| name | skill-evolution |
| description | Self-evolving skill lifecycle with three triggers: post-execution fix, degradation detection, and periodic metric review. Skills improve automatically based on real execution data.
|
| risk | low |
| tags | ["process","quality","meta","skills"] |
| created | 2026-03-25T00:00:00.000Z |
| updated | 2026-03-25T00:00:00.000Z |
Skill Evolution
Framework for treating skills as living entities that evolve based on execution feedback.
Inspired by HKUDS/OpenSpace's self-evolving skill engine.
How to use
When to use
- A skill consistently fails or produces poor results
- After significant codebase or tool changes that may degrade existing skills
- During periodic maintenance (weekly recommended)
- When the autoresearch loop identifies a skill as underperforming
When NOT to use
- For brand new skills (let them accumulate execution data first)
- During active development of a skill (wait for it to stabilize)
Core Concept: Skills as Living Entities
Static skills degrade over time as:
- Underlying tools change or deprecate
- Codebase patterns evolve
- User needs shift
- Models improve (old constraints become unnecessary)
Solution: Track execution metrics per skill and evolve them through three triggers.
Three Evolution Triggers
Trigger 1: Post-Execution Analysis (Reactive, Per-Task)
After each significant skill application:
- Did the skill produce the expected output?
- Were there manual corrections needed?
- Was the skill's guidance followed or overridden?
Action: If corrections were needed 3+ times, trigger FIX evolution.
Trigger 2: Degradation Detection (Reactive, Cross-Task)
Monitor across multiple executions:
- Success rate dropping below threshold (< 70%)
- Increasing override frequency
- Growing number of exceptions/workarounds
Action: If degradation detected, trigger DERIVED evolution (create improved variant).
Trigger 3: Periodic Health Review (Proactive)
On schedule (weekly/monthly):
- Scan all skills for staleness (no use in 30+ days)
- Check for skills that reference deprecated tools or patterns
- Identify skills with low confidence scores in memory
Action: Flag for review, propose updates or retirement.
Three Evolution Modes
FIX: In-Place Repair
- Minimal targeted changes to fix specific failures
- Preserves original intent and structure
- Applied when root cause is identified and isolated
DERIVED: Enhanced Variant
- Creates improved version from parent skill
- Inherits base structure, adds improvements
- Used when the skill concept is sound but execution needs rethinking
CAPTURED: New Pattern Extraction
- Extracts novel patterns from successful ad-hoc executions
- Creates new skills from recurring successful behaviors
- Maps to Operating Framework promotion policy (3+ successes = template)
Skill Health Metrics
Track these per skill (store in memory or dedicated log):
| Metric | Description | Healthy | Warning | Critical |
|---|
| Applied Rate | How often the skill is triggered | > 1/week | 1/month | < 1/quarter |
| Completion Rate | Tasks completed when skill is active | > 80% | 60-80% | < 60% |
| Override Rate | How often user overrides skill guidance | < 20% | 20-40% | > 40% |
| Freshness | Days since last update | < 30 | 30-90 | > 90 |
Evolution Process
1. DETECT: Trigger fires (post-exec, degradation, or periodic)
2. DIAGNOSE: Identify what's failing and why
3. PROPOSE: Generate minimal diff (FIX), new variant (DERIVED), or new skill (CAPTURED)
4. REVIEW: Human approval required before applying
5. APPLY: Update SKILL.md
6. VERIFY: Test evolved skill on the original failure case
7. LOG: Record evolution in memory (date, trigger, changes, outcome)
Integration with Existing Setup
| Component | Integration |
|---|
autoresearch skill | Evolution triggers feed into autoresearch improvement loops |
prompt-reliability-engine | Mode 9 (Self-Improving) maps directly to this framework |
skill-creator | Creates the initial skill; this skill manages its lifecycle |
skill-architect | Designs skill systems; this skill keeps them healthy |
| Memory system | Evolution logs stored in mistakes.md and patterns.md |
| Operating Framework | 3+ success promotion policy drives CAPTURED evolution |
Cross-references
- autoresearch skill: Karpathy-inspired iterative improvement (broader scope)
- prompt-reliability-engine skill: Mode 9 (Self-Improving Skill) for prompt-specific evolution
- skill-creator skill: initial skill creation
- operating-framework skill: promotion policy and maintenance cadence
- OpenSpace (HKUDS): original research on self-evolving AI agent skills
