Total 30,671 skills, Code Quality has 1620 skills
Showing 12 of 1620 skills
Review code for bugs, security vulnerabilities, performance issues, accessibility gaps, and CLAUDE.md workflow compliance. Supports any tech stack - HTML/CSS/JS, React, TypeScript, Node.js, Python, NestJS, Next.js, and more. Use when completing features, before commits, or reviewing pull requests.
Orchestrate comprehensive code review across ~12 AI reviewers. 5 persona reviewers (Grug, Carmack, Ousterhout, Beck, Fowler) via Moonbridge, 4 domain specialists (security-sentinel MANDATORY, performance, data integrity, architecture) via Task, plus hindsight-reviewer and synthesis. Use when: code review, PR review, pre-merge quality check.
Generate custom lint rules from architectural patterns. ESLint local plugins (JS/TS) or ast-grep YAML rules (Python/Go/Rust/any). Invoke when: codifying an import boundary, enforcing API conventions, blocking deprecated patterns, or any "always/never" constraint.
Elevate a working PR: hindsight review, refactor, simplify, test audit, docs, quality gates. Composes: hindsight-reviewer agent, /refactor, /simplify, /update-docs, /check-quality, /distill. Use when: PR works but could be better. "How would we do this knowing what we know now?"
Unblock a PR: resolve conflicts, fix CI, address reviews. Composes: git-mastery conflict resolution, /fix-ci, /respond, /address-review. Use when: PR is blocked by conflicts, red CI, or unaddressed review feedback.
Full code review, fix, quality, PR workflow. Chains review-branch, address-review, check-quality, and pr. Use when: code complete and ready for PR, want comprehensive review before shipping.
Diagnose and fix errors with Codex delegation. Traces error to root cause, researches approach, delegates fix, verifies. Use when: bug reports, error messages, stack traces, test failures.
Evaluate and improve code modularization using the Balanced Coupling Model. Analyzes coupling strength, connascence types, and distance to identify refactoring opportunities and architectural improvements. Use when reviewing code architecture, refactoring modules, or designing new systems.
Systematic code refactoring following Martin Fowler's catalog. Methodologies: characterization tests, Red-Green-Refactor, incremental transformation. Capabilities: SOLID compliance, DRY cleanup, code smell detection, complexity reduction, legacy modernization, design patterns, functional programming patterns. Actions: refactor, extract, inline, rename, move, simplify code. Keywords: refactor, SOLID, DRY, code smell, complexity, extract method, inline, rename, move, clean code, technical debt, legacy code, design pattern, characterization test, Red-Green-Refactor, functional programming, higher-order function, immutability, pure function, composition, currying, side effects. Use when: improving code quality, reducing technical debt, applying SOLID principles, fixing DRY violations, removing code smells, modernizing legacy code, applying design patterns.
Code review practices with technical rigor and verification gates. Practices: receiving feedback, requesting reviews, verification gates. Capabilities: technical evaluation, evidence-based claims, PR review, subagent-driven review, completion verification. Actions: review, evaluate, verify, validate code changes. Keywords: code review, PR review, pull request, technical feedback, review feedback, completion claim, verification, evidence-based, code quality, review request, technical rigor, subagent review, code-reviewer, review gate, merge criteria. Use when: receiving code review feedback, completing major features, making completion claims, requesting systematic reviews, validating before merge, preventing false completion claims.
Use when reviewing Rust code for craft quality, when writing new Rust code that should follow professional patterns, or when the user asks to judge, audit, or improve Rust code against best practices. Covers type design, function signatures, trait architecture, error handling, visibility, macros, testing, and performance patterns.
This skill should be used when the user asks to "remove AI slop", "clean up AI code", "remove AI patterns", "fix AI-generated code", "clean up PR", "remove unnecessary comments", "fix defensive checks", or mentions AI slop, AI code cleanup, or code quality issues from AI-assisted development. Identifies and removes unnecessary comments, defensive checks, type casts to any, and style inconsistencies.