Total 50,510 skills, Code Quality has 2287 skills
Showing 12 of 2287 skills
Review existing code, diffs, branches, or pull requests using concern-specific reviewer personas and evidence. Use when auditing someone else's work, triaging risk in a PR, or producing a ship-it / needs-review / blocked verdict. Do not use to verify your own completed change; use `verify` for that.
Guide discovery through questioning techniques and pattern recognition for Clean Code, GoF design patterns, and architectural decisions. Use when coaching developers, facilitating design discussions, or helping teams discover solutions.
MUST be used whenever reviewing a Dune app for bugs, missing error states, unhandled promise rejections, or incorrect edge-case behaviour. Do NOT skip — run every step when the user asks for a correctness review, bug check, error handling audit, or robustness review. Triggers: correctness, error handling, bug, edge case, crash, unhandled, null, undefined, empty state, loading state, error boundary, try catch, async error, useEffect cleanup, type guard, runtime error, robustness.
MUST be used whenever reviewing a Dune app for code quality, maintainability, or clean code issues — before a PR review, after a feature is complete, or when the user asks for a code review. Do NOT skip linting steps. Triggers: code quality, code review, clean code, refactor, maintainability, technical debt, any type, naming, dead code, duplication, DRY, single responsibility, component size, lint, linting, TypeScript strict, dependency injection, file structure.
Perform a thorough quality review of a pull request or feature branch before merging. Use this skill whenever the user asks to review a PR, check if code is production-ready, assess quality, verify docs are updated, or asks "is this ready to merge?", "review this PR", "check quality", "is this production ready?", or similar. Also use when reviewing your own work before submitting.
Fix a bug with systematic debugging, TDD, and PR workflow
Ultra-lightweight channel for refactor processes - used when changes are obviously too small to justify the full scan → design → apply three-stage workflow. AI directly identifies 1-3 low-risk optimization points, confirms with the user once, modifies in-place using classic methods, and validates itself by running tests. No scan checklist, no design documentation, no multi-step HUMAN verification required. Trigger scenarios: When the user says "quick refactor", "small refactor", "simply optimize XX function", "modify directly", "skip all those steps", and the scope of changes is clearly limited to a single function/single component, with tests available for self-validation.
Organize reusable programming patterns / library usages / technical skills that answer "To do this kind of thing, the correct approach is this" into a prescriptive reference library, which can be retrieved and reused as needed during feature-design and issue-analyze phases. Three types: pattern (design patterns, programming idioms), library (usage and pitfalls of a certain library/framework), technique (specific operation skills / command recipes). Trigger scenarios: When the user says "record a trick", "this usage is worth recording", "tricks", "record library usage", or when a skill worth archiving is discovered during feature-design / issue-analyze phases and actively pushed. Refer to `codestable/reference/system-overview.md` for how to distinguish it from learning / decisions / explore.
Phase 2 of the feature workflow —— Write code according to the implementation sequence in {slug}-design.md, and submit a completion report in a unified format for user review after finishing. Prerequisites: {slug}-design.md has been approved (standard design includes test design, or fastforward design includes acceptance criteria), and {slug}-checklist.yaml exists in the same directory. Trigger scenarios: User says "The plan is confirmed, start implementation", "Write code according to the plan", "Start working". If you encounter situations not covered by the plan during implementation (new concepts, out-of-scope files, need for patch branches), proactively stop and go back to discuss the plan instead of pushing forward blindly.
Ultra-lightweight channel for refactor processes - used when changes are clearly too small to go through the full scan → design → apply three-stage workflow. AI directly identifies 1-3 low-risk optimization points, confirms with the user once, modifies in-place using classic methods, and validates itself by running tests. No scan checklist, no design documentation, no multi-step human verification required. Trigger scenarios: User says "quick refactor", "small refactor", "simply optimize XX function", "modify directly", "skip the extra steps", and the scope of changes is clearly localized to a single function / single component with test coverage for self-validation.
Reviews code for quality, security, tests, and project standards (PEP 8, type hints, VERSION, Docker, funcoes.md). Use when reviewing pull requests, code changes, or when the user asks for a code review or quality check.
Code review and audit system with specialized sub-skills covering SOLID principles, security, performance, architecture, error handling, testing, code smells, design patterns, and framework best practices. Generates severity-scored findings with copy-pasteable fix prompts. Strictly read-only — never modifies user code. Use when user says "review", "audit", "code review", "check my code", "security scan", "code smells", "SOLID check".