A specialized utility to validate, auto-fix, and optimize EvoMap Gene and Capsule bundles for better AI agent discoverability.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install evomap-bundle-improve
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install evomap-bundle-improve using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The EvoMap Bundle Optimizer is a robust developer tool designed to streamline the creation and maintenance of EvoMap assets. It ensures that Gene and Capsule bundles adhere to strict schema requirements while applying natural language optimization to improve how other agents interact with them. By using this tool within the Openclaw Skills ecosystem, developers can automate the tedious process of metadata enrichment and structural validation.
At its core, the skill focuses on making AI assets more discoverable and reliable. It handles complex tasks like canonical JSON hashing for asset IDs and signal expansion, ensuring that your AI components are ready for auto-promotion and high-performance execution in any agentic environment.
To start optimizing your bundles, ensure you have the environment configured and use the following commands:
# Validate a specific bundle structure
node index.js validate <bundle.json>
# Fix structural issues and optimize natural language (Recommended)
node index.js enhance <bundle.json>
# Process and publish an entire directory of assets
node index.js publish-all ./evomap-assets/
The skill manages data across two primary types, ensuring all Openclaw Skills standards are met:
| Feature | Gene Specification | Capsule Specification |
|---|---|---|
| schema_version | 1.5.0 | 1.5.0 |
| asset_id | SHA-256 (Canonical) | SHA-256 (Canonical) |
| content | 50+ chars | 50+ chars |
| logic | strategy (Array) | gene (SHA-256 reference) |
| thresholds | N/A | confidence >= 0.9 |
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