All Skills

Semantic-search personal knowledge (memory, plans, handoffs, skills, Codex rules) via the local RAG index at ~/.claude/rag-index/. Use when a query is fuzzy or cross-file ("how did we fix X", "what did we decide about Y", "which skill handles Z"). Complements grep (exact) and Serena (code symbols). If the user asks a recall question that doesn't map to a specific known file, reach here first.

Systematic root-cause analysis for bugs, errors, and unexpected behavior

Composite skill — emergency bypass of the release branch when production is broken and waiting for the next release-cut is not viable. Chains incident scoping → branch from main (NOT release) → minimal fix → pr-merge-readiness → merge to main → patch-version tag → deploy → cherry-pick to release branch → post-deploy verify (sentry + ci-watch). Use ONLY for production-impacting breakage; routine "small fix" work belongs in /pr-to-release.

Build and debug Retrieval-Augmented Generation pipelines — chunking, embedding, retrieval, reranking

Test generation is repetitive but critical. Developers skip tests because writing edge cases is tedious. AI agents can generate tests quickly but often produce brittle, unrealistic tests that provide false confidence. Tests that mock internals, use fake data like "foo@example.com", or test implementation details break on every refactor.

spec-ship

Default execution rhythm — inspect → act → verify → checkpoint — applied iteratively until the task is done or a clear blocker emerges. Use when working through a known plan, draining a queue (PRs, issues, dependency bumps), or executing a multi-step recipe where each step's output informs the next. Each iteration must produce a concrete artifact (commit, comment, decision) and a one-line state update. Stop on first unrecoverable error rather than retrying blindly. Pair with `ship` for merge-bound work and `handoff` if budget runs out mid-loop.

Build a compact, validation-gated implementation plan for multi-step, risky, or ambiguous work — typically when phases need explicit checkpoints, the change touches multiple files/services, or the user wants alignment before execution. Output goes to `.claude/plans/<topic>.md` (or `.agents/plans/`) with goal, in/out-of-scope, phased steps, validation per phase, and replanning triggers. Skip for trivial single-file fixes, bug investigations (use `tracer`), or open-ended exploration (use `explore`). Pair with `ship` once the plan ends and merge work begins.

Commit, push, and open a pull request with a conventional commit message

Auto-extract reusable patterns from the current session into skills or memory

Route a task to the most fitting skill or agent. Parses user intent, checks installed-skill inventory, and picks the best match (single skill or chain).

Read-only diagnostic that scans Claude Code session transcripts to surface which MCP servers and tools you actually use, ranked by call frequency, with zero-use servers flagged for removal. Use when planning an MCP cleanup, evaluating whether to keep a newly-added server, deciding which servers warrant token cost in the catalog, or before authoring an MCP-removal PR. Outputs a markdown report (last N days) — does not modify any settings. Pair with the manual `claude mcp remove <name>` step once findings are reviewed.

Shortcut for security review on current change set. Runs layered checks (secret-scan, dep-audit, semgrep, OWASP patterns, prompt-injection review).

Assemble a Jira/Linear/GitHub-Issue body from recent commits, diff, and branch context

Run full quality gate suite before committing or creating a PR

Decompose a task into parallel workstreams, assign agent ownership, run integration at dependency boundaries, and synthesize results.

Code review is time-consuming and inconsistent. Reviewers miss security issues, overlook edge cases, or focus on style over substance. Meanwhile, AI agents catch these mechanical issues instantly but can't judge business logic correctness, UX decisions, or architectural fit.

Composite skill — promote the long-lived `release` branch to `main`, cut a single version that batches every PR accumulated since the last tag, then clean up. Chains pr-merge-readiness (on release→main PR) → version-bump (semver from commits) → changelog-update (promote [Unreleased] → versioned) → ship (tag + GitHub release) → stale-branch cleanup → optional ship-it for deploy. Use this INSTEAD of releasing every small fix individually. Many small commits become one well-scoped release.

Inspect current CI state, identify the first real blocker, separate required failures from noise, and monitor until checks settle. Use when checks are failing, flaky, or blocking a merge.

Graceful degradation when primary approach fails. Try alternates in priority order; record which path succeeded for future routing.

Optimize context usage for the session by reducing bloat, improving token efficiency, and focusing on relevant areas. Use when context is growing large, responses are slow, or switching between unrelated tasks. Covers context analysis, compacting, targeted pruning, MCP-first strategies, and focused scoping by area.

UI/UX design system reference, pattern library, and focused review guidance for web and mobile applications. Use for targeted design research, pattern selection, UX audits, accessibility guidance, palette and typography selection, layout heuristics, and component pattern research. Covers 50+ styles, 97 color palettes, 57 font pairings, Web Interface Guidelines compliance, and concrete visual/usability improvements for modern SaaS and product interfaces.

Meta-skill codifying the no-wakeup-polling pattern. Describes the ScheduleWakeup anti-pattern, the fix (--auto merge + detached watcher), and when to still use wakeups. Reduces handoff spam by 80%.

Diagnose and fix macOS resource pressure for Claude Code workflows. Use when load avg is high, swap is saturated, CC feels slow, or before spinning up parallel agents/worktrees. Covers CPU top-talkers, swap pressure, zombie claude processes, Node heap tuning, Spotlight/background-agent pruning, and purge. Apple Silicon aware.

Detect and fix stale chunks (files that changed or were deleted since last indexing)

Autonomous error recovery — detect failures, diagnose root cause, apply fixes, and resume without stopping

AI coding agents fail in consistent, predictable ways. They fabricate npm packages that don't exist. They catch errors and continue silently. They add features you never asked for. They lose context mid-session and forget what they were doing.

Composite skill — full project health check across testing, config, hooks, performance, security, MCP, and plugins. Runs the audit skills in parallel and reconciles into one severity-ranked report with prioritized remediation plan. Use weekly per active project, before major releases, or as part of quarterly tech-debt review.

Git-level WIP safety net. Stashes uncommitted work, tags, and pushes to remote

Composite skill — full debugging workflow from "this is broken" to root cause and fix. Chains systematic-debugging (root-cause hypotheses) → tracer agent (evidence walk) → sentry (production correlation if applicable) → ci-watch (regression check) → incident-response (if production-impacting). Use when a bug needs deep investigation, not just a quick fix.

Verify work before committing, pushing, or requesting review

Recover a PR whose head SHA disagrees with its branch ref (webhook desync). Force-push and close+reopen rarely fix it — recreate the PR.

Composite skill — one-pass cleanup of stale local branches, dead worktrees, merged branches, and abandoned remote PR branches. Chains `git fetch --prune` → `clean_gone` (kill [gone] branches) → worktree prune + offer-to-remove dead worktrees → list-and-delete branches merged to main and release → delete remote PR branches whose PRs merged >7 days ago. Use instead of running `clean_gone` alone — that only catches half the rot. Daily-friction composite; fires on "clean up branches", "branch hygiene", "stale worktrees", and on session start when local branch count > 30.

Orchestrate teams of agents — DAG execution, routing, state sharing, failure recovery. Use when coordinating parallel agents in task pipelines with dependencies or distributing work by capability.

Coordinate multiple subagents/worktrees for parallel workstreams. Decomposes larger tasks into independent sub-tasks, dispatches each to a dedicated agent.

Examine what's actually stored in the index for specific items