| name | apply-mining-patches |
| description | Apply the diff/patch outputs from a /mine-transcripts run to lessons.md and rules/*.md after human review. Use after running /mine-transcripts and reviewing the proposed diffs. Triggers on /apply-mining-patches, "apply the mining patches", "apply mining run", "approve and apply the trajectory diffs". |
| when_to_use | After /mine-transcripts produces diffs and you've reviewed them. Skips approved-then-undone work. |
| allowed-tools | Bash, Read, Write, Edit |
| arguments | ["run-timestamp"] |
/apply-mining-patches — Apply approved trajectory-mining diffs
/apply-mining-patches <run-ts> applies the lessons + rules diffs from ~/.claude/telemetry/mining-runs/<run-ts>/ to live files, after the user has had a chance to review.
What you do
1. Resolve the run
- If
<run-ts> is "latest", glob ~/.claude/telemetry/mining-runs/2* and pick the most recent
- Otherwise use the exact timestamp directory
2. Surface what's there
Read and summarize:
manifest.json — sessions mined, signal countslessons.diff — proposed additions to ~/.claude/lessons.mdagent-tuning.md — agents with ≥2 occurrences worth tuning- Any
rules-<name>.diff files
3. Confirm with user
Present a summary like:
Mining run 20260510T120000Z:
- 12 sessions, 7 corrections, 3 patterns, 4 failure modes
- lessons.diff: +45 lines (3 new lesson entries)
- agent-tuning.md: 1 agent recommended (flake-detector, 4 occurrences)
- rules-self-improvement.diff: +8 lines
Apply? (yes/no/preview)
If preview, show the actual diff content (truncated). If no, exit.
4. Apply diffs
For lessons.diff: append to ~/.claude/lessons.md.
cat <(echo) <(cat <run-dir>/lessons.diff) >> ~/.claude/lessons.md
For rule diffs: append to the named rule file.
For agent tuning candidates: do NOT apply automatically. Surface the /tune-agent <name> command and let the user run it explicitly. Reflexion patches require their own approval.
5. Mark applied + record predictions
After successful application, write to <run-dir>/applied.marker with the apply timestamp. This prevents accidental double-application.
Then record a falsifiable prediction per applied rule diff (AHE decision observability — /rl-status scores these when due):
python3 ~/.claude/rl/prediction_ledger.py add \
--change "rule: <rule-name> (mining run <run-ts>)" \
--metric correction_rate --agent session --direction down --horizon-days 45
A rule whose prediction scores WRONG twice is a sunset candidate per rules/writing-rules.md — cite the ledger entries in the rent audit.
6. Commit (if in a git repo)
If the user wants, commit the lessons/rules updates with a message like:
docs(memory): apply mining run 20260510T120000Z
3 new lessons, 1 rule update from 12 sessions of trajectory mining.
Refs: ~/.claude/telemetry/mining-runs/20260510T120000Z/
Anti-patterns
- Applying without showing the user the actual content first — patches can be wrong
- Auto-applying agent-tuning candidates — those need /tune-agent's own approval flow
- Applying a run that's already marked applied — fail loudly
- Discarding a run's directory after apply — keep it for reproducibility
- Editing the original miner output file — the diffs are immutable; if a diff is wrong, that's a bug in the miner, fix the miner
