What This Skill Does

When you embed a document, you preserve what it says. You lose who decided it, why, what it replaced, and what it caused. This skill teaches you to capture that missing provenance as structured institutional memory — so your agent can answer questions that no RAG system can touch.

Problems It Solves

• Provenance blindness — "Why are we doing it this way?" is unanswerable from a vector store because the reasoning was never indexed, only the output document.

• Type 3 knowledge gap — most organizations capture facts (Type 1) and some events (Type 2), but almost never capture causal reasoning (Type 3) at the time decisions are made. This skill closes that gap before it compounds.

• Retroactive ingestion failure — teams trying to rebuild institutional history from old docs discover the causal edges were never written down. This skill provides a model-assisted extraction workflow with human review for causal edge validation.

• "Why do we use X?" queries — technology, policy, and architectural choices require graph traversal over decision chains, not semantic similarity.

What You Get

The skill defines three knowledge types with distinct storage targets:

• Declarative (Type 1): Facts and current-state policies → Vector RAG. The only category where embeddings are structurally sufficient.

• Episodic (Type 2): Events, incidents, decisions with timestamps → Temporal store with full event schema.

• Causal (Type 3): Decision rationale, constraint chains, alternatives considered → Knowledge graph with explicit causal predecessor/successor edges.

You also get a complete institutional event schema — a JSON structure capturing actors, affected entities, rationale, alternatives considered, constraints, outcome, and causal links — plus an ingestion workflow for both live capture and retroactive extraction from legacy documents like ADRs, post-mortems, and meeting notes.

Who Should Use This

• Teams building AI agents that must answer questions about organizational reasoning — why decisions were made, how the current architecture evolved, what historical constraints drive current policy — across engineering, compliance, strategy, or any domain where institutional memory compounds over time.