Total 50,487 skills, Code Quality has 2287 skills
Showing 12 of 2287 skills
Review code changes in Tenzir projects. Use when auditing diffs or pull requests for bugs, security issues, missing tests, documentation drift, readability problems, performance regressions, user experience issues, or when deciding how to respond to GitHub review comments. Also use this skill whenever the user says "review", "look at this PR", "check my changes", "audit this diff", "what do you think of this code", or asks for feedback on any code they've written or changed — even if they don't explicitly say "code review."
Knip finds unused files, dependencies, exports, and types in JavaScript/TypeScript projects. Plugin system for frameworks (React, Next.js, Vite), test runners (Vitest, Jest), and build tools. Use when cleaning up codebases, optimizing bundle size, or enforcing strict dependency hygiene in CI.
Cross-language linter autofix commands and common fix patterns for biome, ruff, clippy, shellcheck, and more.
Applicable to code-centric tasks such as coding, debug/debugging, bug fixing, refactor/refactoring, code review, scripting, automation, and implementation planning.
Use this skill for mathematical code verification. Use when reviewing math-heavy code, verifying algorithm correctness, checking numerical stability, aligning with mathematical standards. Do not use when general algorithm review - use architecture-review. DO NOT use when: performance optimization - use parseltongue:python-performance.
Devil's Advocate stress-testing for code, architecture, PRs, and decisions. Surfaces hidden flaws through structured adversarial analysis with metacognitive depth. Use for high-stakes review, stress-testing choices, or when the user wants problems found deliberately. NOT for routine code review (use engineering:code-review). Triggers on "스트레스 테스트", "stress test", "devil's advocate", "반론", "이거 괜찮아", "문제 없을까", "깊은 리뷰", "critical review", "adversarial".
Static code analysis and complexity metrics
Run Python quality checks with ruff, pytest, mypy, and bandit in deterministic order. Use WHEN user requests "quality gate", "lint", "verify code quality", "check python", or "pre-commit check". Use for pre-merge validation, CI/CD gating, or comprehensive code quality reports. Do NOT use for single-tool runs (run tool directly), debugging runtime bugs (use systematic-debugging), refactoring (use systematic-refactoring), or architecture review.
Statistical rule discovery through measurement of Go codebases: Count patterns, derive confidence-scored rules, produce Style Vector fingerprint. Use when analyzing codebase conventions, extracting implicit coding rules, profiling a repo before onboarding or PR automation. Use for "analyze codebase", "find coding patterns", "what conventions does this repo use", "extract rules", or "codebase DNA". Do NOT use for code review, bug fixes, refactoring, or performance optimization.
Evidence-based 4-phase root cause analysis: Reproduce, Isolate, Identify, Verify. Use when user reports a bug, tests are failing, code introduced regressions, or production issues need investigation. Use for "debug", "fix bug", "why is this failing", "root cause", or "tests broken". Do NOT use for feature requests, refactoring, or performance optimization without a specific bug symptom.
Execute a micro-level Flutter code quality audit. Validates code against live GitHub standards for testing, architecture, and code implementation. Produces a detailed violations report with prioritized action plan. Use when the user asks to check Flutter code quality, validate best practices, or review code standards compliance. Triggers on: 'flutter best practices', 'code quality', 'code review', 'flutter standards', 'architecture compliance', 'testing quality'.
Autonomously optimize code for performance using CodSpeed benchmarks, flamegraph analysis, and iterative improvement. Use this skill whenever the user wants to make code faster, reduce CPU usage, optimize memory, improve throughput, find performance bottlenecks, or asks to 'optimize', 'speed up', 'make faster', 'reduce latency', 'improve performance', or points at a CodSpeed benchmark result wanting improvements. Also trigger when the user mentions a slow function, a regression, or wants to understand where time is spent in their code.