Total 44,072 skills, Code Quality has 2066 skills
Showing 12 of 2066 skills
Core code quality principles for writing maintainable code. Use when reviewing code for design violations, assessing code quality, or applying architectural patterns like composition, immutability, and fail-fast.
Auto-selects best Kaizen method (Gemba Walk, Value Stream, or Muda) for target
Initialize repo-scoped code review policy files under .opencode/review. Use when setting up project-specific review rules for /code-review.
This skill should be used when fixing bugs, implementing features, debugging issues, or making code changes. Ensures understanding of code flow before implementation by: (1) Tracing execution path with specific file:line references, (2) Creating lightweight text diagrams showing class.method() flows, (3) Verifying understanding with user. Prevents wasted effort from assumptions or guessing. Triggers when users request: bug fixes, feature implementations, refactoring, TDD cycles, debugging, code analysis.
Standards for code linting, formatting, and pre-commit hooks.
Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification
Transform code into clean, testable architecture using SOLID principles, Clean Architecture, and proven design patterns
Optimize application performance and scalability. Use when investigating slow applications, scaling bottlenecks, or improving response times. Use for profiling, caching, database optimization, frontend performance, and backend tuning.
SOLID principles for React 19. Files < 100 lines, hooks separated, interfaces in src/interfaces/, JSDoc mandatory. Use for React architecture and code quality.
Run a full-scale implementation review with parallel subagents for plan alignment, UI verification, technical and strategic analysis, and test coverage gap closure across app and database layers.
Review PR comments, discuss improvements, and reply with resolution status
CI-only Simplify & Harden workflow for pull requests using gh-aw (GitHub Agentic Workflows). Runs headless scan-and-report checks for simplify/harden/document, posts structured findings, and can block merges on critical or advisory classes. Use when: you want automated quality/security review in CI without interactive approvals.