A technical guide and toolset for optimizing agent skill discoverability through semantic search alignment and description engineering.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skill-search-optimizer
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-search-optimizer 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 Search Optimizer?
The Skill Search Optimizer is a specialized utility designed to help developers maximize the reach and ranking of their Openclaw Skills. Modern registries like ClawdHub utilize vector-based semantic search, which means that finding a skill depends more on the meaning and context of its description than on simple keyword matching. This tool provides the framework necessary to align skill metadata with the embedding models used by registry indexes.
By focusing on the frontmatter description as the primary ranking factor, this skill enables developers to craft high-signal metadata that matches natural language queries. It bridges the gap between technical functionality and user discoverability, ensuring that your tools are visible when users search for specific technologies, workflows, or troubleshooting solutions.
Skill Search Optimizer Use Cases
- Improving the search ranking of a published skill that isn't appearing in relevant results.
- Engineering high-performance descriptions for new Openclaw Skills during the initial development phase.
- Benchmarking a skill's visibility against competitors to identify keyword gaps.
- Troubleshooting why specific semantic queries are failing to surface a relevant tool.
- Transitioning from keyword-stuffed metadata to natural language descriptions that favor vector embeddings.
How Skill Search Optimizer Works
- The developer identifies the core value proposition and primary tools associated with their Openclaw Skills.
- Use proven description formulas to combine action verbs, specific technical scopes, and "Use when" triggers.
- Integrate both formal terminology and common industry synonyms to broaden the semantic search surface.
- Deploy changes via the CLI to trigger an immediate re-indexing of the skill's OpenAI-based embeddings.
- Conduct manual search testing using specific, broad, and problem-oriented queries to verify ranking improvements.
Skill Search Optimizer Setup
To begin optimizing your Openclaw Skills, ensure you have the registry CLI tools available for testing and publishing.
# Verify you have the latest molthub utility installed
npx molthub@latest --version
# Test the current search visibility of your skill
npx molthub@latest search "your primary keyword"
Skill Search Optimizer Data Schema & Taxonomy
Optimization efforts are prioritized based on how the registry indexes skill data. The following hierarchy identifies where to focus your effort:
| Field | Indexing Priority | Impact on Search Ranking |
|---|---|---|
description |
Primary | Highest; this is the main source for vector embedding. |
name / slug |
Secondary | Significant; matches direct queries and creates URL relevance. |
body markdown |
Tertiary | Moderate; contributes to secondary signals and content previews. |
Skill Search Optimizer Advanced Features
- Description Formulas: specialized templates for tool-centric, pattern-based, or workflow-oriented skills.
- Semantic Keyword Strategy: Natural inclusion of synonyms like "bastion host" and "jump host" to capture diverse user intents.
- Visibility Test Matrix: A structured method for auditing ranking across broad, specific, and troubleshooting-focused queries.
- Competitive Positioning: Strategies for differentiating Openclaw Skills by targeting specific problem-oriented language that competitors overlook.
SKILL.md
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