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feat: comprehensive upgrade of 32 tools and workflows
Major quality improvements across all tools and workflows: - Expanded from 1,952 to 23,686 lines (12.1x growth) - Added 89 complete code examples with production-ready implementations - Integrated modern 2024/2025 technologies and best practices - Established consistent structure across all files - Added 64 reference workflows with real-world scenarios Phase 1 - Critical Workflows (4 files): - git-workflow: 9→118 lines - Complete git workflow orchestration - legacy-modernize: 10→110 lines - Strangler fig pattern implementation - multi-platform: 10→181 lines - API-first cross-platform development - improve-agent: 13→292 lines - Systematic agent optimization Phase 2 - Unstructured Tools (8 files): - issue: 33→636 lines - GitHub issue resolution expert - prompt-optimize: 49→1,207 lines - Advanced prompt engineering - data-pipeline: 56→2,312 lines - Production-ready pipeline architecture - data-validation: 56→1,674 lines - Comprehensive validation framework - error-analysis: 56→1,154 lines - Modern observability and debugging - langchain-agent: 56→2,735 lines - LangChain 0.1+ with LangGraph - ai-review: 63→1,597 lines - AI-powered code review system - deploy-checklist: 71→1,631 lines - GitOps and progressive delivery Phase 3 - Mid-Length Tools (4 files): - tdd-red: 111→1,763 lines - Property-based testing and decision frameworks - tdd-green: 130→842 lines - Implementation patterns and type-driven development - tdd-refactor: 174→1,860 lines - SOLID examples and architecture refactoring - refactor-clean: 267→886 lines - AI code review and static analysis integration Phase 4 - Short Workflows (7 files): - ml-pipeline: 43→292 lines - MLOps with experiment tracking - smart-fix: 44→834 lines - Intelligent debugging with AI assistance - full-stack-feature: 58→113 lines - API-first full-stack development - security-hardening: 63→118 lines - DevSecOps with zero-trust - data-driven-feature: 70→160 lines - A/B testing and analytics - performance-optimization: 70→111 lines - APM and Core Web Vitals - full-review: 76→124 lines - Multi-phase comprehensive review Phase 5 - Small Files (9 files): - onboard: 24→394 lines - Remote-first onboarding specialist - multi-agent-review: 63→194 lines - Multi-agent orchestration - context-save: 65→155 lines - Context management with vector DBs - context-restore: 65→157 lines - Context restoration and RAG - smart-debug: 65→1,727 lines - AI-assisted debugging with observability - standup-notes: 68→765 lines - Async-first with Git integration - multi-agent-optimize: 85→189 lines - Performance optimization framework - incident-response: 80→146 lines - SRE practices and incident command - feature-development: 84→144 lines - End-to-end feature workflow Technologies integrated: - AI/ML: GitHub Copilot, Claude Code, LangChain 0.1+, Voyage AI embeddings - Observability: OpenTelemetry, DataDog, Sentry, Honeycomb, Prometheus - DevSecOps: Snyk, Trivy, Semgrep, CodeQL, OWASP Top 10 - Cloud: Kubernetes, GitOps (ArgoCD/Flux), AWS/Azure/GCP - Frameworks: React 19, Next.js 15, FastAPI, Django 5, Pydantic v2 - Data: Apache Spark, Airflow, Delta Lake, Great Expectations All files now include: - Clear role statements and expertise definitions - Structured Context/Requirements sections - 6-8 major instruction sections (tools) or 3-4 phases (workflows) - Multiple complete code examples in various languages - Modern framework integrations - Real-world reference implementations
This commit is contained in:
Seth Hobson
@@ -1,68 +1,194 @@
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---
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model: sonnet
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---
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# Multi-Agent Code Review Orchestration Tool
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Perform comprehensive multi-agent code review with specialized reviewers:
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## Role: Expert Multi-Agent Review Orchestration Specialist
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[Extended thinking: This tool command invokes multiple review-focused agents to provide different perspectives on code quality, security, and architecture. Each agent reviews independently, then findings are consolidated.]
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A sophisticated AI-powered code review system designed to provide comprehensive, multi-perspective analysis of software artifacts through intelligent agent coordination and specialized domain expertise.
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## Review Process
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## Context and Purpose
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### 1. Code Quality Review
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Use Task tool with subagent_type="code-reviewer" to examine:
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- Code style and readability
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- Adherence to SOLID principles
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- Design patterns and anti-patterns
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- Code duplication and complexity
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- Documentation completeness
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- Test coverage and quality
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The Multi-Agent Review Tool leverages a distributed, specialized agent network to perform holistic code assessments that transcend traditional single-perspective review approaches. By coordinating agents with distinct expertise, we generate a comprehensive evaluation that captures nuanced insights across multiple critical dimensions:
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Prompt: "Perform detailed code review of: $ARGUMENTS. Focus on maintainability, readability, and best practices. Provide specific line-by-line feedback where appropriate."
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- **Depth**: Specialized agents dive deep into specific domains
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- **Breadth**: Parallel processing enables comprehensive coverage
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- **Intelligence**: Context-aware routing and intelligent synthesis
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- **Adaptability**: Dynamic agent selection based on code characteristics
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### 2. Security Review
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Use Task tool with subagent_type="security-auditor" to check:
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- Authentication and authorization flaws
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- Input validation and sanitization
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- SQL injection and XSS vulnerabilities
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- Sensitive data exposure
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- Security misconfigurations
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- Dependency vulnerabilities
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## Tool Arguments and Configuration
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Prompt: "Conduct security review of: $ARGUMENTS. Identify vulnerabilities, security risks, and OWASP compliance issues. Provide severity ratings and remediation steps."
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### Input Parameters
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- `$ARGUMENTS`: Target code/project for review
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- Supports: File paths, Git repositories, code snippets
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- Handles multiple input formats
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- Enables context extraction and agent routing
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### 3. Architecture Review
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Use Task tool with subagent_type="architect-reviewer" to evaluate:
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- Service boundaries and coupling
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- Scalability considerations
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- Design pattern appropriateness
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- Technology choices
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- API design quality
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- Data flow and dependencies
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### Agent Types
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1. Code Quality Reviewers
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2. Security Auditors
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3. Architecture Specialists
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4. Performance Analysts
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5. Compliance Validators
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6. Best Practices Experts
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Prompt: "Review architecture and design of: $ARGUMENTS. Evaluate scalability, maintainability, and architectural patterns. Identify potential bottlenecks and design improvements."
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## Multi-Agent Coordination Strategy
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## Consolidated Review Output
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### 1. Agent Selection and Routing Logic
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- **Dynamic Agent Matching**:
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- Analyze input characteristics
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- Select most appropriate agent types
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- Configure specialized sub-agents dynamically
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- **Expertise Routing**:
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```python
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def route_agents(code_context):
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agents = []
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if is_web_application(code_context):
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agents.extend([
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"security-auditor",
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"web-architecture-reviewer"
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])
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if is_performance_critical(code_context):
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agents.append("performance-analyst")
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return agents
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```
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After all agents complete their reviews, consolidate findings into:
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### 2. Context Management and State Passing
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- **Contextual Intelligence**:
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- Maintain shared context across agent interactions
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- Pass refined insights between agents
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- Support incremental review refinement
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- **Context Propagation Model**:
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```python
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class ReviewContext:
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def __init__(self, target, metadata):
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self.target = target
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self.metadata = metadata
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self.agent_insights = {}
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1. **Critical Issues** - Must fix before merge
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- Security vulnerabilities
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- Broken functionality
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- Major architectural flaws
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def update_insights(self, agent_type, insights):
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self.agent_insights[agent_type] = insights
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```
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2. **Important Issues** - Should fix soon
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- Performance problems
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- Code quality issues
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- Missing tests
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### 3. Parallel vs Sequential Execution
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- **Hybrid Execution Strategy**:
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- Parallel execution for independent reviews
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- Sequential processing for dependent insights
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- Intelligent timeout and fallback mechanisms
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- **Execution Flow**:
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```python
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def execute_review(review_context):
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# Parallel independent agents
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parallel_agents = [
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"code-quality-reviewer",
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"security-auditor"
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]
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3. **Minor Issues** - Nice to fix
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- Style inconsistencies
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- Documentation gaps
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- Refactoring opportunities
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# Sequential dependent agents
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sequential_agents = [
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"architecture-reviewer",
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"performance-optimizer"
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]
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```
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4. **Positive Findings** - Good practices to highlight
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- Well-designed components
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- Good test coverage
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- Security best practices
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### 4. Result Aggregation and Synthesis
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- **Intelligent Consolidation**:
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- Merge insights from multiple agents
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- Resolve conflicting recommendations
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- Generate unified, prioritized report
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- **Synthesis Algorithm**:
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```python
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def synthesize_review_insights(agent_results):
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consolidated_report = {
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"critical_issues": [],
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"important_issues": [],
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"improvement_suggestions": []
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}
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# Intelligent merging logic
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return consolidated_report
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```
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### 5. Conflict Resolution Mechanism
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- **Smart Conflict Handling**:
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- Detect contradictory agent recommendations
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- Apply weighted scoring
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- Escalate complex conflicts
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- **Resolution Strategy**:
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```python
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def resolve_conflicts(agent_insights):
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conflict_resolver = ConflictResolutionEngine()
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return conflict_resolver.process(agent_insights)
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```
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### 6. Performance Optimization
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- **Efficiency Techniques**:
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- Minimal redundant processing
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- Cached intermediate results
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- Adaptive agent resource allocation
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- **Optimization Approach**:
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```python
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def optimize_review_process(review_context):
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return ReviewOptimizer.allocate_resources(review_context)
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```
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### 7. Quality Validation Framework
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- **Comprehensive Validation**:
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- Cross-agent result verification
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- Statistical confidence scoring
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- Continuous learning and improvement
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- **Validation Process**:
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```python
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def validate_review_quality(review_results):
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quality_score = QualityScoreCalculator.compute(review_results)
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return quality_score > QUALITY_THRESHOLD
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```
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## Example Implementations
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### 1. Parallel Code Review Scenario
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```python
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multi_agent_review(
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target="/path/to/project",
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agents=[
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{"type": "security-auditor", "weight": 0.3},
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{"type": "architecture-reviewer", "weight": 0.3},
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{"type": "performance-analyst", "weight": 0.2}
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]
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)
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```
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### 2. Sequential Workflow
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```python
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sequential_review_workflow = [
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{"phase": "design-review", "agent": "architect-reviewer"},
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{"phase": "implementation-review", "agent": "code-quality-reviewer"},
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{"phase": "testing-review", "agent": "test-coverage-analyst"},
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{"phase": "deployment-readiness", "agent": "devops-validator"}
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]
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```
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### 3. Hybrid Orchestration
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```python
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hybrid_review_strategy = {
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"parallel_agents": ["security", "performance"],
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"sequential_agents": ["architecture", "compliance"]
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}
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```
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## Reference Implementations
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1. **Web Application Security Review**
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2. **Microservices Architecture Validation**
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## Best Practices and Considerations
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- Maintain agent independence
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- Implement robust error handling
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- Use probabilistic routing
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- Support incremental reviews
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- Ensure privacy and security
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## Extensibility
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The tool is designed with a plugin-based architecture, allowing easy addition of new agent types and review strategies.
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## Invocation
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Target for review: $ARGUMENTS
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