Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use when designing, importing, querying, or modernizing Neo4j knowledge graphs from CSV, Excel, pandas, Cypher, py2neo, the official neo4j Python driver, vector indexes, or GraphRAG workflows.
Step-by-step workflow for converting bloated command files to lean declarative definitions
Applies the Refactoring Priority Premise (RPP) levels L1-L6 for systematic code refactoring. Use when improving code quality through structured refactoring passes.
Speculative parallel implementation methodology — dispatch N candidate implementations, audit all, score, pick best. Auditability mandate: ALL candidates logged (not just winner).
Runs a timeboxed PROBE to validate one core assumption, then optionally PROMOTES the probe into a walking skeleton — the first e2e thin slice of the feature, committed and demo-able. Use after DISCUSS when the feature involves a new mechanism, performance requirement, or external integration.
Port the state-delta + property-based testing paradigm to languages other than Python. DIY recipes per language; canonical Python ref shipped in nwave_ai.state_delta.
Shared rules for feature ID derivation and wave detection used by /nw-new, /nw-continue, and /nw-fast-forward wizards
Archives a completed feature to docs/evolution/, migrates lasting artifacts to permanent directories, and cleans up the temporary workspace. Use after all implementation steps pass and mutation testing completes.
Backfill memory/topics into an OKF-conformant bundle by adding type frontmatter, then open a PR