Gives the agent up-to-date documentation and code examples for libraries and frameworks, fetched on demand so answers are not stuck at the model's training cutoff. Runs locally from the @upstash/context7-mcp npm package.
Install the Claude Code Framework into the current repo by cloning it from GitHub (no local clone path needed). Global/account-level skill. Distinct from the per-repo /setup, which refines an already-installed framework.
Add or update knowledge reference files for skills — domain knowledge (business rules, external systems), codebase inventories (entities, classes, APIs), or both. Use when documenting knowledge for AI context.
Automated readiness gate for tickets before implementation. Scans for TBDs, vague acceptance criteria, missing specs, unresolved questions, and integration gaps. Use before /develop to catch incomplete tickets early.
Orchestrate deployments to target environments. Handles merging PRs, deploying, posting notifications, and updating work items. Use when deploying to staging/UAT/production.
Full development cycle - fetches ticket from work item tracker, analyzes, implements, validates, fixes build errors, and creates PR. Use when developing a ticket end-to-end.
Create new tickets from business requirements or design documents. Analyzes requirements, scans codebase, identifies gaps and open questions, and drafts complete refined tickets.
Triage production errors from your monitoring system. Queries new errors, groups patterns, analyzes root cause with codebase context, and lets developer create tickets or dismiss. Supports unattended CI mode.
AI Software Factory — end-to-end pipeline from approved ticket to deployed test environment. Chains readiness gate → develop (factory mode) → PR → CI deploy. Reduces human touchpoints from 5+ to 2.
Fetch library/framework documentation via context7 or web search and persist it to the knowledge base. Automatically invoked when documentation is needed for implementation tasks.
On-demand precise impact analysis for a symbol or file before changing it. Spawns impact-analyzer to grep callers, classify the cascade (direct/indirect/test-only), and produce a confidence-scored report. Different from .claude/state/architecture.md (broad map) — this is the focused per-symbol drill-down.
Build or refresh a vector index of the codebase at .claude/state/codebase.db for semantic search via /search. Opt-in for large codebases (>50k LOC); small projects get sufficient awareness from .claude/state/architecture.md + Grep.
Plan → code → review → re-code feedback loop with persistent state across iterations. Spawns the review-coordinator, tracks findings across pushes, dedupes against prior iterations, and respects user "won't fix" decisions.
AI-powered merge conflict resolution. Reads both features' story docs to understand intent, resolves conflicts per file type, validates merged result. Works for any codebase.
Create mock definitions for external APIs and services. Generates contract files, test fixtures, mock servers, and integration wrappers so AI can safely develop and test against external dependencies without hitting real services.
Invoke the planner-coordinator on a story or task — spawns planning specialists in parallel (requirements-clarifier, scope-decomposer, risk-assessor, test-strategy-planner, plus architect/api-designer/database-architect when relevant) and produces .claude/state/plan- .md.
Ticket refinement and gap analysis - analyzes existing tickets, identifies gaps, scans codebase, and generates refined documentation. Use when improving existing tickets or doing a quick gap check.
Semantic search across the indexed codebase. Returns top-K matching chunks with file:line + similarity score. Requires /index to have been run first. Useful for "where do we handle X?" questions that grep can't answer because there's no exact-string match.
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