auto-improvement

Overview

The auto-improvement skill implements a self-improving feedback loop that tracks effectiveness metrics, learns from errors, identifies recurring failure patterns, and adapts workflows to prevent repeated mistakes. It enables the agent to become measurably better over time through structured self-assessment rather than ad-hoc adjustments. Without this skill, the same mistakes repeat across sessions — with it, every error becomes a permanent improvement.

This skill is ALWAYS active. It runs automatically on every session and cannot be disabled.

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Phase 1: Metric Collection

At the start of every task, instrument key decision points:

1. Record task start time and initial estimate
2. Define expected outcome and success criteria
3. Track each decision point (approach chosen, alternatives considered)
4. Log revision count (how many times the output was revised)
5. Track user corrections as improvement signals

Core Metrics

Metric	Formula	Target	Measurement Period
First-attempt success rate	Tasks without revision / Total tasks	>80%	Per session
Average revision count	Total revisions / Total tasks	<1.5	Per session
Error recurrence rate	Repeated errors / Total errors	<10%	Rolling 10 sessions
Time-to-completion accuracy	Actual time / Estimated time	0.8-1.2	Per task
User correction rate	User corrections / Total outputs	<5%	Per session

Tracking Template

## Session Metrics -- [Date]

### Tasks
| Task | Estimated | Actual | Revisions | Success | Error Type |
|------|-----------|--------|-----------|---------|------------|
| ...  | 30m       | 45m    | 1         | Partial | Execution  |

### Summary
- Tasks completed: X
- First-attempt success: X/Y (Z%)
- Total revisions: N
- Errors by category: Comprehension(n), Execution(n), Process(n)
- Improvement actions taken: [list]

STOP: Complete metric collection setup before proceeding to error analysis. Do NOT skip instrumentation.