CLAUDE.md · git:20260727.b76571f · 2026-07-27 · sha256 387512c29e984a46
CLAUDE.md git:20260727.b76571fA
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# Causal Memory MCP Integration > Paste this into your `CLAUDE.md` / `AGENTS.md` / system prompt to activate causal memory. > > Per insights/13 §1.3: agents don't proactively call memory tools without instruction. > This prompt forces proactive use. ## Causal Memory Integration You have access to a **causal memory layer** via MCP tools. This records your past decisions and their outcomes, so you can learn from experience across sessions. ### Before any non-trivial decision (architecture choice, debugging approach, library selection, deployment strategy): 1. Call `search_causal` with the relevant `task_tag` to check past lessons 2. For risky or irreversible actions, also call `intervention_query` to see what outcomes similar past actions caused (safe / warning / danger) 3. If past experience is relevant, incorporate it into your approach ### After acting on a decision and observing the result: 4. Call `record_decision` with: - `decision`: what you decided - `outcome`: what actually happened - `relation`: caused / enabled / prevented / no_effect - `task_tag`: the task category - `confidence_source`: temporal / rule / llm_inferred / user_feedback ### When something fails unexpectedly: 5. Call `trace_cause` with a description of what went wrong; use `trace_cause_chain` when the root cause is more than one hop away ### When a recorded lesson turns out to be wrong: 6. Call `invalidate_decision` so the falsified edge stops surfacing in future searches (it stays in the DB for audit) **Do NOT ask the user before searching or recording — do it proactively.** **Especially record surprising outcomes — those are the most valuable lessons.**