Skill Creator

Build, refine, and publish OpenClaw skills. Supports six modes.

Modes at a Glance

Mode 1: Create

Build a skill from scratch in 6 steps.

Step 1 — Understand

• - What does this skill do that no existing skill does?
• Who triggers it and when? (the description field drives triggering)
• What CLI tools, APIs, or files does it need?
• What's the output format?

Run scripts/analyze_patterns.py --query "<skill concept>" to see if relevant patterns already exist.

Step 2 — Plan

Step 3 — Init

Scripts are bundled in scripts/ — no external path needed:

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Or locate the skill dir and use relative path:
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This creates:
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Step 4 — Write SKILL.md

Frontmatter rules:
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Body structure:
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Progressive disclosure rules:

• - Frontmatter: always loaded (~100 words) — make it count
• Body: loaded on trigger (<500 lines) — stay under limit
• Bundled resources: loaded on demand — put verbosity here

Step 5 — Package

# package_skill.py is bundled in this skill's scripts/ directory:
SKILL_SCRIPTS="$(dirname "$(find ~/.openclaw/workspace/skills/skill-creator ~/.nvm -name "package_skill.py" 2>/dev/null | head -1)")"
python3 "$SKILL_SCRIPTS/package_skill.py" ~/.openclaw/workspace/skills/<skill-name>

Step 6 — Iterate

Mode 2: Eval

Measure skill quality against defined expectations.

Setup

Eval Run

1. 1. Execute the prompt using current skill
2. Grade against assertions (pass/fail per assertion)
3. Log result to INLINECODE9

Run Report Format

{
  "skill": "skill-creator",
  "version": "1.0.0",
  "timestamp": "2026-02-22T03:00:00Z",
  "pass_rate": 0.85,
  "cases": [
    { "id": "basic-create", "passed": true, "assertions_passed": 3, "assertions_total": 3 }
  ]
}

Mode 3: Improve

Iterate on an existing skill using eval feedback.

Improvement Loop

history.json

[
  {
    "version": "1.0.0",
    "parent": null,
    "expectation_pass_rate": 0.70,
    "is_current_best": false,
    "notes": "Initial version"
  },
  {
    "version": "1.1.0",
    "parent": "1.0.0",
    "expectation_pass_rate": 0.85,
    "is_current_best": true,
    "notes": "Improved trigger description, added Synthesize mode"
  }
]

Mode 4: Benchmark

Blind A/B comparison of two skill versions.

Process

1. 1. Run identical eval suite against version A and version B
2. Collect raw outputs without labels
3. Compare blind (no version labels) → pick winner per case
4. Reveal versions, compute delta
5. Recommend: keep A, adopt B, or cherry-pick specific cases

Benchmark Output

Version A: 1.0.0  pass_rate=0.70
Version B: 1.1.0  pass_rate=0.85
Delta: +0.15 (B wins)
Regressions: 0
Recommendation: Adopt B

Mode 5: Analyze Patterns

Scan installed skills to extract reusable building blocks.

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What it extracts:

• - Trigger phrases — common description keywords that activate skills
• Tool patterns — CLI tools, APIs, Docker patterns used across skills
• Output formats — JSON schemas, markdown templates, log formats
• Structural patterns — how skills organize commands/recipes
• Error handling patterns — retry logic, circuit breakers, fallbacks

See references/patterns.md for the current extracted pattern library.

Mode 6: Synthesize from Patterns

Build a new skill scaffold by combining patterns from the library.

Usage

1. 1. Run Analyze Patterns first
2. Query references/patterns.md for relevant patterns
3. Compose a SKILL.md that combines:

Example

• - Pull trigger phrases from INLINECODE14
• Pull CDP/browser patterns from INLINECODE15
• Pull output format (JSON array) from INLINECODE16
• Synthesize into INLINECODE17

Skill Anatomy Quick Reference

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_meta.json template:

{
  "ownerId": "",
  "slug": "skill-name",
  "version": "1.0.0",
  "publishedAt": null
}

Publishing to OpenClaw Community

Registry: clawhub.com — use the clawhub CLI (already installed).

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1. 1. Ensure _meta.json has correct slug and INLINECODE21
2. Run full eval suite — pass rate must be ≥ 0.80
3. INLINECODE22 (one-time browser auth)
4. INLINECODE23
5. Verify at clawhub.com/skills/

Quality bar for publishing:

• - [ ] Description triggers correctly (test with 3+ natural phrasings)
• [ ] At least 3 concrete command examples with real output
• [ ] Error cases documented
• [ ] Eval pass rate ≥ 0.80
• [ ] _meta.json complete