expansion

Graceful context transfer before session end or compaction. Commits work, documents pending threads, captures learnings, and prepares the next instance to continue seamlessly.

Promote a session's durable cross-project learnings to the shared union store (~/.claude-sync — the MISTAKES.md ledger, LIBRARY.md and its Index in SHARED-MEMORY.md, and the SHARED-MEMORY.md Journal) and to per-project auto-memory. This is the /meta-update ritual.

Meta-skill for learning from skill usage. Tracks uncertainties, human interventions, and gaps discovered during use, then proposes concrete skill patches.

Audit the QUALITY of an existing test suite (not just its existence) — finds tests that can't fail, change-detector/over-mocked tests, unfocused tests, unreadable tests, unactionable failures, weak test values, non-hermetic tests, and risk/coverage gaps. Grounded in the Google Testing Blog / "Testing on the Toilet" canon. Use after a test sprint, before trusting a suite, or when green tests still ship bugs.

Generate Obsidian Canvas (.canvas) files for UX flows, data flows, and testing infrastructure visualization

Systematic codebase review for technical debt - pattern conflicts, test gaps, band-aid fixes, security issues, and documentation holes. Use after rapid development sprints or before major refactors.

Use when docs may have drifted from code, after development sprints, before onboarding, or when unsure if documented guarantees still hold. Surfaces stale docs, undocumented modules, naming conflicts, invalidated proofs, and convention violations.

Use when a codebase property needs a formal guarantee — mathematical invariants, security boundaries, data privacy, normalization correctness. Creates ASSERTION proof files citing specific code lines, and re-verifies them when code changes.

Use when doc-audit surfaces gaps, when a new module needs documentation, when design rationale needs capturing, or when CONVENTIONS.md needs creation. Writes module docs with staleness markers, design rationale, and bottom-up decision capture.

Use after discovering bugs that may have silently caused data loss or degradation. Traces root cause through git history, identifies human-agent miscommunication, then systematically searches for downstream damage.