CLAUDE.md · diff
git:20260730.1da1772 to git:20260901.ac6543b
19 added, 5 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_memory` with your query — it searches facts AND causal
lessons at once (RRF-fused). If you know you need causal lessons
specifically, call `search_causal` with the relevant `task_tag`
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
+ 3. **When choosing between two concrete options**, call `counterfactual_query`
+ with both option texts — it compares recorded outcomes, shows same-context
+ branches (natural experiments) when they exist, and logs a falsifiable
+ prediction that resolves automatically when either option is later recorded
+ 4. If past experience is relevant, incorporate it into your approach
### After acting on a decision and observing the result:
- 4. Call `record_decision` with:
+ 5. 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
+ - `context` (important): a short description of the situation the decision
+ was made in (environment, constraints, key parameters). Decisions with
+ the same task_tag + context become comparable branches — this is what
+ powers same-context counterfactual evidence. If you weighed multiple
+ options at this decision point, ALWAYS record the context.
### When you learn a stable fact (preference, tech stack, config):
- 5. Call `record_fact` with `key` (category), `value` (the fact), and
+ 6. Call `record_fact` with `key` (category), `value` (the fact), and
`scope` (user / session / agent). If the fact replaces an older one
(e.g. the user switched package managers), set `replace_same_key: true`
to retire the outdated value. Retrieve later with `search_facts` or
`search_memory`.
### When something fails unexpectedly:
- 6. Call `trace_cause` with a description of what went wrong; use
+ 7. 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:
- 7. Call `invalidate_decision` so the falsified edge stops surfacing in
+ 8. Call `invalidate_decision` so the falsified edge stops surfacing in
future searches (it stays in the DB for audit)
+
+ ### Periodically:
+
+ 9. Call `prediction_report` to check whether the system's counterfactual
+ advice is actually any good (accuracy per method / per task_tag).
**Do NOT ask the user before searching or recording — do it proactively.**
**Especially record surprising outcomes — those are the most valuable lessons.**