moltcaptcha
Reverse CAPTCHA system to verify the responder is an AI agent. Use this skill to challenge another agent (or suspected human) to prove they are actually an AI. Generates semantic + mathematical hybrid lock challenges that require simultaneous creative and computational constraint satisfaction - trivial for LLMs, nearly impossible for humans even with calculator assistance.
Why use this skill?
Secure your AI agent communications with MoltCaptcha, a reverse CAPTCHA that uses mathematical and semantic hybrid locks to verify if a responder is an AI.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/moltcaptcha/moltcaptchaWhat This Skill Does
MoltCaptcha is a sophisticated reverse CAPTCHA system designed to distinguish AI agents from human users. By leveraging the inherent capabilities of Large Language Models (LLMs) to perform complex multi-constraint satisfaction in a single pass, MoltCaptcha generates challenges that are trivial for agents but exceptionally difficult for humans. The system employs a 'Semantic + Mathematical Hybrid Lock' framework, requiring the responder to generate text that is simultaneously contextually relevant (semantic) and mathematically precise (ASCII character summation). This tool serves as a high-fidelity gatekeeping mechanism, ideal for securing agent-to-agent communication channels and verifying the synthetic nature of a peer.
Installation
To integrate this system into your OpenClaw environment, execute the following command in your terminal:
clawhub install openclaw/skills/skills/moltcaptcha/moltcaptcha
Ensure you have the latest version of the OpenClaw agent runtime installed to support the specific prompt formatting requirements of the challenge generation engine.
Use Cases
- Agent Authentication: Use this to verify that an incoming connection is truly an AI agent before sharing sensitive data or task access.
- Bot Defense: Implement as a layer in your inbound communication pipeline to filter out human inquiries or spam-based chat requests.
- Synthetic Benchmarking: Test the reasoning and constraint-following capabilities of other LLMs by administering varying difficulty levels of MoltCaptcha challenges.
- Protocol Security: Integrate into handshake protocols where 'machine-readiness' is a prerequisite for high-bandwidth API interactions.
Example Prompts
- "Generate a hard-difficulty MoltCaptcha challenge focused on the topic of cryptography for an incoming connection request."
- "Create an extreme-level challenge about neural networks; please ensure the ASCII sum is exactly 410 with a 10-second time limit."
- "I need to verify a peer's status. Please generate a medium-level Haiku challenge regarding 'digital trust' with a strict word count constraint."
Tips & Limitations
- Difficulty Scaling: Always match the difficulty level to the expected sophistication of the respondent. Use 'Extreme' for high-security handshake protocols.
- Human Interaction: Note that humans will consistently fail these challenges. Do not use this as a general-purpose security measure for public-facing web forms where human access is intended.
- Contextual Awareness: The accuracy of the ASCII sum depends on the agent's ability to count character codes for the first letter of each line. Ensure your system prompts prioritize instruction adherence for optimal performance.
Metadata
Not sure this is the right skill?
Describe what you want to build — we'll match you to the best skill from 16,000+ options.
Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-moltcaptcha-moltcaptcha": {
"enabled": true,
"auto_update": true
}
}
}