STOP. READ THIS ENTIRE SKILL.MD BEFORE CALLING ANY ENDPOINT.

ingest-code

Codebase ingestion into /memory:

Phase 1 — Functional Knowledge: Python AST extracts module docstrings, function signatures, class hierarchies. Markdown parser extracts section-level knowledge from CONTEXT.md, README.md, etc. Generic parser handles TS/JS exports.
Phase 2 — CWE Scanning: /taxonomy extracts security-relevant patterns (bridge tags + CWE mappings) per file.
Phase 3 — Relationship Edges: Python import analysis stores resolved code dependency edges in /memory; the local code-graph bundle also records typed import/call/inheritance occurrences, including inactive unresolved and ambiguous candidates for audit.
Phase 4 — Structured Code Index: Optional Tree-sitter extraction emits a complete code-graph bundle and applies it through Memory/GMO's governed /code/projection/apply lifecycle endpoint.
Static Debugger Affordances: Code-graph bundles emit debugger.invocation_candidate.v1 rows in debug_invocations.jsonl. These are candidate routes only; $debugger must prove a candidate at runtime before Memory can promote it.

Functional lessons and CWE summaries remain lesson-style memory records for compatibility. Structured files, symbols, and edges are canonicalized by Memory/GMO's complete projection lifecycle, not by independent per-symbol batches. /memory owns ArangoDB, Qdrant, embeddings, sparse/hybrid retrieval, and payload/index behavior. /ingest-code must not talk to ArangoDB or Qdrant directly.

Quick Start

cd .pi/skills/ingest-code