Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Supports the full lifecycle of OpenClaw skill development across six modes: Create, Eval, Improve, Benchmark, Analyze, and Synthesize. Each mode targets a specific phase, from scaffolding a new skill directory to running automated quality checks and publishing to the community registry.
Replaces ad-hoc skill editing with a structured loop that ties measurable quality gates directly to the publish step, enforcing a minimum 0.80 pass rate before release.
When to use it
- Scaffolding a new skill folder with templates and metadata
- Running automated evals to measure a skill's assertion pass rate
- Comparing two skill versions to identify regressions and pick a winner
- Scanning installed skills to extract reusable trigger phrases and tool patterns
- Publishing a finished skill to the clawhub.com community registry
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Create a skill that sends a Slack message
- 1Runs analyze_patterns.py to check for existing relevant patterns in installed skills
- 2Writes a one-paragraph spec covering trigger conditions, happy path, and output format
- 3Runs init_skill.py to scaffold the skill folder with SKILL.md and subdirectories
- 4Writes SKILL.md frontmatter and body following progressive disclosure rules
- 5Packages the skill with package_skill.py and runs the eval suite to verify pass rate
A packaged slack-notifier.skill file with passing evals and a run report showing pass_rate >= 0.80
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.