Development

Test-driven development (TDD), unit/integration/UAT testing strategies, test organization, coverage requirements, and GenAI validation patterns. Use when writing tests, validating code, or ensuring quality.

State persistence patterns for autonomous-dev including JSON persistence, atomic writes, file locking, crash recovery, and state versioning. Use when implementing stateful libraries or features requiring persistent state.

Patterns for agent skill discovery, referencing, and composition using progressive disclosure architecture. Use when building agents, composing skills, or optimizing context usage. TRIGGER when: skill discovery, agent integration, skill composition, progressive disclosure. DO NOT TRIGGER when: implementing features, writing tests, documentation-only changes.

GenAI-powered semantic validation - detects outdated docs, version mismatches, and architectural drift

Security best practices, API key management, input validation. Use when handling secrets, user input, or security-sensitive code.

4-phase research methodology: codebase recon, targeted web search, deep source analysis, and evidence synthesis. Use when investigating patterns, evaluating libraries, or analyzing best practices. TRIGGER when: research, investigate, evaluate options, compare libraries. DO NOT TRIGGER when: implementation tasks, bug fixes, routine code changes.

Python code quality standards (PEP 8, type hints, docstrings). Use when writing Python code.

PROJECT.md alignment patterns and validation strategies

Semantic validation patterns for PROJECT.md alignment (GOALS, SCOPE, CONSTRAINTS, ARCHITECTURE)

Structured logging, debugging techniques (pdb/ipdb), profiling (cProfile/line_profiler), stack traces, performance monitoring, and metrics. Use when adding logging, debugging issues, or optimizing performance.

Standardized library design patterns for autonomous-dev including two-tier design, progressive enhancement, non-blocking enhancements, and security-first architecture. Use when creating or refactoring Python libraries.

Documentation standards and automation. Use when updating docs, writing guides, or synchronizing code with documentation.

Documentation consistency enforcement - prevents drift between README.md and actual codebase state. Auto-activates when updating docs, committing changes, or working with skills/agents/commands.

This skill should be used when reviewing code or preparing code for review. It provides guidelines for what to look for in reviews, how to write constructive feedback, and standards for review comments.

This skill should be used when designing system architecture, making architectural decisions, or evaluating design patterns. It provides guidance on common patterns, ADR templates, design principles, and tradeoff analysis.

API integration patterns for autonomous-dev including subprocess safety, GitHub CLI integration, retry logic, authentication, rate limiting, and timeout handling. Use when integrating external APIs or CLI tools.

REST API design best practices, versioning strategies, error handling, pagination, and OpenAPI documentation. Use when designing or implementing REST APIs, HTTP endpoints, or API documentation.

Standardized output formats for research, planning, implementation, and review agents. Use when generating agent outputs or parsing agent responses.

Detects when user requests warrant critical analysis via /advise command

Project-specific context including project nature, technology stack, and implementation principles. Customize for each project.

Defines environment variables, architecture design, and build/test commands. Use when configuring environment or designing architecture.

Detects code smells, anti-patterns, and readability issues. Use when implementing features, reviewing code, or refactoring.

ユーザーのスキル変更要求を最適化パターン評価付きで実装

Implement user skill change requests with optimization pattern evaluation