- name
- active-self-improvement
- version
- 1.2.0
- description
- 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.
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.mdand 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:
- Extract the
Contextcolumn value - Match against skill names (fuzzy: "SiteBlitz CSS" →
webdev-sop) - If match found and skill doesn't already have the fix in
## Learned:
## Learned
- [date] [error summary] → [fix]. Source: .learnings/ERRORS.md#L[N]- Use
Pattern-Key: hash(error+fix)to prevent duplicates
Skills self-heal: every failure improves the relevant skill.
Delegation Feedback
After delegation plan completes:
- Read atom timing data from DELEGATION_PLAN.md
- Atom actual time > 2× estimated → flag estimation drift
- Atom model upgraded (flash→sonnet) → update routing suggestion in MODEL_ROUTING_PROTOCOL.md
- Append summary to
.learnings/LEARNINGS.md