Knowledge Graph

Knowledge Graph | 知識圖

Language: English | 繁體中文

Answer structural questions across specs, decisions, and code — "what is the full impact chain of XSPEC-205?" — using the Knowledge Graph Memory relationship schema. Works with or without a graph engine.

回答橫跨規格、決策與程式碼的結構性問題——「XSPEC-205 的完整影響鏈是什麼？」——依據知識圖記憶標準的關係 schema。有無圖引擎皆可運作。

Implements: XSPEC-237 Phase 5 — knowledge-graph skill (EngramGraph opt-in)

Mode Selection | 模式選擇

Detect which mode to use before answering:

Workflow | 工作流程

1. Resolve the target — normalise the argument to a canonical id (XSPEC-205, DEC-062, a function name).
2. Choose mode — probe for a graph engine (service) else fall back (degraded).
3. Service mode (AC-5b) — issue a single multi-hop query and present the returned chain, including cross-domain links (code → spec → decision):

   curl -s -X POST "$ENGRAM_URL/graph/impact-analysis" \
     -H 'content-type: application/json' \
     -d '{"nodeId":"XSPEC-205","maxHops":3}'
   

4. Degraded mode (AC-5a) — with no engine, read the target document, follow its impacts/impacted_by/supersedes/related front-matter and inline [[ref]] links by reading the linked files, and assemble the chain manually (bounded by reading depth).
5. Present the chain — list the connected Specs and Decisions, the edge type for each hop, and (if present) each node's confidence, highest first.
6. State the mode used — always say whether the answer came from the engine or Markdown fallback, so completeness is clear.

Relationship Schema | 關係 schema

See knowledge-graph-memory. Front-matter fields: related, impacts, impacted_by, supersedes, implements. Edge derivation: Decision impacts Spec → IMPACTS; Decision supersedes Decision → SUPERSEDES.

關係欄位與邊推導見知識圖記憶標準。

Next Steps Guidance | 下一步引導

• If degraded mode hit a reading-depth limit, tell the user a graph engine (e.g. EngramGraph) would give a complete chain, and how to set ENGRAM_URL.
• If a referenced id was not found, surface it as a dangling reference to fix.
• Offer to add missing impacts/impacted_by front-matter to the documents you traversed.

Reference | 參考

• Standard: core/knowledge-graph-memory.md
• Engine (opt-in): EngramGraph — engramgraph
• Detailed guide: guide.md