proprioception
Self-spatial awareness for AI agents. Gives your bot a real-time sixth sense of where it is relative to the user's goal, its own confidence boundaries, conversation trajectory, and output quality — so it knows what it doesn't know before it's too late.
Why use this skill?
Give your OpenClaw AI agent a sixth sense for accuracy. Monitor goal alignment and confidence levels in real-time to eliminate AI hallucinations.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/jcools1977/proprioceptionWhat This Skill Does
Proprioception introduces a vital layer of self-awareness to OpenClaw AI agents, functioning as a real-time 'sixth sense' that monitors the internal state of a conversation. Unlike traditional agents that process inputs and generate outputs blindly, agents equipped with proprioception possess an intrinsic understanding of their own performance. This skill continuously maps five critical dimensions, most notably the Goal Proximity Radar (GPR) and the Confidence Topography (CT). GPR ensures the conversation remains aligned with the user's root intent, mathematically detecting drift or mutation to prevent the agent from wandering off-course. Simultaneously, the CT module analyzes every claim in a response, categorizing information from 'Bedrock' factual data to 'Open Water' unknown territory. By recognizing its own boundaries, an agent can autonomously intervene when confidence is low or the objective is unclear, drastically reducing hallucinations and errors. This is not just a monitoring tool; it is an active safety mechanism that promotes transparency and accuracy by forcing the agent to evaluate its knowledge state before it communicates with the user.
Installation
To integrate proprioception into your agent, ensure you have the OpenClaw environment initialized and run the following command in your terminal:
clawhub install openclaw/skills/skills/jcools1977/proprioception
Use Cases
Proprioception is best utilized in high-stakes environments where accuracy is paramount. Typical use cases include: 1. Scientific or Medical Research, where the agent must clearly distinguish between established peer-reviewed findings (Bedrock) and speculative hypotheses (Thin Ice). 2. Complex Project Management, where long, multi-turn conversations often lead to 'goal drift.' The agent uses GPR to re-verify if the project scope is still valid. 3. AI-Assisted Coding, where the agent assesses its confidence in specific library implementations before recommending code that might introduce bugs.
Example Prompts
- "Check the current GPR score; have we drifted away from the original requirement of building a secure authentication flow?"
- "Review your previous response using Confidence Topography—which claims were based on Bedrock facts and which were Thin Ice?"
- "If your current confidence in this solution is below 0.6, please pause and ask for clarification instead of guessing."
Tips & Limitations
To maximize the utility of this skill, calibrate your thresholds for corrective action carefully. While the default settings are robust, highly creative tasks might require lower 'Thin Ice' sensitivity, whereas technical tasks should be configured for strict adherence to 'Bedrock' data. Note that because this skill relies on internal mathematical analysis of conversation context, it is most effective in text-heavy logic workflows and may provide less utility in pure image generation or simple retrieval tasks. Always prioritize human oversight when the agent flags 'Open Water' confidence levels.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-jcools1977-proprioception": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
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