ai-coding-config

Triage and address PR comments from code review bots - analyzes feedback, prioritizes issues, fixes valid concerns, and declines incorrect suggestions

Set up or update AI coding configurations - interactive setup for Claude Code, Cursor, and other AI coding tools

Execute development task autonomously from description to PR-ready - handles implementation, testing, and git workflow without supervision

Clean up a git worktree after its PR has been merged - verifies merge status and safely removes the worktree directory

Create optimized prompts following prompt-engineering principles - uses LLM mechanics and pattern reinforcement for effective prompts

Generate or update AGENTS.md with project context for AI assistants - creates universal context for Claude Code, Cursor, Copilot

Generate or update llms.txt to help LLMs understand your site - standardized file for AI assistants to navigate documentation

Generate context handoff and copy to clipboard for new session - preserves decisions, progress, and next steps across conversations

AI Product Manager - maintain living product understanding, keep docs/knowledge/ current as single source of truth

Load relevant coding rules for the current task - analyzes context and loads only needed rules from rules/

Multi-agent code review with diverse perspectives - run multiple specialized reviewers in parallel for comprehensive analysis

Change or activate a personality for both Cursor and Claude Code - syncs personality across tools with alwaysApply frontmatter

Research product intelligence on competitors and industry trends - analyzes features, positioning, and opportunities

Set up repos with linting, formatting, and CI/CD - configures ESLint, Prettier, Husky, Ruff based on detected language

Save and resume development sessions across conversations - preserves context, decisions, and progress for continuity

Initialize development environment for git worktree - creates worktree, installs deps, validates setup for productive work

Autonomous production error resolution system - analyzes, prioritizes, and fixes errors from Sentry or error logs

Verify a fix actually works before claiming success - runs tests, checks live behavior, confirms fixes work from user perspective

When creating custom agent files in `.claude/agents/`, the YAML frontmatter format

Use when reviewing architecture, checking design patterns, auditing dependencies, or catching structural problems before they multiply

Use when completing tasks autonomously, working independently, delivering PR-ready code, or needing end-to-end implementation without supervision

Use when reviewing comments, checking docstrings, auditing documentation accuracy, or finding stale/misleading comments in code

Use when debugging errors, investigating failures, finding root causes, or tracing unexpected behavior to its source

Use when reviewing frontend design, checking UI quality, auditing visual consistency, or verifying responsive behavior across viewports

Use when reviewing UX, user experience, interfaces, user-facing features, or need empathy/design perspective on code changes

Use when reviewing error handling, finding silent failures, checking try-catch patterns, or ensuring errors surface properly with actionable feedback

Use when writing commit messages, creating PR descriptions, naming branches, or needing git messages that explain why changes were made

Use when choosing libraries, evaluating npm packages, deciding build vs buy, researching technology choices, or comparing library options

Use when reviewing for logic bugs, edge cases, off-by-one errors, race conditions, or finding correctness issues before users do

Use when reviewing mobile UX, checking responsive design, testing touch interactions, or verifying mobile layouts work on phones and tablets

Use when reviewing logging, checking error tracking, auditing monitoring patterns, or ensuring production issues are debuggable at 3am

Use when reviewing performance, finding N+1 queries, checking algorithmic complexity, or catching efficiency problems before production

Use when writing prompts, creating agent instructions, designing system prompts, or crafting LLM-to-LLM communication

Use when reviewing for production readiness, fragile code, error handling, resilience, reliability, or catching bugs before deployment

Use when reviewing security, checking for injection flaws, auditing authentication, or finding OWASP vulnerabilities before attackers do

Use when auditing SEO, improving search rankings, implementing structured data, or optimizing for organic traffic and Core Web Vitals

Use when simplifying code, reducing complexity, eliminating redundancy, or making code more readable without changing behavior

Use when monitoring production health, triaging errors, running reliability checks, or catching problems before they impact users

Use when reviewing code style, checking naming conventions, auditing project patterns, or ensuring consistency with codebase conventions

Use when analyzing test coverage, reviewing test quality, finding coverage gaps, or identifying brittle tests that test the wrong things

Use when writing tests, generating test coverage, creating unit/integration tests, or ensuring code is proven to work before shipping

Use when running tests, checking test results, getting pass/fail status, or needing terse output that preserves context

Use when designing user interfaces, writing user-facing content, crafting error messages, or making experiences feel obvious and polished

Use when facing hard architectural decisions, multiple valid approaches exist, need diverse perspectives before committing, or want M-of-N synthesis on complex problems

Use when rough ideas need design before code, requirements are fuzzy, multiple approaches exist, or you need to explore options before implementation

Use when automating browsers, testing pages, taking screenshots, checking UI, verifying login flows, or testing responsive behavior

Use when current web info needed, verifying APIs still work, checking latest versions, or avoiding outdated implementations

Use when creating skills, writing SKILL.md files, editing skill definitions, or adding new reusable techniques to ai-coding-config

Use when debugging bugs, test failures, unexpected behavior, or needing to find root cause before fixing

Use when analyzing YouTube videos, extracting insights from tutorials, researching video content, or learning from talks and presentations