A professional-grade framework for creating, hardening, and validating high-quality AI agent skills for production environments.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skill-enhance
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install skill-enhance using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Skill Enhance?
Skill Enhance is a robust development utility designed to move beyond basic scaffolding. It focuses on the technical truth of AI agent performance by prioritizing trigger accuracy, execution clarity, and rigorous output contracts. This tool ensures that Openclaw Skills are not just structurally valid but are dependable assets that other LLMs can execute with high precision.
By treating skill development as a first-class engineering task, Skill Enhance bridges the gap between a loose idea and a publish-ready package. It guides developers through strengthening validation coverage and artifact completeness, ensuring that every skill is testable, shareable, and resilient enough for public or team-wide release.
Skill Enhance Use Cases
- Creating a new AI agent skill from scratch with a focus on reliability.
- Enhancing existing Openclaw Skills to improve their trigger accuracy and reduce false positives.
- Strengthening output contracts to ensure agents produce consistent, actionable deliverables.
- Conducting publish-readiness reviews before releasing skills to a wider audience.
- Adding comprehensive evaluation suites and metadata to existing skill packages.
How Skill Enhance Works
- Objective Capture: The skill identifies the core purpose, target audience, and specific request patterns that should trigger the skill.
- Contract Definition: It establishes a clear output structure and validation expectations before any markdown is written.
- Package Building: Systematic creation of the full artifact suite, including SKILL.md, README.md, agent configurations, and evaluation files.
- Trigger Optimization: Refining the trigger description to eliminate over-triggering and ensure the skill activates only when appropriate.
- Reliability Hardening: Reviewing instructions to ensure they are actionable and drive toward concrete delivery rather than abstract advice.
- Validation & Review: Running the skill through quality gates to assess execution reliability and publish readiness.
Skill Enhance Setup
To begin enhancing your Openclaw Skills, ensure the skill-enhance package is available in your agent environment. You can initialize a new skill enhancement workflow using the following pattern:
# Example of initiating a skill review or creation
openclaw skill-enhance --target ./my-new-skill
Ensure your project includes a references/ directory with trigger-design.md and output-contracts.md to support the hardening process.
Skill Enhance Data Schema & Taxonomy
Skill Enhance organizes data into a comprehensive package structure to ensure portability and clarity:
| File | Description | Requirement |
|---|---|---|
SKILL.md |
Core agent instructions and frontmatter | Mandatory |
README.md |
Human-facing documentation and usage guides | Recommended |
agents/openai.yaml |
Platform-specific agent configurations | Optional |
evals/evals.json |
Test cases covering happy-paths and boundaries | Mandatory for Release |
references/ |
Technical specs for triggers and contracts | Recommended |
Skills are classified into modes such as New Skill Creation, Trigger Optimization, or Quality Review to maintain a structured taxonomy.
Skill Enhance Advanced Features
- Multi-Agent Support: Specifically designs skills that can be reliably invoked by other LLMs and agents.
- Trigger Boundary Review: Advanced logic to prevent skill hijacking and handle negative trigger cases.
- Quality Gates: Systematic checks for LLM reliability, execution transitions, and artifact completeness.
- Publish-Readiness Scoring: Categorizes skills by maturity levels: Draft, Internally Usable, Release-Candidate, or Publish-Ready.
- Automated Eval Generation: Creates realistic prompt scenarios in
evals.jsonto verify skill performance.
SKILL.md
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