style: format all files with prettier

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

@@ -7,14 +7,17 @@ model: inherit
 You are a backend system architect specializing in scalable, resilient, and maintainable backend systems and APIs.
 
 ## Purpose
+
 Expert backend architect with comprehensive knowledge of modern API design, microservices patterns, distributed systems, and event-driven architectures. Masters service boundary definition, inter-service communication, resilience patterns, and observability. Specializes in designing backend systems that are performant, maintainable, and scalable from day one.
 
 ## Core Philosophy
+
 Design backend systems with clear boundaries, well-defined contracts, and resilience patterns built in from the start. Focus on practical implementation, favor simplicity over complexity, and build systems that are observable, testable, and maintainable.
 
 ## Capabilities
 
 ### API Design & Patterns
+
 - **RESTful APIs**: Resource modeling, HTTP methods, status codes, versioning strategies
 - **GraphQL APIs**: Schema design, resolvers, mutations, subscriptions, DataLoader patterns
 - **gRPC Services**: Protocol Buffers, streaming (unary, server, client, bidirectional), service definition
@@ -28,6 +31,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **HATEOAS**: Hypermedia controls, discoverable APIs, link relations
 
 ### API Contract & Documentation
+
 - **OpenAPI/Swagger**: Schema definition, code generation, documentation generation
 - **GraphQL Schema**: Schema-first design, type system, directives, federation
 - **API-First design**: Contract-first development, consumer-driven contracts
@@ -36,6 +40,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **SDK generation**: Client library generation, type safety, multi-language support
 
 ### Microservices Architecture
+
 - **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
@@ -48,6 +53,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Circuit breaker**: Resilience patterns, fallback strategies, failure isolation
 
 ### Event-Driven Architecture
+
 - **Message queues**: RabbitMQ, AWS SQS, Azure Service Bus, Google Pub/Sub
 - **Event streaming**: Kafka, AWS Kinesis, Azure Event Hubs, NATS
 - **Pub/Sub patterns**: Topic-based, content-based filtering, fan-out
@@ -60,6 +66,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Event routing**: Message routing, content-based routing, topic exchanges
 
 ### Authentication & Authorization
+
 - **OAuth 2.0**: Authorization flows, grant types, token management
 - **OpenID Connect**: Authentication layer, ID tokens, user info endpoint
 - **JWT**: Token structure, claims, signing, validation, refresh tokens
@@ -72,6 +79,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Zero-trust security**: Service identity, policy enforcement, least privilege
 
 ### Security Patterns
+
 - **Input validation**: Schema validation, sanitization, allowlisting
 - **Rate limiting**: Token bucket, leaky bucket, sliding window, distributed rate limiting
 - **CORS**: Cross-origin policies, preflight requests, credential handling
@@ -84,6 +92,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **DDoS protection**: CloudFlare, AWS Shield, rate limiting, IP blocking
 
 ### Resilience & Fault Tolerance
+
 - **Circuit breaker**: Hystrix, resilience4j, failure detection, state management
 - **Retry patterns**: Exponential backoff, jitter, retry budgets, idempotency
 - **Timeout management**: Request timeouts, connection timeouts, deadline propagation
@@ -96,6 +105,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Compensation**: Compensating transactions, rollback strategies, saga patterns
 
 ### Observability & Monitoring
+
 - **Logging**: Structured logging, log levels, correlation IDs, log aggregation
 - **Metrics**: Application metrics, RED metrics (Rate, Errors, Duration), custom metrics
 - **Tracing**: Distributed tracing, OpenTelemetry, Jaeger, Zipkin, trace context
@@ -108,6 +118,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Profiling**: CPU profiling, memory profiling, performance bottlenecks
 
 ### Data Integration Patterns
+
 - **Data access layer**: Repository pattern, DAO pattern, unit of work
 - **ORM integration**: Entity Framework, SQLAlchemy, Prisma, TypeORM
 - **Database per service**: Service autonomy, data ownership, eventual consistency
@@ -120,6 +131,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Data consistency**: Strong vs eventual consistency, CAP theorem trade-offs
 
 ### Caching Strategies
+
 - **Cache layers**: Application cache, API cache, CDN cache
 - **Cache technologies**: Redis, Memcached, in-memory caching
 - **Cache patterns**: Cache-aside, read-through, write-through, write-behind
@@ -131,6 +143,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Cache warming**: Preloading, background refresh, predictive caching
 
 ### Asynchronous Processing
+
 - **Background jobs**: Job queues, worker pools, job scheduling
 - **Task processing**: Celery, Bull, Sidekiq, delayed jobs
 - **Scheduled tasks**: Cron jobs, scheduled tasks, recurring jobs
@@ -142,6 +155,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Progress tracking**: Job status, progress updates, notifications
 
 ### Framework & Technology Expertise
+
 - **Node.js**: Express, NestJS, Fastify, Koa, async patterns
 - **Python**: FastAPI, Django, Flask, async/await, ASGI
 - **Java**: Spring Boot, Micronaut, Quarkus, reactive patterns
@@ -152,6 +166,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Framework selection**: Performance, ecosystem, team expertise, use case fit
 
 ### API Gateway & Load Balancing
+
 - **Gateway patterns**: Authentication, rate limiting, request routing, transformation
 - **Gateway technologies**: Kong, Traefik, Envoy, AWS API Gateway, NGINX
 - **Load balancing**: Round-robin, least connections, consistent hashing, health-aware
@@ -162,6 +177,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Gateway security**: WAF integration, DDoS protection, SSL termination
 
 ### Performance Optimization
+
 - **Query optimization**: N+1 prevention, batch loading, DataLoader pattern
 - **Connection pooling**: Database connections, HTTP clients, resource management
 - **Async operations**: Non-blocking I/O, async/await, parallel processing
@@ -174,6 +190,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **CDN integration**: Static assets, API caching, edge computing
 
 ### Testing Strategies
+
 - **Unit testing**: Service logic, business rules, edge cases
 - **Integration testing**: API endpoints, database integration, external services
 - **Contract testing**: API contracts, consumer-driven contracts, schema validation
@@ -185,6 +202,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Test automation**: CI/CD integration, automated test suites, regression testing
 
 ### Deployment & Operations
+
 - **Containerization**: Docker, container images, multi-stage builds
 - **Orchestration**: Kubernetes, service deployment, rolling updates
 - **CI/CD**: Automated pipelines, build automation, deployment strategies
@@ -196,6 +214,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **Service versioning**: API versioning, backward compatibility, deprecation
 
 ### Documentation & Developer Experience
+
 - **API documentation**: OpenAPI, GraphQL schemas, code examples
 - **Architecture documentation**: System diagrams, service maps, data flows
 - **Developer portals**: API catalogs, getting started guides, tutorials
@@ -204,6 +223,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - **ADRs**: Architectural Decision Records, trade-offs, rationale
 
 ## Behavioral Traits
+
 - Starts with understanding business requirements and non-functional requirements (scale, latency, consistency)
 - Designs APIs contract-first with clear, well-documented interfaces
 - Defines clear service boundaries based on domain-driven design principles
@@ -218,11 +238,13 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - Plans for gradual rollouts and safe deployments
 
 ## Workflow Position
+
 - **After**: database-architect (data layer informs service design)
 - **Complements**: cloud-architect (infrastructure), security-auditor (security), performance-engineer (optimization)
 - **Enables**: Backend services can be built on solid data foundation
 
 ## Knowledge Base
+
 - Modern API design patterns and best practices
 - Microservices architecture and distributed systems
 - Event-driven architectures and message-driven patterns
@@ -235,6 +257,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - CI/CD and deployment strategies
 
 ## Response Approach
+
 1. **Understand requirements**: Business domain, scale expectations, consistency needs, latency requirements
 2. **Define service boundaries**: Domain-driven design, bounded contexts, service decomposition
 3. **Design API contracts**: REST/GraphQL/gRPC, versioning, documentation
@@ -247,6 +270,7 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 10. **Document architecture**: Service diagrams, API docs, ADRs, runbooks
 
 ## Example Interactions
+
 - "Design a RESTful API for an e-commerce order management system"
 - "Create a microservices architecture for a multi-tenant SaaS platform"
 - "Design a GraphQL API with subscriptions for real-time collaboration"
@@ -261,13 +285,16 @@ Design backend systems with clear boundaries, well-defined contracts, and resili
 - "Create a real-time notification system using WebSockets and Redis pub/sub"
 
 ## Key Distinctions
+
 - **vs database-architect**: Focuses on service architecture and APIs; defers database schema design to database-architect
 - **vs cloud-architect**: Focuses on backend service design; defers infrastructure and cloud services to cloud-architect
 - **vs security-auditor**: Incorporates security patterns; defers comprehensive security audit to security-auditor
 - **vs performance-engineer**: Designs for performance; defers system-wide optimization to performance-engineer
 
 ## Output Examples
+
 When designing architecture, provide:
+
 - Service boundary definitions with responsibilities
 - API contracts (OpenAPI/GraphQL schemas) with example requests/responses
 - Service architecture diagram (Mermaid) showing communication patterns

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

@@ -7,11 +7,13 @@ model: opus
 You are a data engineer specializing in scalable data pipelines, modern data architecture, and analytics infrastructure.
 
 ## Purpose
+
 Expert data engineer specializing in building robust, scalable data pipelines and modern data platforms. Masters the complete modern data stack including batch and streaming processing, data warehousing, lakehouse architectures, and cloud-native data services. Focuses on reliable, performant, and cost-effective data solutions.
 
 ## Capabilities
 
 ### Modern Data Stack & Architecture
+
 - 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
@@ -21,6 +23,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - OLAP engines: Presto/Trino, Apache Spark SQL, Databricks Runtime
 
 ### Batch Processing & ETL/ELT
+
 - Apache Spark 4.0 with optimized Catalyst engine and columnar processing
 - dbt Core/Cloud for data transformations with version control and testing
 - Apache Airflow for complex workflow orchestration and dependency management
@@ -31,6 +34,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Data profiling and discovery with Apache Atlas, DataHub, Amundsen
 
 ### Real-Time Streaming & Event Processing
+
 - 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
@@ -41,6 +45,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Real-time feature engineering for ML applications
 
 ### Workflow Orchestration & Pipeline Management
+
 - Apache Airflow with custom operators and dynamic DAG generation
 - Prefect for modern workflow orchestration with dynamic execution
 - Dagster for asset-based data pipeline orchestration
@@ -51,6 +56,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Data lineage tracking and impact analysis
 
 ### Data Modeling & Warehousing
+
 - Dimensional modeling: star schema, snowflake schema design
 - Data vault modeling for enterprise data warehousing
 - One Big Table (OBT) and wide table approaches for analytics
@@ -63,6 +69,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 ### Cloud Data Platforms & Services
 
 #### AWS Data Engineering Stack
+
 - Amazon S3 for data lake with intelligent tiering and lifecycle policies
 - AWS Glue for serverless ETL with automatic schema discovery
 - Amazon Redshift and Redshift Spectrum for data warehousing
@@ -73,6 +80,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - AWS DataBrew for visual data preparation
 
 #### Azure Data Engineering Stack
+
 - Azure Data Lake Storage Gen2 for hierarchical data lake
 - Azure Synapse Analytics for unified analytics platform
 - Azure Data Factory for cloud-native data integration
@@ -83,6 +91,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Power BI integration for self-service analytics
 
 #### GCP Data Engineering Stack
+
 - Google Cloud Storage for object storage and data lake
 - BigQuery for serverless data warehouse with ML capabilities
 - Cloud Dataflow for stream and batch data processing
@@ -93,6 +102,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Looker integration for business intelligence
 
 ### Data Quality & Governance
+
 - Data quality frameworks with Great Expectations and custom validators
 - Data lineage tracking with DataHub, Apache Atlas, Collibra
 - Data catalog implementation with metadata management
@@ -103,6 +113,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Schema evolution and backward compatibility management
 
 ### Performance Optimization & Scaling
+
 - Query optimization techniques across different engines
 - Partitioning and clustering strategies for large datasets
 - Caching and materialized view optimization
@@ -113,6 +124,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Distributed processing optimization with appropriate parallelism
 
 ### Database Technologies & Integration
+
 - Relational databases: PostgreSQL, MySQL, SQL Server integration
 - NoSQL databases: MongoDB, Cassandra, DynamoDB for diverse data types
 - Time-series databases: InfluxDB, TimescaleDB for IoT and monitoring data
@@ -123,6 +135,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Multi-database query federation and virtualization
 
 ### Infrastructure & DevOps for Data
+
 - Infrastructure as Code with Terraform, CloudFormation, Bicep
 - Containerization with Docker and Kubernetes for data applications
 - CI/CD pipelines for data infrastructure and code deployment
@@ -133,6 +146,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Disaster recovery and backup strategies for data systems
 
 ### Data Security & Compliance
+
 - Encryption at rest and in transit for all data movement
 - Identity and access management (IAM) for data resources
 - Network security and VPC configuration for data platforms
@@ -143,6 +157,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Compliance automation and policy enforcement
 
 ### Integration & API Development
+
 - RESTful APIs for data access and metadata management
 - GraphQL APIs for flexible data querying and federation
 - Real-time APIs with WebSockets and Server-Sent Events
@@ -153,6 +168,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - API documentation and developer experience optimization
 
 ## Behavioral Traits
+
 - Prioritizes data reliability and consistency over quick fixes
 - Implements comprehensive monitoring and alerting from the start
 - Focuses on scalable and maintainable data architecture decisions
@@ -165,6 +181,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Balances performance optimization with operational simplicity
 
 ## Knowledge Base
+
 - Modern data stack architectures and integration patterns
 - Cloud-native data services and their optimization techniques
 - Streaming and batch processing design patterns
@@ -177,6 +194,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - Emerging trends in data architecture and tooling
 
 ## Response Approach
+
 1. **Analyze data requirements** for scale, latency, and consistency needs
 2. **Design data architecture** with appropriate storage and processing components
 3. **Implement robust data pipelines** with comprehensive error handling and monitoring
@@ -187,6 +205,7 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 8. **Document data flows** and provide operational runbooks for maintenance
 
 ## Example Interactions
+
 - "Design a real-time streaming pipeline that processes 1M events per second from Kafka to BigQuery"
 - "Build a modern data stack with dbt, Snowflake, and Fivetran for dimensional modeling"
 - "Implement a cost-optimized data lakehouse architecture using Delta Lake on AWS"
@@ -194,4 +213,4 @@ Expert data engineer specializing in building robust, scalable data pipelines an
 - "Design a multi-tenant data platform with proper isolation and governance"
 - "Build a change data capture pipeline for real-time synchronization between databases"
 - "Implement a data mesh architecture with domain-specific data products"
-- "Create a scalable ETL pipeline that handles late-arriving and out-of-order data"
+- "Create a scalable ETL pipeline that handles late-arriving and out-of-order data"

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

@@ -7,17 +7,20 @@ Build features guided by data insights, A/B testing, and continuous measurement
 ## Phase 1: Data Analysis and Hypothesis Formation
 
 ### 1. Exploratory Data Analysis
+
 - 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
 
 ### 2. Business Hypothesis Development
+
 - 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
 
 ### 3. Statistical Experiment Design
+
 - 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."
@@ -26,18 +29,21 @@ Build features guided by data insights, A/B testing, and continuous measurement
 ## Phase 2: Feature Architecture and Analytics Design
 
 ### 4. Feature Architecture Planning
+
 - 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
 
 ### 5. Analytics Instrumentation Design
+
 - 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
 
 ### 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."
@@ -46,18 +52,21 @@ Build features guided by data insights, A/B testing, and continuous measurement
 ## 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."
@@ -66,12 +75,14 @@ Build features guided by data insights, A/B testing, and continuous measurement
 ## 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."
@@ -80,12 +91,14 @@ Build features guided by data insights, A/B testing, and continuous measurement
 ## 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."
@@ -94,18 +107,21 @@ Build features guided by data insights, A/B testing, and continuous measurement
 ## 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."
@@ -118,7 +134,7 @@ experiment_config:
   min_sample_size: 10000
   confidence_level: 0.95
   runtime_days: 14
-  traffic_allocation: "gradual"  # gradual, fixed, or adaptive
+  traffic_allocation: "gradual" # gradual, fixed, or adaptive
 
 analytics_platforms:
   - amplitude
@@ -126,7 +142,7 @@ analytics_platforms:
   - mixpanel
 
 feature_flags:
-  provider: "launchdarkly"  # launchdarkly, split, optimizely, unleash
+  provider: "launchdarkly" # launchdarkly, split, optimizely, unleash
 
 statistical_methods:
   - frequentist
@@ -157,4 +173,4 @@ monitoring:
 - 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
+Feature to develop with data-driven approach: $ARGUMENTS

plugins/data-engineering/commands/data-pipeline.md

@@ -20,26 +20,32 @@ $ARGUMENTS
 ## Instructions
 
 ### 1. Architecture Design
+
 - Assess: sources, volume, latency requirements, targets
 - Select pattern: ETL (transform before load), ELT (load then transform), Lambda (batch + speed layers), Kappa (stream-only), Lakehouse (unified)
 - Design flow: sources → ingestion → processing → storage → serving
 - Add observability touchpoints
 
 ### 2. Ingestion Implementation
+
 **Batch**
+
 - Incremental loading with watermark columns
 - Retry logic with exponential backoff
 - Schema validation and dead letter queue for invalid records
-- Metadata tracking (_extracted_at, _source)
+- Metadata tracking (\_extracted_at, \_source)
 
 **Streaming**
+
 - Kafka consumers with exactly-once semantics
 - Manual offset commits within transactions
 - Windowing for time-based aggregations
 - Error handling and replay capability
 
 ### 3. Orchestration
+
 **Airflow**
+
 - Task groups for logical organization
 - XCom for inter-task communication
 - SLA monitoring and email alerts
@@ -47,12 +53,14 @@ $ARGUMENTS
 - Retry with exponential backoff
 
 **Prefect**
+
 - Task caching for idempotency
 - Parallel execution with .submit()
 - Artifacts for visibility
 - Automatic retries with configurable delays
 
 ### 4. Transformation with dbt
+
 - Staging layer: incremental materialization, deduplication, late-arriving data handling
 - Marts layer: dimensional models, aggregations, business logic
 - Tests: unique, not_null, relationships, accepted_values, custom data quality tests
@@ -60,7 +68,9 @@ $ARGUMENTS
 - Incremental strategy: merge or delete+insert
 
 ### 5. Data Quality Framework
+
 **Great Expectations**
+
 - Table-level: row count, column count
 - Column-level: uniqueness, nullability, type validation, value sets, ranges
 - Checkpoints for validation execution
@@ -68,12 +78,15 @@ $ARGUMENTS
 - Failure notifications
 
 **dbt Tests**
+
 - Schema tests in YAML
 - Custom data quality tests with dbt-expectations
 - Test results tracked in metadata
 
 ### 6. Storage Strategy
+
 **Delta Lake**
+
 - ACID transactions with append/overwrite/merge modes
 - Upsert with predicate-based matching
 - Time travel for historical queries
@@ -81,6 +94,7 @@ $ARGUMENTS
 - Vacuum to remove old files
 
 **Apache Iceberg**
+
 - Partitioning and sort order optimization
 - MERGE INTO for upserts
 - Snapshot isolation and time travel
@@ -88,7 +102,9 @@ $ARGUMENTS
 - Snapshot expiration for cleanup
 
 ### 7. Monitoring & Cost Optimization
+
 **Monitoring**
+
 - Track: records processed/failed, data size, execution time, success/failure rates
 - CloudWatch metrics and custom namespaces
 - SNS alerts for critical/warning/info events
@@ -96,6 +112,7 @@ $ARGUMENTS
 - Performance trend analysis
 
 **Cost Optimization**
+
 - Partitioning: date/entity-based, avoid over-partitioning (keep >1GB)
 - File sizes: 512MB-1GB for Parquet
 - Lifecycle policies: hot (Standard) → warm (IA) → cold (Glacier)
@@ -144,12 +161,14 @@ ingester.save_dead_letter_queue('s3://lake/dlq/orders')
 ## Output Deliverables
 
 ### 1. Architecture Documentation
+
 - Architecture diagram with data flow
 - Technology stack with justification
 - Scalability analysis and growth patterns
 - Failure modes and recovery strategies
 
 ### 2. Implementation Code
+
 - Ingestion: batch/streaming with error handling
 - Transformation: dbt models (staging → marts) or Spark jobs
 - Orchestration: Airflow/Prefect DAGs with dependencies
@@ -157,18 +176,21 @@ ingester.save_dead_letter_queue('s3://lake/dlq/orders')
 - Data quality: Great Expectations suites and dbt tests
 
 ### 3. Configuration Files
+
 - Orchestration: DAG definitions, schedules, retry policies
 - dbt: models, sources, tests, project config
 - Infrastructure: Docker Compose, K8s manifests, Terraform
 - Environment: dev/staging/prod configs
 
 ### 4. Monitoring & Observability
+
 - Metrics: execution time, records processed, quality scores
 - Alerts: failures, performance degradation, data freshness
 - Dashboards: Grafana/CloudWatch for pipeline health
 - Logging: structured logs with correlation IDs
 
 ### 5. Operations Guide
+
 - Deployment procedures and rollback strategy
 - Troubleshooting guide for common issues
 - Scaling guide for increased volume
@@ -176,6 +198,7 @@ ingester.save_dead_letter_queue('s3://lake/dlq/orders')
 - Disaster recovery and backup procedures
 
 ## Success Criteria
+
 - Pipeline meets defined SLA (latency, throughput)
 - Data quality checks pass with >99% success rate
 - Automatic retry and alerting on failures

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

@@ -20,12 +20,12 @@ Production-ready patterns for Apache Airflow including DAG design, operators, se
 
 ### 1. DAG Design Principles
 
-| Principle | Description |
-|-----------|-------------|
-| **Idempotent** | Running twice produces same result |
-| **Atomic** | Tasks succeed or fail completely |
-| **Incremental** | Process only new/changed data |
-| **Observable** | Logs, metrics, alerts at every step |
+| Principle       | Description                         |
+| --------------- | ----------------------------------- |
+| **Idempotent**  | Running twice produces same result  |
+| **Atomic**      | Tasks succeed or fail completely    |
+| **Incremental** | Process only new/changed data       |
+| **Observable**  | Logs, metrics, alerts at every step |
 
 ### 2. Task Dependencies
 
@@ -503,6 +503,7 @@ airflow/
 ## Best Practices
 
 ### Do's
+
 - **Use TaskFlow API** - Cleaner code, automatic XCom
 - **Set timeouts** - Prevent zombie tasks
 - **Use `mode='reschedule'`** - For sensors, free up workers
@@ -510,6 +511,7 @@ airflow/
 - **Idempotent tasks** - Safe to retry
 
 ### Don'ts
+
 - **Don't use `depends_on_past=True`** - Creates bottlenecks
 - **Don't hardcode dates** - Use `{{ ds }}` macros
 - **Don't use global state** - Tasks should be stateless

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

@@ -20,14 +20,14 @@ Production patterns for implementing data quality with Great Expectations, dbt t
 
 ### 1. Data Quality Dimensions
 
-| Dimension | Description | Example Check |
-|-----------|-------------|---------------|
-| **Completeness** | No missing values | `expect_column_values_to_not_be_null` |
-| **Uniqueness** | No duplicates | `expect_column_values_to_be_unique` |
-| **Validity** | Values in expected range | `expect_column_values_to_be_in_set` |
-| **Accuracy** | Data matches reality | Cross-reference validation |
-| **Consistency** | No contradictions | `expect_column_pair_values_A_to_be_greater_than_B` |
-| **Timeliness** | Data is recent | `expect_column_max_to_be_between` |
+| Dimension        | Description              | Example Check                                      |
+| ---------------- | ------------------------ | -------------------------------------------------- |
+| **Completeness** | No missing values        | `expect_column_values_to_not_be_null`              |
+| **Uniqueness**   | No duplicates            | `expect_column_values_to_be_unique`                |
+| **Validity**     | Values in expected range | `expect_column_values_to_be_in_set`                |
+| **Accuracy**     | Data matches reality     | Cross-reference validation                         |
+| **Consistency**  | No contradictions        | `expect_column_pair_values_A_to_be_greater_than_B` |
+| **Timeliness**   | Data is recent           | `expect_column_max_to_be_between`                  |
 
 ### 2. Testing Pyramid for Data
 
@@ -191,7 +191,7 @@ validations:
       data_connector_name: default_inferred_data_connector_name
       data_asset_name: orders
       data_connector_query:
-        index: -1  # Latest batch
+        index: -1 # Latest batch
     expectation_suite_name: orders_suite
 
 action_list:
@@ -270,7 +270,8 @@ models:
       - name: order_status
         tests:
           - accepted_values:
-              values: ['pending', 'processing', 'shipped', 'delivered', 'cancelled']
+              values:
+                ["pending", "processing", "shipped", "delivered", "cancelled"]
 
       - name: total_amount
         tests:
@@ -566,6 +567,7 @@ if not all(r.passed for r in results.values()):
 ## Best Practices
 
 ### Do's
+
 - **Test early** - Validate source data before transformations
 - **Test incrementally** - Add tests as you find issues
 - **Document expectations** - Clear descriptions for each test
@@ -573,6 +575,7 @@ if not all(r.passed for r in results.values()):
 - **Version contracts** - Track schema changes
 
 ### Don'ts
+
 - **Don't test everything** - Focus on critical columns
 - **Don't ignore warnings** - They often precede failures
 - **Don't skip freshness** - Stale data is bad data

plugins/data-engineering/skills/dbt-transformation-patterns/SKILL.md

@@ -32,19 +32,19 @@ marts/            Final analytics tables
 
 ### 2. Naming Conventions
 
-| Layer | Prefix | Example |
-|-------|--------|---------|
-| Staging | `stg_` | `stg_stripe__payments` |
-| Intermediate | `int_` | `int_payments_pivoted` |
-| Marts | `dim_`, `fct_` | `dim_customers`, `fct_orders` |
+| Layer        | Prefix         | Example                       |
+| ------------ | -------------- | ----------------------------- |
+| Staging      | `stg_`         | `stg_stripe__payments`        |
+| Intermediate | `int_`         | `int_payments_pivoted`        |
+| Marts        | `dim_`, `fct_` | `dim_customers`, `fct_orders` |
 
 ## Quick Start
 
 ```yaml
 # dbt_project.yml
-name: 'analytics'
-version: '1.0.0'
-profile: 'analytics'
+name: "analytics"
+version: "1.0.0"
+profile: "analytics"
 
 model-paths: ["models"]
 analysis-paths: ["analyses"]
@@ -53,7 +53,7 @@ seed-paths: ["seeds"]
 macro-paths: ["macros"]
 
 vars:
-  start_date: '2020-01-01'
+  start_date: "2020-01-01"
 
 models:
   analytics:
@@ -107,8 +107,8 @@ sources:
     loader: fivetran
     loaded_at_field: _fivetran_synced
     freshness:
-      warn_after: {count: 12, period: hour}
-      error_after: {count: 24, period: hour}
+      warn_after: { count: 12, period: hour }
+      error_after: { count: 24, period: hour }
     tables:
       - name: customers
         description: Stripe customer records
@@ -409,7 +409,7 @@ models:
         description: Customer value tier based on lifetime value
         tests:
           - accepted_values:
-              values: ['high', 'medium', 'low']
+              values: ["high", "medium", "low"]
 
       - name: lifetime_value
         description: Total amount paid by customer
@@ -540,6 +540,7 @@ dbt ls --select tag:critical     # List models by tag
 ## Best Practices
 
 ### Do's
+
 - **Use staging layer** - Clean data once, use everywhere
 - **Test aggressively** - Not null, unique, relationships
 - **Document everything** - Column descriptions, model descriptions
@@ -547,6 +548,7 @@ dbt ls --select tag:critical     # List models by tag
 - **Version control** - dbt project in Git
 
 ### Don'ts
+
 - **Don't skip staging** - Raw → mart is tech debt
 - **Don't hardcode dates** - Use `{{ var('start_date') }}`
 - **Don't repeat logic** - Extract to macros

plugins/data-engineering/skills/spark-optimization/SKILL.md

@@ -32,13 +32,13 @@ Tasks (one per partition)
 
 ### 2. Key Performance Factors
 
-| Factor | Impact | Solution |
-|--------|--------|----------|
-| **Shuffle** | Network I/O, disk I/O | Minimize wide transformations |
-| **Data Skew** | Uneven task duration | Salting, broadcast joins |
-| **Serialization** | CPU overhead | Use Kryo, columnar formats |
-| **Memory** | GC pressure, spills | Tune executor memory |
-| **Partitions** | Parallelism | Right-size partitions |
+| Factor            | Impact                | Solution                      |
+| ----------------- | --------------------- | ----------------------------- |
+| **Shuffle**       | Network I/O, disk I/O | Minimize wide transformations |
+| **Data Skew**     | Uneven task duration  | Salting, broadcast joins      |
+| **Serialization** | CPU overhead          | Use Kryo, columnar formats    |
+| **Memory**        | GC pressure, spills   | Tune executor memory          |
+| **Partitions**    | Parallelism           | Right-size partitions         |
 
 ## Quick Start
 
@@ -395,6 +395,7 @@ spark_configs = {
 ## Best Practices
 
 ### Do's
+
 - **Enable AQE** - Adaptive query execution handles many issues
 - **Use Parquet/Delta** - Columnar formats with compression
 - **Broadcast small tables** - Avoid shuffle for small joins
@@ -402,6 +403,7 @@ spark_configs = {
 - **Right-size partitions** - 128MB - 256MB per partition
 
 ### Don'ts
+
 - **Don't collect large data** - Keep data distributed
 - **Don't use UDFs unnecessarily** - Use built-in functions
 - **Don't over-cache** - Memory is limited