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

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]

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]

```

1. 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