Comprehensive data validation using Pydantic v2 with data quality monitoring and schema alignment for PlanetScale PostgreSQL. Use when implementing API validation, database schema alignment, or data quality assurance. Triggers: 'validation', 'Pydantic', 'schema', 'data quality'.
Agent assistance skill that provides stuck detection, memory management, and session learning capabilities for AI agents Trigger terms: agent stuck, loop detected, session memory, agent learning, condense memory, stuck detection, agent memory, session learnings, extraction Use when: User reports agent is stuck, looping, or needs memory/learning management
Integrated orchestrator agent that manages and coordinates 25 specialized AI agents for Specification Driven Development Trigger terms: orchestrate, coordinate, multi-agent, workflow, execution plan, task breakdown, agent selection, project planning, complex task, full lifecycle, end-to-end development, comprehensive solution Use when: User requests involve orchestrator tasks.
Intelligently onboards brownfield projects by merging existing CLAUDE.md backups into SpecWeave structure. Extracts project-specific knowledge, domain context, team conventions, and technical details from backup CLAUDE.md files, then distributes content to appropriate SpecWeave folders without bloating CLAUDE.md. Activates for: merge docs, merge claude, onboard brownfield, import existing docs, claude backup, specweave merge-docs.
Extract and organize Claude Code session history into project .chats directory. Use when users want to document their Claude sessions, export conversation inputs, or maintain a log of instructions given to Claude.
Explains how SpecWeave achieves context efficiency through Claude's native progressive disclosure mechanism and sub-agent parallelization. Skills load only when relevant, sub-agents isolate context. Activates when users ask about context loading, token usage, or how SpecWeave scales. Keywords: context loading, progressive disclosure, token efficiency, sub-agents, context management.
Improve embedded LLM system prompt based on evaluation test failures
Copilot agent that assists with machine learning model development, training, evaluation, deployment, and MLOps Trigger terms: machine learning, ML, AI, model training, MLOps, model deployment, feature engineering, model evaluation, neural network, deep learning Use when: User requests involve ai ml engineer tasks.
AI agent supporting REST/GraphQL/gRPC API design, OpenAPI specification generation, and API best practices Trigger terms: API design, REST API, GraphQL, OpenAPI, API specification, endpoint design, API contract, API documentation, gRPC, API versioning Use when: User requests involve api designer tasks.
Self-improving AI memory system that learns from sessions. Stores learnings in skill-specific MEMORY.md files. Supports cross-platform Claude Code and non-Claude environments. Smart merging preserves user learnings during marketplace updates. Activates for reflect, remember, learn from session, memory, self-improvement, AI memory, correction patterns, skill learning, persistent memory.
Use when facing 3+ independent failures that can be investigated without shared state or dependencies - dispatches multiple Claude agents to investigate and fix independent problems concurrently
Integrated orchestrator agent that manages and coordinates 25 specialized AI agents for Specification Driven Development Trigger terms: orchestrate, coordinate, multi-agent, workflow, execution plan, task breakdown, agent selection, project planning, complex task, full lifecycle, end-to-end development, comprehensive solution Use when: User requests involve orchestrator tasks.
Ralph Loop Best Practices - Guidelines for effective autonomous iteration. Based on Anthropic's official ralph-loop plugin. Helps design prompts for iterative AI development with clear completion signals.
Complete Python Cloudflare deployment system. PROACTIVELY activate for: (1) Python Workers with Pyodide, (2) FastAPI on Workers, (3) Cloudflare Containers for heavy compute, (4) Service bindings (RPC), (5) Environment variables in Workers, (6) Cold start optimization, (7) Workflows for durable execution, (8) GPU containers for AI. Provides: Worker code patterns, wrangler config, Dockerfile templates, FastAPI integration. Ensures edge deployment with proper architecture choices.
SQL Server index design and optimization strategies. Use this skill when: (1) User needs help designing indexes, (2) User asks about clustered vs nonclustered indexes, (3) User wants to optimize columnstore indexes, (4) User needs filtered or covering indexes, (5) User asks about index maintenance and fragmentation.
Prepares ML models for production deployment with containerization, API creation, monitoring setup, and A/B testing. Activates for "deploy model", "production deployment", "model API", "containerize model", "docker ml", "serving ml model", "model monitoring", "A/B test model". Generates deployment artifacts and ensures models are production-ready with monitoring, versioning, and rollback capabilities.
Orchestrates complete machine learning pipelines within SpecWeave increments. Activates when users request "ML pipeline", "train model", "build ML system", "end-to-end ML", "ML workflow", "model training pipeline", or similar. Guides users through data preprocessing, feature engineering, model training, evaluation, and deployment using SpecWeave's spec-driven approach. Integrates with increment lifecycle for reproducible ML development.
Detects when user reports issues with recently completed work and suggests reopening relevant tasks, user stories, or increments. Auto-activates on keywords: not working, broken, bug, issue, problem, failing, error, crash, regression, still broken, incorrect, missing, wrong. Scans active and recently completed (7 days) work to find related items. Provides smart suggestions with relevance scoring.
Model interpretability and explainability using SHAP, LIME, feature importance, and partial dependence plots. Activates for "explain model", "model interpretability", "SHAP", "LIME", "feature importance", "why prediction", "model explanation". Generates human-readable explanations for model predictions, critical for trust, debugging, and regulatory compliance.
Use when creating or developing anything, before writing code - refines rough ideas into bd epics with immutable requirements
