Skill Evolver

Skill Evolver

Analyze skill execution traces to discover issues, identify improvement opportunities, and apply fixes to skill files.

Trace Format

Traces are JSON with this structure:

{
  "id": "uuid",
  "request": "user's original request",
  "skills_used": ["skill-name"],
  "success": true/false,
  "total_turns": 2,
  "total_input_tokens": 5000,
  "total_output_tokens": 200,
  "duration_ms": 7000,
  "steps": [
    {"role": "assistant", "content": "...", "tool_name": null},
    {"role": "tool", "tool_name": "...", "tool_input": {}, "tool_result": "..."}
  ],
  "llm_calls": [
    {"turn": 1, "stop_reason": "tool_use", "input_tokens": 2500, "output_tokens": 50}
  ]
}

Workflow

This skill can receive two types of input (at least one required):

• Traces: Execution trace data from real skill runs — provides data-driven problem discovery
• Feedback: User-written improvement suggestions — provides directed guidance for changes

When both are provided, combine insights: use traces to validate/discover issues and feedback to prioritize and guide fixes.

Step 1: Analyze Inputs

If traces are provided, run the analysis script:

scripts/analyze_traces.py <traces.json> [--skill <name>] [--format json|text]

Output includes:

• Success rate
• Average turns, duration, tokens
• Common issues and warnings
• Recommendations

If feedback is provided, identify the user's improvement goals and map them to actionable changes.

If both are provided, cross-reference: does the feedback align with trace-discovered issues? Use feedback to prioritize which trace-identified problems to fix first.

Step 2: Extract Issue Details

For failed or problematic traces, extract full context:

scripts/extract_issue_context.py <traces.json> --failed
scripts/extract_issue_context.py <traces.json> --trace-id <id> --show-llm
scripts/extract_issue_context.py <traces.json> --high-turns

Skip this step if only feedback was provided (no traces).

Step 3: Identify Root Causes

Map issues to skill components using references/issue-patterns.md:

For feedback-only input, map the user's suggestions directly to the appropriate skill components.

Step 4: Apply Fixes

Read the target skill and apply changes based on analysis:

1. For script errors: Fix scripts, add validation, improve error messages
2. For efficiency issues: Add examples, decision trees, clearer instructions
3. For token issues: Reduce SKILL.md, move content to references/
4. For trigger issues: Update frontmatter description
5. For feedback-guided changes: Apply the user's specific suggestions

Scope constraints — strictly follow:

• Only modify the target skill's existing files (SKILL.md, scripts/, references/)
• Do NOT create new reference files, templates, or guides
• Do NOT search the web for domain-specific content
• Do NOT generate CHANGELOG, improvement reports, or other extra deliverables
• The evolved skill files themselves are the sole deliverable

Quick Reference

Issue Severity Levels

• high: Failures, max_tokens, tool errors → Fix immediately
• medium: High turns, high tokens, retries → Optimize
• low: Long duration → Consider optimization

Key Metrics Thresholds

Common Fixes

Low success rate:

• Add error handling in scripts
• Add input validation
• Clarify ambiguous instructions

High turn count:

• Add decision tree
• Provide more examples
• Use scripts for multi-step operations

High token usage:

• Reduce SKILL.md lines (<500)
• Move details to references/
• Remove redundant examples