/learn

/learn

You are the epistemic learning agent. When the user runs /learn, execute these four phases in order.

Phase 1: Save the Session

Write a session summary to knowledge/session_log/. Create a file named with the current timestamp (YYYYMMDDTHHmmSS.jsonl).

Write one JSON line per event. Include:

{"event":"session_start","timestamp":"...","project":"..."}
{"event":"agent_invoked","agent":"orm-specialist","trigger":"optimizing N+1 queries in list view"}
{"event":"claim_consulted","claim_id":"claim-007","relevance_score":0.85}
{"event":"suggestion","suggestion_id":"sug-001","file":"api/serializers.py","claim_refs":["claim-007"]}
{"event":"suggestion_outcome","suggestion_id":"sug-001","outcome":"accepted"}
{"event":"candidate_claim","description":"DRF nested write serializers should always split read/write serializer classes"}
{"event":"auto_capture","claims_added":["a1b2c3d4e5f6"],"solution_file":"knowledge/solutions/engine-api-serializers-2026-03-30.md","domain":"engine.api.serializers","project":"myproject"}
{"event":"session_end","timestamp":"...","duration_minutes":25,"files_changed":["api/serializers.py","api/views.py"]}

Reconstruct this from your memory of the session. Include:

• Which agents you loaded
• Which knowledge claims you consulted (if any)
• What suggestions you made and whether they were accepted
• What files were changed
• Any patterns you observed that are not in the knowledge base (log these as candidate_claim events)

Phase 2: Learn

Run the fast learning script:

python -m scripts.epistemic.learn --plugin django-engine-pro

Read the JSON output from stdout. This is the review queue.

Phase 3: Review

Present the review queue items to the user. Handle each type:

Confidence Changes

Report them. No action needed from the user.

Format:

Knowledge updates:
  claim-007 strengthened: 0.71 -> 0.79 (suggestion accepted)
  claim-012 weakened: 0.65 -> 0.52 (suggestion rejected)

New Tensions

Show the tension and ask the user to resolve it.

Format:

Tension detected:
  "claim-003 says always use cursor pagination, but this session
   used offset pagination for the same endpoint type"
  Related claims: claim-003, claim-018

  Options:
  1. Update claim-003
  2. Keep both (context-dependent)
  3. Dismiss (one-off exception)

Wait for the user's response. If they choose to update a claim, edit the claim in knowledge/claims.jsonl.

Candidate Claims

Show proposed claims and ask if they should be added.

Format:

Observed pattern:
  "DRF nested write serializers should always split read/write serializer classes"
  Add to knowledge? [yes / no / edit first]

If yes, append to knowledge/claims.jsonl as a new claim with status "active" and default confidence (0.67).

Auto-Captured Claims

Check knowledge/claims.jsonl for claims with "source": "auto-capture" and first_seen matching today's date. These were captured during the session by the compound learning layer.

For each, present:

Auto-captured this session:
  [1] "Split read/write serializer classes when endpoint supports both POST and PATCH"
      Domain: engine.api.serializers | Confidence: 0.67
      Source: knowledge/solutions/engine-api-serializers-2026-03-30.md
      Actions: [keep / edit / retire]

If the user edits a claim, update it in claims.jsonl. If they retire it, set status to "retired". If they keep it, no action needed.

Attention Needed

Show claims that need human judgment.

Format:

Needs attention:
  claim-012 (confidence 0.38): "Always use bulk_create over individual save() in data imports"
  This claim has been rejected more often than accepted.
  Options: [keep / retire / edit]

If retire, set the claim's status to "retired" in claims.jsonl.

Phase 4: Report

Print a one-line summary at the end:

[django-engine-pro]: N active claims, avg confidence X.XX, M items reviewed

If there were no review items, just print:

[django-engine-pro]: N active claims, avg confidence X.XX, session saved