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Found 633 Skills
Naming patterns and conventions based on Clean Code JavaScript principles.
Comprehensive multi-stage code review using specialized subagents. Use when reviewing PRs with complex architectural impact, security concerns, or when thorough multi-perspective analysis is needed.
Executes comprehensive PR reviews following Freenet standards. Performs four-perspective review covering code-first analysis, testing, skeptical review, and big-picture assessment.
Code Reviewer Specialist. Use this to review PRs, check security, and ensure code quality standards before merging.
Reduce cyclomatic complexity with targeted refactoring strategies
Three-lens code review using parallel subagents: Epimetheus (hindsight — bugs, debt, fragility), Metis (craft — clarity, idiom, fit-for-purpose), Prometheus (foresight — vision, extensibility, future-Claude). Triggers on /titans, /review, 'review this code', 'what did I miss', 'before I ship this'. Use after completing substantial work, before /close. (user)
AI-powered systematic codebase analysis. Combines mechanical structure extraction with Claude's semantic understanding to produce documentation that captures not just WHAT code does, but WHY it exists and HOW it fits into the system. Includes pattern recognition, red flag detection, flow tracing, and quality assessment. Use for codebase analysis, documentation generation, architecture understanding, or code review.
Comprehensively reviews Python libraries for quality across project structure, packaging, code quality, testing, security, documentation, API design, and CI/CD. Provides actionable feedback and improvement recommendations. Use when evaluating library health, preparing for major releases, or auditing dependencies.
Initialize repo-scoped code review policy files under .opencode/review. Use when setting up project-specific review rules for /code-review.
SOLID principles for React 19. Files < 100 lines, hooks separated, interfaces in src/interfaces/, JSDoc mandatory. Use for React architecture and code quality.
Removes AI writing artifacts from documentation and code. Use when editing LLM-generated prose, reviewing READMEs, polishing docs before publishing, or cleaning up AI-generated code. Use for emdash cleanup, formulaic phrase removal, tone calibration, over-commented code, verbose naming, and AI code smell detection.
Bootstrap new projects with strong typing, linting, formatting, and testing. Supports Python, TypeScript, and other languages with research fallback.