Install
$ agentstack add skill-dp-archive-archive-skill-evolver ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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 [--skill ] [--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 --failed
scripts/extract_issue_context.py --trace-id --show-llm
scripts/extract_issue_context.py --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](references/issue-patterns.md):
| Issue Type | Likely Fix Location | |------------|-------------------| | executionfailure | scripts/, error handling | | highturncount | SKILL.md clarity, add examples | | toolerrors | scripts/, input validation | | hightokenusage | SKILL.md verbosity, progressive disclosure | | repeatedtoolcalls | SKILL.md decision trees |
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:
- For script errors: Fix scripts, add validation, improve error messages
- For efficiency issues: Add examples, decision trees, clearer instructions
- For token issues: Reduce SKILL.md, move content to references/
- For trigger issues: Update frontmatter description
- 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
| Metric | Warning | Action | |--------|---------|--------| | successrate | 4 | Simplify workflow | | avgtokens | >30000 | Reduce context | | duration_ms | >60000 | Optimize scripts |
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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: dp-archive
- Source: dp-archive/archive
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.