Loading...
Loading...
Found 1,194 Skills
Analyze codebases for anti-patterns, code smells, and quality issues using ast-grep structural pattern matching. Use when reviewing code quality, identifying technical debt, or performing comprehensive code analysis across JavaScript, TypeScript, Python, Vue, React, or other supported languages.
Python code quality with ruff linter. Fast linting, rule selection, auto-fixing, and configuration. Use when checking Python code quality, enforcing standards, or finding bugs.
Guide for quality focused software architecture. This skill should be used when users want to write code, design architecture, analyze code, in any case that relates to software development.
Linting workflows with neostandard and ESLint v9 flat config
Naming patterns and conventions based on Clean Code JavaScript principles.
Three-lens code review using parallel subagents: Epimetheus (hindsight — bugs, debt, fragility), Metis (craft — clarity, idiom, fit-for-purpose), Prometheus (foresight — vision, extensibility, future-Claude). Triggers on /titans, /review, 'review this code', 'what did I miss', 'before I ship this'. Use after completing substantial work, before /close. (user)
This skill should be used when cleaning up codebases that have accumulated dead code, redundant implementations, and orphaned artifacts — especially codebases maintained by coding agents. Triggers on "find dead code", "clean up unused code", "remove redundant code", "prune this codebase", "dead code sweep", "code cleanup", or when a codebase has gone through multiple agent-driven refactors and likely contains overlooked remnants. Systematically identifies cruft, categorizes findings, and removes confirmed dead code with user approval.
Use this when the user asks to refactor, clean up, optimize, or improve code quality.
Universal coding standards, best practices, and patterns for TypeScript, JavaScript, React, and Node.js development.
Analyzes code based on John Ousterhout's "A Philosophy of Software Design". Identifies unnecessary complexity, shallow modules, information leaks, and design problems. Use when reviewing architecture, PRs, refactoring, or asking about code quality.
Verify completed implementation against the plan. Checks that all tasks were fully implemented, nothing was forgotten, code compiles, tests pass, and quality standards are met. Use after "/aif-implement" completes, or when user says "verify", "check work", "did we miss anything".
Captures quality metrics baseline (tests, coverage, type errors, linting, dead code) by running quality gates and storing results in memory for regression detection. Use at feature start, before refactor work, or after major changes to establish baseline. Triggers on "capture baseline", "establish baseline", or PROACTIVELY at start of any feature/refactor work. Works with pytest output, pyright errors, ruff warnings, vulture results, and memory MCP server for baseline storage.