git:20260726.86a185f to git:20260727.b76571f

11 added, 3 removed. Audit A to A.

# 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. If past experience is relevant, incorporate it into your approach

+ 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:

- 3. Call `record_decision` with:

+ 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:

- 4. Call `trace_cause` with a description of what went wrong

+ 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.**