# Apply Mining Patches

> 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".

- Skill: `sethdford/apply-mining-patches-2` (Agent Skill)
- Install (CLI): `npx skillmds@latest add sethdford/apply-mining-patches-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/sethdford/apply-mining-patches-2/raw
- Safety review: pending (external: skill-scanner PASS, skillspector CAUTION)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: sethdford (https://skillmd.com/u/sethdford)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/sethdford/apply-mining-patches-2

---


# /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 counts
- `lessons.diff` — proposed additions to `~/.claude/lessons.md`
- `agent-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`.

```bash
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):

```bash
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