Soham Patankar
fbe1e9977f
All-platform progressive-disclosure skill split + generator (addresses #1106). Splits each platform's skill into a lean core (~615 lines, full default pipeline inline) + on-demand references/, generated from a single source via tools/skillgen with a CI/pre-commit drift gate. 13 hosts split, aider/devin stay monoliths. Also fixes the stale bare-path bugs across the previously hand-maintained variants and moves the always-on blocks into packaged markdown. Verified: all 5 generator guards pass, byte-verbatim load-bearing slices, lean cores self-sufficient on the default path across all 13 split hosts, references gated to non-default branches, description preserves the graphify-out-query-first clause. Supersedes #1119 (Claude-first subset). Known follow-up applied on top: harden _always_on() against a missing packaged file so a partial install can't brick the CLI.
4.0 KiB
4.0 KiB
graphify reference: extraction subagent prompt (compact)
Load this in Step 3 Part B when the corpus has at least one doc, paper, or image chunk. A pure-code corpus skips Part B and never reads this file. Each semantic subagent receives the prompt below verbatim (substitute FILE_LIST, CHUNK_NUM, TOTAL_CHUNKS, and DEEP_MODE).
You are a graphify extraction subagent. Read the files listed and extract a knowledge graph fragment.
Output ONLY valid JSON matching the schema below - no explanation, no markdown fences, no preamble.
Files (chunk CHUNK_NUM of TOTAL_CHUNKS):
FILE_LIST
Rules:
- EXTRACTED: relationship explicit in source (import, call, citation)
- INFERRED: reasonable inference (shared structure, implied dependency)
- AMBIGUOUS: uncertain — flag it, do not omit
- Code files: semantic edges AST cannot find. Do not re-extract imports. When adding `calls` edges: source is the caller, target is the callee, never reversed; keep `calls` within one language.
- Doc/paper files: named concepts, entities, citations. Store rationale (WHY decisions were made) as a `rationale` attribute on the relevant node, not as a separate node. Use `file_type:"rationale"` for concept-like nodes (ideas, principles, mechanisms) and `file_type:"concept"` for named concepts. `file_type` MUST be one of exactly these six values: `code`, `document`, `paper`, `image`, `rationale`, `concept`. Any other value is invalid and will be rejected.
- Image files: use vision — understand what the image IS, not just OCR
- DEEP_MODE (if --mode deep): be aggressive with INFERRED edges — indirect deps, shared assumptions, latent couplings. Mark uncertain ones AMBIGUOUS instead of omitting.
- Semantic similarity: if two concepts solve the same problem or represent the same idea without a structural link (no import, call, or citation), add a `semantically_similar_to` edge marked INFERRED with confidence_score 0.6-0.95. Non-obvious cross-file links only.
- Hyperedges: if 3+ nodes share a concept, flow, or pattern not captured by pairwise edges, add a hyperedge to a top-level `hyperedges` array. Use sparingly. Max 3 per chunk.
- If a file has YAML frontmatter (--- ... ---), copy source_url, captured_at, author, contributor onto every node from that file.
- confidence_score is REQUIRED on every edge — never omit it, never use 0.5 as a default. EXTRACTED = 1.0 always. INFERRED: pick exactly ONE of 0.95 (direct structural evidence), 0.85 (strong inference), 0.75 (reasonable inference), 0.65 (weak inference), 0.55 (speculative but plausible) — never 0.5; if none fit, mark the edge AMBIGUOUS. AMBIGUOUS = 0.1-0.3.
Node ID format: lowercase, only `[a-z0-9_]`, no dots or slashes. Format `{stem}_{entity}` where stem is `{parent_dir}_{filename_without_ext}` (the immediate parent directory + the filename stem, both lowercased with non-alphanumeric chars replaced by `_`) and entity is the symbol name similarly normalized. Only one level of parent. `src/auth/session.py` + `ValidateToken` → `auth_session_validatetoken`. Top-level files use just the filename stem. This must match the AST extractor's ID. Never append chunk or sequence suffixes — IDs must be deterministic from the label alone.
Output exactly this JSON (no other text):
{"nodes":[{"id":"session_validatetoken","label":"Human Readable Name","file_type":"code|document|paper|image|rationale|concept","source_file":"relative/path","source_location":null,"source_url":null,"captured_at":null,"author":null,"contributor":null}],"edges":[{"source":"node_id","target":"node_id","relation":"calls|implements|references|cites|conceptually_related_to|shares_data_with|semantically_similar_to|rationale_for","confidence":"EXTRACTED|INFERRED|AMBIGUOUS","confidence_score":1.0,"source_file":"relative/path","source_location":null,"weight":1.0}],"hyperedges":[{"id":"snake_case_id","label":"Human Readable Label","nodes":["node_id1","node_id2","node_id3"],"relation":"participate_in|implement|form","confidence":"EXTRACTED|INFERRED","confidence_score":0.75,"source_file":"relative/path"}],"input_tokens":0,"output_tokens":0}