure for: $FEATURE.
+
+    ## Experiment Design
+    [Insert contents of .data-driven-feature/03-experiment-design.md]
+
+    ## Architecture
+    [Insert contents of .data-driven-feature/04-architecture.md]
+
+    ## Instructions
+    1. Set up feature flags with proper targeting rules
+    2. Configure traffic allocation (start with 5-10%)
+    3. Implement kill switches for safety
+    4. Set up monitoring alerts for key metrics
+    5. Test randomization and assignment logic
+    6. Create rollback procedures
+
+    Provide experiment configuration, monitoring dashboards, and rollout plan.
+```
+
+**11b. Monitoring Setup:**
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Set up monitoring for $FEATURE experiment"
+  prompt: |
+    You are an observability engineer. Set up comprehensive monitoring for: $FEATURE.
+
+    ## Architecture
+    [Insert contents of .data-driven-feature/04-architecture.md]
+
+    ## Experiment Design
+    [Insert contents of .data-driven-feature/03-experiment-design.md]
+
+    ## Analytics Design
+    [Insert contents of .data-driven-feature/05-analytics-design.md]
+
+    ## Instructions
+    1. Create real-time dashboards for experiment metrics
+    2. Configure alerts for statistical significance milestones
+    3. Monitor guardrail metrics for negative impacts
+    4. Track system performance and error rates
+    5. Define SLOs for the experiment period
+    6. Use tools like Datadog, New Relic, or custom dashboards
+
+    Provide monitoring dashboard configs, alert definitions, and SLO specifications.
+```
+
+After both complete, consolidate results into `.data-driven-feature/11-experiment-setup.md`:
+
+```markdown
+# Experiment Setup: $FEATURE
+
+## Experiment Infrastructure
+
+[Summary from 11a — feature flags, traffic allocation, rollback plan]
+
+## Monitoring Configuration
+
+[Summary from 11b — dashboards, alerts, SLOs]
+```
+
+Update `state.json`: set `current_step` to 12, add step 11 to `completed_steps`.
+
+### Step 12: Gradual Rollout
+
+Read `.data-driven-feature/11-experiment-setup.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Create gradual rollout plan for $FEATURE"
+  prompt: |
+    You are a deployment engineer. Create a detailed gradual rollout plan for feature: $FEATURE.
+
+    ## Experiment Setup
+    [Insert contents of .data-driven-feature/11-experiment-setup.md]
+
+    ## Instructions
+    1. Define rollout stages: internal dogfooding → beta (1-5%) → gradual increase to target traffic
+    2. Specify health checks and go/no-go criteria for each stage
+    3. Define monitoring checkpoints and metrics thresholds
+    4. Create automated rollback triggers for anomalies
+    5. Document manual rollback procedures
+
+    Provide a stage-by-stage rollout plan with decision criteria.
+```
+
+Save the agent's output to `.data-driven-feature/12-rollout-plan.md`.
+
+Update `state.json`: set `current_step` to 13, add step 12 to `completed_steps`.
+
+### Step 13: Security Review
+
+Read `.data-driven-feature/04-architecture.md`, `.data-driven-feature/07-backend.md`, and `.data-driven-feature/08-frontend.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Security review of $FEATURE"
+  prompt: |
+    You are a security auditor. Perform a security review of this data-driven feature implementation.
+
+    ## Architecture
+    [Insert contents of .data-driven-feature/04-architecture.md]
+
+    ## Backend Implementation
+    [Insert contents of .data-driven-feature/07-backend.md]
+
+    ## Frontend Implementation
+    [Insert contents of .data-driven-feature/08-frontend.md]
+
+    ## Instructions
+    Review for: OWASP Top 10, data privacy and GDPR compliance, PII handling in analytics events,
+    authentication/authorization flaws, input validation gaps, experiment manipulation risks,
+    and any security anti-patterns.
+
+    Provide findings with severity, location, and specific fix recommendations.
+```
+
+Save the agent's output to `.data-driven-feature/13-security-review.md`.
+
+If there are Critical or High severity findings, address them now before proceeding. Apply fixes and re-validate.
+
+Update `state.json`: set `current_step` to "checkpoint-4", add step 13 to `completed_steps`.
+
+---
+
+## PHASE CHECKPOINT 4 — User Approval Required
+
+Display a summary of validation and launch readiness and ask:
+
+```
+Validation and launch preparation complete. Please review:
+- .data-driven-feature/10-analytics-validation.md
+- .data-driven-feature/11-experiment-setup.md
+- .data-driven-feature/12-rollout-plan.md
+- .data-driven-feature/13-security-review.md
+
+Security findings: [X critical, Y high, Z medium]
+
+1. Approve — proceed to analysis planning
+2. Request changes — tell me what to fix
+3. Pause — save progress and stop here
+```
+
+Do NOT proceed to Phase 5 until the user approves.
+
+---
+
+## Phase 5: Analysis & Decision (Steps 14–16)
+
+### Step 14: Statistical Analysis
+
+Read `.data-driven-feature/03-experiment-design.md` and `.data-driven-feature/02-hypotheses.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Create statistical analysis plan for $FEATURE experiment"
+  prompt: |
+    You are a data scientist specializing in experimentation. Create the statistical analysis plan for the A/B test results of: $FEATURE.
+
+    ## Experiment Design
+    [Insert contents of .data-driven-feature/03-experiment-design.md]
+
+    ## Hypotheses
+    [Insert contents of .data-driven-feature/02-hypotheses.md]
+
+    ## Instructions
+    1. Define statistical significance calculations with confidence intervals
+    2. Plan segment-level effect analysis
+    3. Specify secondary metrics impact analysis
+    4. Use both frequentist and Bayesian approaches
+    5. Account for multiple testing corrections
+    6. Define stopping rules and decision criteria
+
+    Provide an analysis plan with templates for results reporting.
+```
+
+Save the agent's output to `.data-driven-feature/14-analysis-plan.md`.
+
+Update `state.json`: set `current_step` to 15, add step 14 to `completed_steps`.
+
+### Step 15: Business Impact Assessment Framework
+
+Read `.data-driven-feature/02-hypotheses.md` and `.data-driven-feature/14-analysis-plan.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Create business impact assessment framework for $FEATURE"
+  prompt: |
+    You are a business analyst. Create a business impact assessment framework for feature: $FEATURE.
+
+    ## Hypotheses
+    [Insert contents of .data-driven-feature/02-hypotheses.md]
+
+    ## Analysis Plan
+    [Insert contents of .data-driven-feature/14-analysis-plan.md]
+
+    ## Instructions
+    1. Define actual vs expected ROI calculation methodology
+    2. Create a framework for analyzing impact on key business metrics
+    3. Plan cost-benefit analysis including operational overhead
+    4. Define criteria for full rollout, iteration, or rollback decisions
+    5. Create templates for stakeholder reporting
+
+    Provide a business impact framework and decision matrix.
+```
+
+Save the agent's output to `.data-driven-feature/15-impact-framework.md`.
+
+Update `state.json`: set `current_step` to 16, add step 15 to `completed_steps`.
+
+### Step 16: Optimization Roadmap
+
+Read `.data-driven-feature/14-analysis-plan.md` and `.data-driven-feature/15-impact-framework.md`.
+
+Use the Task tool:
+
+```
+Task:
+  subagent_type: "general-purpose"
+  description: "Create post-launch optimization roadmap for $FEATURE"
+  prompt: |
+    You are a data scientist specializing in product optimization. Create a post-launch optimization roadmap for: $FEATURE.
+
+    ## Analysis Plan
+    [Insert contents of .data-driven-feature/14-analysis-plan.md]
+
+    ## Impact Framework
+    [Insert contents of .data-driven-feature/15-impact-framework.md]
+
+    ## Instructions
+    1. Define user behavior analysis methodology for treatment group
+    2. Plan friction point identification in user journeys
+    3. Suggest improvement hypotheses based on expected data patterns
+    4. Plan follow-up experiments and iteration cycles
+    5. Design cohort analysis for long-term impact assessment
+    6. Create a continuous learning feedback loop
+
+    Provide an optimization roadmap with follow-up experiment plans.
+```
+
+Save the agent's output to `.data-driven-feature/16-optimization-roadmap.md`.
+
+Update `state.json`: set `current_step` to "complete", add step 16 to `completed_steps`.
+
+---
+
+## Completion
+
+Update `state.json`:
+
+- Set `status` to `"complete"`
+- Set `last_updated` to current timestamp
+
+Present the final summary:
+
+```
+Data-driven feature development complete: $FEATURE
+
+## Files Created
+[List all .data-driven-feature/ output files]
+
+## Development Summary
+- EDA Report: .data-driven-feature/01-eda-report.md
+- Hypotheses: .data-driven-feature/02-hypotheses.md
+- Experiment Design: .data-driven-feature/03-experiment-design.md
+- Architecture: .data-driven-feature/04-architecture.md
+- Analytics Design: .data-driven-feature/05-analytics-design.md
+- Data Pipelines: .data-driven-feature/06-data-pipelines.md
+- Backend: .data-driven-feature/07-backend.md
+- Frontend: .data-driven-feature/08-frontend.md
+- ML Integration: .data-driven-feature/09-ml-integration.md
+- Analytics Validation: .data-driven-feature/10-analytics-validation.md
+- Experiment Setup: .data-driven-feature/11-experiment-setup.md
+- Rollout Plan: .data-driven-feature/12-rollout-plan.md
+- Security Review: .data-driven-feature/13-security-review.md
+- Analysis Plan: .data-driven-feature/14-analysis-plan.md
+- Impact Framework: .data-driven-feature/15-impact-framework.md
+- Optimization Roadmap: .data-driven-feature/16-optimization-roadmap.md
+
+## Next Steps
+1. Review all generated artifacts and documentation
+2. Execute the rollout plan in .data-driven-feature/12-rollout-plan.md
+3. Monitor using the dashboards from .data-driven-feature/11-experiment-setup.md
+4. Run analysis after experiment completes using .data-driven-feature/14-analysis-plan.md
+5. Make go/no-go decision using .data-driven-feature/15-impact-framework.md
+```

plugins/data-engineering/skills/airflow-dag-patterns/SKILL.md

@@ -517,9 +517,3 @@ airflow/
 - **Don't use global state** - Tasks should be stateless
 - **Don't skip catchup blindly** - Understand implications
 - **Don't put heavy logic in DAG file** - Import from modules
-
-## Resources
-
-- [Airflow Documentation](https://airflow.apache.org/docs/)
-- [Astronomer Guides](https://docs.astronomer.io/learn)
-- [TaskFlow API](https://airflow.apache.org/docs/apache-airflow/stable/tutorial/taskflow.html)

plugins/data-engineering/skills/data-quality-frameworks/SKILL.md

@@ -581,10 +581,3 @@ if not all(r.passed for r in results.values()):
 - **Don't skip freshness** - Stale data is bad data
 - **Don't hardcode thresholds** - Use dynamic baselines
 - **Don't test in isolation** - Test relationships too
-
-## Resources
-
-- [Great Expectations Documentation](https://docs.greatexpectations.io/)
-- [dbt Testing Documentation](https://docs.getdbt.com/docs/build/tests)
-- [Data Contract Specification](https://datacontract.com/)
-- [Soda Core](https://docs.soda.io/soda-core/overview.html)