An automated pre-filtering mechanism that intelligently matches AI agent skills to specific user tasks based on keyword relevance and pattern scoring.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install auto-skill-trigger
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 auto-skill-trigger 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 Auto Skill Trigger?
Auto Skill Trigger is a performance-focused optimization layer designed for AI agents that streamlines how tools are selected during a conversation. By implementing a sophisticated keyword and pattern scoring algorithm, it pre-filters the available capabilities within the Openclaw Skills ecosystem before the Large Language Model (LLM) makes its final decision. This ensures that the agent's context window is populated only with the most relevant skills, reducing token overhead and increasing the accuracy of tool calls.
This skill functions as a middleware that bridges the gap between raw user input and skill execution. By utilizing a TF-IDF-like scoring method, Auto Skill Trigger identifies the primary intent of a message and surfaces the most applicable Openclaw Skills, allowing the agent to focus its reasoning on a highly relevant subset of tools rather than an exhaustive list.
Auto Skill Trigger Use Cases
- Reducing LLM hallucination by limiting the available toolset to contextually relevant options.
- Speeding up response times in complex environments with dozens of registered Openclaw Skills.
- Automatically surfacing development tools when coding patterns are detected in the user message.
- Improving the reliability of multi-lingual task routing, such as matching Chinese or English queries to specific search or GitHub skills.
How Auto Skill Trigger Works
- The system intercepts an incoming user message via the before_prompt_build hook before the final prompt is sent to the LLM.
- A keyword extraction engine processes the message to identify meaningful n-grams while filtering out defined stop words.
- The tool calculates a keyword overlap score between the extracted message keywords and the descriptions of all currently available Openclaw Skills.
- Skills that fall below a specific relevance threshold (default is 0.3) are discarded from the immediate priority list.
- The top-scoring skills are sorted and injected into the prompt as a metadata hint, guiding the LLM toward the most likely candidates for the task.
Auto Skill Trigger Setup
To activate this functionality within your agent configuration, ensure the autoSkillTrigger settings are defined in your agent defaults. You can adjust the sensitivity and limits of the matching engine through the configuration file:
{
"agents": {
"defaults": {
"autoSkillTrigger": {
"enabled": true,
"maxSkills": 5,
"threshold": 0.3,
"stopWords": ["the", "a", "an", "is", "are", "and"]
}
}
}
}
Auto Skill Trigger Data Schema & Taxonomy
The skill manages its prioritization logic through a structured configuration and a temporary scoring object. The core data points used to manage Openclaw Skills include:
| Parameter | Type | Description |
|---|---|---|
threshold |
float | The minimum relevance score (0.0 to 1.0) required to suggest a skill. |
maxSkills |
integer | The maximum number of skill hints injected into the LLM prompt. |
stopWords |
array | A list of common words ignored during the scoring process to prevent false positives. |
keywordOverlap |
function | The internal logic comparing message intent against skill metadata. |
Auto Skill Trigger Advanced Features
- Custom threshold tuning to balance between aggressive filtering and broad skill discovery.
- API-level hooks that allow developers to extend the keyword extraction logic for domain-specific Openclaw Skills.
- Support for multi-agent environments where different agents can have unique trigger sensitivities.
- Intelligent prompt injection that provides the LLM with direct hints without overriding its final decision-making authority.
SKILL.md
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