active-self-improvement

Active self-improvement loop that reads learnings, errors, batch outputs, and memory — detects patterns — and UPDATES skills/protocols/behavior automatically. Use when the agent should get smarter without being prompted. Different from passive logging — this ACTS on what it learns. Triggers after Recorder at end of sessions, after batch processing, after project milestones, on explicit "improve" or "what have we learned" prompts, or on a weekly cron schedule.

作者: admin | 来源: ClawHub

# Auto-Improve Reads logs, detects patterns, rewrites the playbook. Not passive logging — this ACTS on what it learns. ``` SCAN (read logs) ──► PROPOSE (specific edits) ──► APPLY (low-risk auto, high-risk flag) ``` ## Input Sources | Source | What It Contains | |--------|-----------------| | `.learnings/ERRORS.md` | What broke and how it was fixed | | `.learnings/LEARNINGS.md` | Corrections, insights, knowledge gaps, batch outcomes | | `workspace/OUTSTANDING.md` | Ranked ideas and opportunities | | `memory/permanent/*.md` | Current knowledge state | | `workspace/DELEGATION_PLAN.md` | Atom timing data (if delegation was used) | ## Step 1: SCAN Detect: - **Repeated errors** — same mistake 3+ times → needs a prevention rule - **Repeated corrections** — user keeps fixing the same thing → behavior change needed - **Emerging patterns** — 3+ items connecting → thesis forming - **Stale knowledge** — facts in permanent memory contradicted by recent sessions - **Unused wins** — high-value items that haven't been acted on ## Step 2: PROPOSE For each detected pattern: ``` PROPOSAL: [short title] EVIDENCE: [file#line references] CHANGE: [exact edit — old text → new text] RISK: [low/medium/high] REVERSIBLE: [yes/no] Pattern-Key: [hash(error+fix) for dedup] ``` | Pattern Type | Action | Target File | |-------------|--------|-------------| | Repeated error | Add prevention rule | relevant skill's `## Learned` section | | Repeated correction | Update behavior guideline | `SOUL.md` or `AGENTS.md` | | Emerging thesis | Write thesis + next steps | `OUTSTANDING.md` | | Stale knowledge | Update the fact | `memory/permanent/*.md` | | Unused win | Create ticket or reminder | `NEXT_TICKET.md` or cron | ## Step 3: APPLY - **Low risk + reversible**: Apply immediately. Log the change. - **Medium risk**: Apply but notify user on next interaction. - **High risk**: Write to `OUTSTANDING.md` and wait for approval. - **Dry-run mode** (`--dry-run`): Propose all changes but apply none. Output a report. Use 3-occurrence threshold before proposing pattern-based changes. Track recurrence with `Pattern-Key` and `Recurrence-Count`. ## Error→Skill Feedback Loop After SCAN, for each error in ERRORS.md: 1. Extract the `Context` column value 2. Match against skill names (fuzzy: "SiteBlitz CSS" → `webdev-sop`) 3. If match found and skill doesn't already have the fix in `## Learned`: ```markdown ## Learned - [date] [error summary] → [fix]. Source: .learnings/ERRORS.md#L[N] ``` 4. Use `Pattern-Key: hash(error+fix)` to prevent duplicates Skills self-heal: every failure improves the relevant skill. ## Delegation Feedback After delegation plan completes: 1. Read atom timing data from DELEGATION_PLAN.md 2. Atom actual time > 2× estimated → flag estimation drift 3. Atom model upgraded (flash→sonnet) → update routing suggestion in MODEL_ROUTING_PROTOCOL.md 4. Append summary to `.learnings/LEARNINGS.md`