Skill Optimizer 🔬

Systematic skill document optimization for OpenClaw, adapted from Microsoft SkillOpt research.

Core insight: Skills are the "weights" of a frozen agent. Optimize them with the same discipline as neural network training — rollout, reflect, aggregate, select, update, gate — but in text space, without retraining the model.

When to Use

• Skill execution is inconsistent or frequently fails
• Want to systematically improve skill quality (not just ad-hoc fixes)
• Onboarding new skills and want to validate quality before deployment
• Auditing existing skills for completeness and best practices
• Batch-optimizing multiple skills across the workspace
• Preparing skills for ClawHub publication

Core Concepts (from SkillOpt)

SkillOpt Concept	Our Adaptation
Skill document = trainable state	SKILL.md is the "model" we optimize
Rollout	Execute skill with test scenarios, capture results
Reflect	Analyze execution patterns, identify weaknesses
Aggregate	Combine multiple improvement suggestions
Select (learning rate)	Limit edits per pass to prevent overfitting
Update	Apply bounded edits to SKILL.md
Validation gate	Only accept edits that demonstrably improve quality
Slow update	Gradual improvement across optimization passes
Meta skill	Cross-skill patterns extracted as reusable heuristics
best_skill.md	The optimized SKILL.md artifact

Quality Dimensions (Scoring Rubric)

Every skill is scored on 10 dimensions (1-5 each, max 50):

#	Dimension	What to Check
1	Trigger Clarity	Are activation triggers specific and comprehensive?
2	Structure	Does it follow consistent sections (Overview → Steps → Examples)?
3	Step Completeness	Are all steps present, ordered, and actionable?
4	Error Handling	Does it handle edge cases, failures, and exceptions?
5	Input Validation	Does it validate inputs before execution?
6	Output Specification	Are expected outputs clearly defined?
7	Examples	Are there concrete before/after examples?
8	Tool References	Are tools/commands referenced correctly with versions?
9	Dependencies	Are prerequisites and dependencies documented?
10	Maintainability	Is it easy to update? Version controlled?

Workflow

Mode 1: Analyze (Read-Only)

Input: skill name or path to SKILL.md
Output: quality score + detailed analysis

Steps:

1. Read the SKILL.md file
2. Parse structure: frontmatter, sections, steps, examples
3. Score each of the 10 quality dimensions
4. Identify specific weaknesses with line references
5. Generate analysis report

Command pattern:

Analyze skill: <skill-name>

Mode 2: Optimize (Read + Write)

Input: skill name or path to SKILL.md
Output: optimized SKILL.md + diff report

Steps:

1. Analyze — Run full analysis (Mode 1)
2. Backup — Copy original to SKILL.md.bak
3. Propose — Generate specific edits for each weakness
4. Select — Apply learning rate: max 4 edits per pass (SkillOpt default)
5. Update — Apply edits to SKILL.md
6. Gate — Re-score the skill. Only accept if score improves.
7. Report — Show before/after diff + score comparison

Learning Rate Control:

• Default: max 4 edits per optimization pass
• If more than 4 improvements needed → prioritize by impact
• Rejected edits go to a buffer for next pass
• Prevents overfitting / over-editing in one pass

Command pattern:

Optimize skill: <skill-name>

Mode 3: Batch Optimize

Input: list of skills or "all"
Output: optimization report for all skills

Steps:

1. List all skills in workspace
2. Score each skill (quick analysis)
3. Rank by improvement potential (lowest scores first)
4. Optimize top N skills (default: 3)
5. Generate batch report

Command pattern:

Batch optimize skills [top N]

Mode 4: Compare

Input: skill name
Output: before/after comparison

Steps:

1. Read current SKILL.md and SKILL.md.bak (if exists)
2. Generate side-by-side diff
3. Show score comparison
4. List accepted/rejected edits

Command pattern:

Compare skill: <skill-name>

Optimization Strategies

Strategy 1: Structural Completion

Problem: Skill is missing critical sections. Fix: Add missing sections based on template. Impact: High (foundational)

Strategy 2: Trigger Expansion

Problem: Triggers are too narrow, skill doesn't activate when needed. Fix: Expand trigger keywords based on common usage patterns. Impact: Medium-High

Strategy 3: Error Path Addition

Problem: Skill only covers happy path. Fix: Add error handling, fallback paths, and troubleshooting. Impact: High

Strategy 4: Example Enhancement

Problem: Abstract instructions without concrete examples. Fix: Add before/after examples for each major step. Impact: Medium

Strategy 5: Dependency Documentation

Problem: Prerequisites not documented, causes setup failures. Fix: Add clear dependency list with versions and install commands. Impact: Medium

Strategy 6: Output Specification

Problem: Unclear what the skill should produce. Fix: Add explicit output format, file paths, success criteria. Impact: Medium

Strategy 7: Step Decomposition

Problem: Steps are too large or vague. Fix: Break complex steps into smaller, atomic actions. Impact: Medium

Strategy 8: Cross-Reference Enhancement

Problem: Skill doesn't reference related skills or tools. Fix: Add links to related skills, tools, and documentation. Impact: Low-Medium

Validation Gate Rules

An edit is ONLY accepted when:

1. ✅ The quality score improves (or stays same with no regressions)
2. ✅ No existing functionality is removed
3. ✅ The edit is bounded (add/delete/replace, not full rewrite)
4. ✅ The edit doesn't introduce contradictions
5. ✅ The edit maintains consistent tone and style

If the gate rejects an edit:

• Log it to the rejected-edit buffer
• Try alternative formulation in next pass
• If 3+ rejections on same issue → flag for human review

Meta Skill Extraction

After optimizing 5+ skills, extract cross-skill patterns:

1. Common weaknesses — what do most skills lack?
2. Common strengths — what do the best skills have?
3. Structural patterns — what sections work best?
4. Anti-patterns — what should skills NEVER do?

Store patterns in references/meta-skill-patterns.md. Use patterns to improve the scoring rubric and optimization strategies.

Output Format

Analysis Report

# Skill Analysis: [skill-name]

## Score: [X]/50

### Dimension Scores
| Dimension | Score | Notes |
|-----------|-------|-------|
| Trigger Clarity | 3/5 | ... |
| ... | ... | ... |

### Weaknesses (Priority Order)
1. [HIGH] Missing error handling in Step 3
2. [MEDIUM] No examples for main workflow
3. [LOW] Triggers could be more specific

### Strengths
- Clear step-by-step structure
- Good tool references

### Recommended Edits
1. Add error handling section after Step 5
2. Add example for common use case
3. Expand trigger keywords

Optimization Report

# Optimization Report: [skill-name]

## Before → After
- Score: [X]/50 → [Y]/50 (+[Z])
- Edits applied: [N]
- Edits rejected: [M]

## Changes Made
### Edit 1: [Type] [Section]
- Before: [original text]
- After: [new text]
- Reason: [why this improves the skill]

## Validation
- [✅/❌] Score improved
- [✅/❌] No functionality removed
- [✅/❌] Edits bounded
- [✅/❌] No contradictions
- [✅/❌] Style consistent

Integration with AAR

After optimizing a skill:

1. Run the skill on a real task
2. Note any failures or friction points
3. Feed findings back into next optimization pass
4. This creates a continuous improvement loop (like SkillOpt epochs)

References

• Paper: SkillOpt: Executive Strategy for Self-Evolving Agent Skills
• GitHub: microsoft/SkillOpt (15.5K stars)
• Key insight: Train skills like neural networks — epochs, learning rates, validation gates — without touching model weights
• Results: 41% → 80% accuracy (spreadsheet), 33% → 72% accuracy (document)
• Our adaptation: Text-space optimization for OpenClaw SKILL.md files with LLM-driven analysis