CloseTheLoops moradin:learn-from-sessions
Mine your accumulated Claude Code session history for implicit preferences. Surfaces candidates for review; operator decides what becomes memory.
When to invoke
/moradin:learn-from-sessions [--since 180d] [--project <name>]
Best run ONCE at first-time setup, then again periodically (monthly or quarterly) to catch new patterns. For per-session incremental capture, use /moradin:ship after each session.
What you do
Set up. Confirm
ANTHROPIC_API_KEYis in env. If not, ask operator to set it or use--local-onlymode.Discover sessions. Run
scripts/session_scanner.py --since 180 --count-onlyto show operator how many sessions/turns will be scanned.Estimate cost. Roughly $0.001 per turn for pre-filter + $0.01 per extracted candidate. For ~10k turns: typically $8-15.
Confirm with operator before running the expensive extraction.
Run extraction.
python scripts/extract_preferences.py --since 180 --min-count 5 --min-sessions 3This:
- Scans matching JSONL files
- Pre-filters with regex (corrections / preferences / lessons signal patterns)
- Classifies filtered turns with Haiku (cheap)
- Extracts structured candidates with Sonnet (strong)
- Clusters similar candidates
- Applies stability thresholds (count ≥ 5, sessions ≥ 3, confidence ≥ 0.6)
- Writes review file to
scratch/proposed_<date>.md
Surface results to operator. Read the proposed file, summarize:
- Total candidates that passed stability
- Top 5 by occurrence count
- Distribution: how many principle / preference / lesson candidates
Walk operator through review (optional). For each candidate ask:
- ACCEPT → write to
memory/preferences/<title>.md(orprinciples/orlessons/based on category) - EDIT → rewrite statement, then accept
- REJECT → skip
- ACCEPT → write to
After approval session. Run
python scripts/refresh_indexes.pyto update_INDEX.mdfiles. Delete the review file.
Tuning knobs
| Flag | Default | When to adjust |
|---|---|---|
--since N |
180 days | Start with 180 to verify pipeline quality; expand to 365+ once trusted |
--min-count N |
5 | Lower (3) if your corpus is small; raise (8-10) if you want only strongest signals |
--min-sessions N |
3 | Same logic — diversity threshold |
--project <slug> |
(all) | Restrict to one project (e.g. C--Users-yuezh-Documents-GodTech) |
--dry-run |
off | Test the regex pre-filter without spending LLM dollars |
Privacy
- All session reading is LOCAL (Python reads files on disk).
- LLM calls go to Anthropic API. Per Anthropic's commercial terms, API traffic is NOT used for training.
- If you need pure-local processing: set
MORADIN_LOCAL_ONLY=1(requires Ollama installed; future enhancement). - The script applies regex redaction for common secrets (API keys, emails, bearer tokens) before sending to LLM.
Don't
- Don't run this without operator confirmation on the LLM spend.
- Don't auto-write extracted candidates to
memory/. Operator reviews each. - Don't include any candidate in
memory/without first cleaning the title + statement. - Don't run on a corpus you haven't redacted appropriately if it contains sensitive IP.
Output
scratch/proposed_<date>.md— reviewable candidates- 0 or more new files in
memory/preferences/,memory/principles/, ormemory/lessons/after operator approval - Updated
_INDEX.mdfiles