Total 55,449 skills, Code Quality has 2491 skills
Showing 12 of 2491 skills
Critically assess external feedback (code reviews, AI reviewers, PR comments) and decide which suggestions to apply using a confidence-based framework with adversarial verification. Use when the user asks to "evaluate findings", "assess review comments", "triage review feedback", "evaluate review output", or "filter false positives".
Master Git hooks setup with Husky, lint-staged, pre-commit framework, and commitlint. Automate code quality gates, formatting, linting, and commit message enforcement before code reaches CI.
Provides general code quality and best practices guidance applicable across languages and frameworks. Focuses on linting, testing, and type safety.
Use this skill when profiling application performance, debugging memory leaks, optimizing latency, benchmarking code, or reducing resource consumption. Triggers on CPU profiling, memory profiling, flame graphs, garbage collection tuning, load testing, P99 latency, throughput optimization, bundle size reduction, and any task requiring performance analysis or optimization.
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.
Systematic detection and prioritization of neglected code quality issues: stale TODOs, unused imports, deprecated functions, high complexity, dead code. Use when user requests "code cleanup", "find TODOs", "technical debt scan", or "quality of life fixes". Do NOT use for bug fixing (use systematic-debugging), feature work (use test-driven-development), or formatting-only (use code-linting).
Review pull requests for the MiniMax Skills repository. Use when reviewing PRs, validating new skill submissions, or checking existing skills for compliance. Run the validation script first for hard checks, then apply quality guidelines for content review. Triggers: PR review, pull request, validate skill, check skill.
Verify a spec-driven change is complete and correctly implemented. Checks task completion, implementation evidence, and spec alignment.
Execute a micro-level NestJS code quality audit. Validates code against live GitHub standards for testing, architecture, DTO validation, error handling, and code implementation. Produces a detailed violations report with prioritized action plan. Use when the user asks to check NestJS code quality, validate best practices, or review backend code standards. Triggers on: 'nestjs best practices', 'backend code quality', 'code review', 'nestjs standards', 'dto validation', 'error handling review'.
Execute a micro-level React code quality audit. Validates code against live GitHub standards for testing, component architecture, hooks patterns, state management, performance, and TypeScript. Produces a detailed violations report with prioritized action plan. Use when the user asks to check React code quality, validate best practices, or review frontend code standards. Triggers on: 'react best practices', 'react code quality', 'component review', 'hooks review', 'react standards', 'frontend code quality'.
WordPress performance code review and optimization analysis. Use when reviewing WordPress PHP code for performance issues, auditing themes/plugins for scalability, optimizing WP_Query, analyzing caching strategies, checking code before launch, or detecting anti-patterns, or when user mentions "performance review", "optimization audit", "slow WordPress", "slow queries", "high-traffic", "scale WordPress", "code review", "timeout", "500 error", "out of memory", or "site won't load". Detects anti-patterns in database queries, hooks, object caching, AJAX, and template loading.
Use when you need to review, improve, or refactor Java code for generics quality — including avoiding raw types, applying the PECS (Producer Extends Consumer Super) principle for wildcards, using bounded type parameters, designing effective generic methods, leveraging the diamond operator, understanding type erasure implications, handling generic inheritance correctly, preventing heap pollution with @SafeVarargs, and integrating generics with modern Java features like Records, sealed types, and pattern matching. Part of the skills-for-java project