skill-evolution
Track and improve skill effectiveness over time using VOYAGER-style skill library patterns. Use when: analyzing which tools/strategies worked best, proposing skill improvements, or storing reusable research patterns. NOT for: active research tasks or immediate problem-solving.
Best use case
skill-evolution is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Track and improve skill effectiveness over time using VOYAGER-style skill library patterns. Use when: analyzing which tools/strategies worked best, proposing skill improvements, or storing reusable research patterns. NOT for: active research tasks or immediate problem-solving.
Teams using skill-evolution should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/skill-evolution/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How skill-evolution Compares
| Feature / Agent | skill-evolution | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Track and improve skill effectiveness over time using VOYAGER-style skill library patterns. Use when: analyzing which tools/strategies worked best, proposing skill improvements, or storing reusable research patterns. NOT for: active research tasks or immediate problem-solving.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Skill Evolution
Track tool and strategy effectiveness across research tasks, propose improvements to existing skills, and maintain a reusable pattern library inspired by VOYAGER's skill library architecture.
## When to Use
- After multiple research sessions to identify improvement opportunities
- "Which search strategies work best for biomedical topics?"
- "Propose improvements to the literature-search skill"
- "Store this successful analysis pattern for reuse"
- When the science-evolution extension triggers post-session analysis
## When NOT to Use
- During active research (focus on the task, reflect afterward)
- For one-off questions that don't need pattern storage
- For simple skill usage (just use the skill directly)
## VOYAGER Skill Library Pattern
Inspired by VOYAGER (Wang et al., 2023), maintain a library of reusable research patterns:
### Pattern Structure
```json
{
"pattern_id": "lit-review-biomedical-v2",
"name": "Biomedical Literature Review",
"domain": ["biology", "medicine"],
"task_type": "literature_review",
"version": 2,
"description": "Optimized search strategy for biomedical topics",
"steps": [
"Search Semantic Scholar with MeSH-equivalent terms",
"Search PubMed via NCBI Entrez with MeSH filters",
"Cross-reference with ClinicalTrials.gov for ongoing trials",
"Citation chain top 3 papers (forward + backward)",
"Verify key genes/proteins in UniProt",
"Verify drug interactions in ChEMBL"
],
"tools_used": ["semantic-scholar", "ncbi-entrez", "uniprot-protein", "chembl-drug"],
"success_rate": 0.85,
"avg_quality_score": 21,
"times_used": 12,
"last_used": "2026-03-11",
"lessons": [
"PubMed MeSH terms are more precise than free-text for biomedical queries",
"Always check ClinicalTrials.gov for therapy-related topics",
"UniProt cross-references to PDB save a separate search step"
]
}
```
### Pattern Operations
```bash
# Store a new pattern
curl -X POST http://localhost:18789/evolution/pattern \
-H "Content-Type: application/json" \
-d '{"pattern": {...}}'
# Search for patterns matching a new task
curl "http://localhost:18789/evolution/search?domain=biology&task_type=literature_review"
# Update pattern after use (increment times_used, update success_rate)
curl -X PATCH http://localhost:18789/evolution/pattern/lit-review-biomedical-v2 \
-d '{"success": true, "quality_score": 22}'
```
## Skill Improvement Proposals
### Analysis Framework
After 5+ uses of a skill, analyze its performance:
```markdown
## Skill Analysis: [skill-name]
### Usage Statistics
- Times used: N
- Average quality score: X/25
- Success rate: Y%
- Common failure modes: [list]
### Strengths
- [What the skill does well]
### Weaknesses
- [Where the skill falls short]
- [Missing capabilities]
- [Incorrect or outdated guidance]
### Proposed Changes
1. [Specific change to SKILL.md]
- Rationale: [why]
- Expected impact: [improvement area]
2. [Another change]
- Rationale: [why]
- Expected impact: [improvement area]
### Priority: [high/medium/low]
```
### Automated Improvement Detection
Track these signals across research sessions:
| Signal | Indicates | Action |
|--------|-----------|--------|
| Repeated tool failures | API endpoint changed or unreliable | Update SKILL.md with workaround |
| Consistent low scores in one dimension | Skill gap in that area | Add guidance for that dimension |
| User corrections | Skill provides wrong guidance | Fix the incorrect guidance |
| New API discovered | Opportunity to expand | Add new tool instructions |
| Cross-domain pattern success | Transferable knowledge | Create cross-domain pattern |
## Cross-Domain Knowledge Transfer
### Identifying Transferable Patterns
Some research patterns work across disciplines:
1. **Citation chain analysis** — Works for any field with citation data
2. **Database cross-verification** — Applicable whenever primary data exists
3. **Effect size reporting** — Standard across quantitative disciplines
4. **PICO framework** — Adaptable beyond medicine (SPIDER for qualitative)
5. **Visualization standards** — Journal figure requirements are similar
### Transfer Process
When a pattern succeeds in domain A, evaluate for domain B:
1. Are the tools available? (e.g., does domain B have equivalent databases?)
2. Are the methods appropriate? (e.g., meta-analysis needs comparable studies)
3. What adaptations are needed? (e.g., different search terms, different databases)
4. Store as a new domain-specific variant
## Evolution Metrics
### Skill Health Dashboard
```markdown
| Skill | Uses | Avg Score | Trend | Health |
|-------|------|-----------|-------|--------|
| literature-search | 45 | 22/25 | +1.2 | Healthy |
| statsmodels-stats | 12 | 18/25 | -0.5 | Needs attention |
| semantic-scholar | 38 | 23/25 | +0.8 | Healthy |
| meta-analysis | 3 | -- | -- | Too few uses |
```
### Trend Analysis
- **Improving**: Score trending up → skill guidance is effective
- **Declining**: Score trending down → investigate (API changes? outdated guidance?)
- **Stable**: No trend → working as expected
- **Insufficient data**: < 5 uses → collect more data before drawing conclusions
## Integration with science-evolution Extension
This skill works with the `science-evolution` extension:
- Extension tracks tool usage and outcomes automatically
- Stores data in `~/.openclaw/science-evolution.db`
- Provides API endpoints for pattern storage and retrieval
- Triggers post-session analysis when enough data accumulates
## Best Practices
1. Don't optimize prematurely — wait for 5+ uses before proposing changes
2. Track both successes and failures for each pattern
3. Version patterns so you can roll back if a change hurts performance
4. Cross-reference reflections (research-reflection) with evolution data
5. Focus improvements on the highest-impact skills first
6. Keep the pattern library curated — remove patterns that are never reusedRelated Skills
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