Synthesizing Institutional Knowledge is a skill for teams building AI agents that must answer questions about organizational reasoning — not just what a policy says, but why it exists, what it replaced, and what constraints produced it.
The skill addresses a structural gap in how organizations store knowledge. Embedding a document preserves its content but discards the reasoning behind it. The skill introduces a three-type knowledge taxonomy to route information to the correct storage target. Declarative knowledge — facts and current-state policies — goes to a vector RAG store, the only category where embeddings are architecturally sufficient. Episodic knowledge — events, incidents, and decisions with timestamps — goes to a temporal store using a full event schema. Causal knowledge — decision rationale, constraint chains, and alternatives considered — goes to a knowledge graph with explicit predecessor and successor edges, enabling graph traversal over decision chains rather than semantic similarity queries.
The skill provides a complete institutional event schema: a JSON structure capturing actors, affected entities, rationale, alternatives considered, constraints, outcome, and causal links. It also provides an ingestion workflow covering live capture at the moment decisions are made and retroactive extraction from legacy documents such as architecture decision records, post-mortems, and meeting notes, using model-assisted extraction with human review for causal edge validation.
This skill suits engineering, compliance, and strategy teams whose agents need to answer queries like “why do we use X” — queries requiring causal graph traversal, not nearest-embedding retrieval.