model-prompt-adapter

What This Skill Does

The Model Prompt Adapter is a specialized architectural utility for OpenClaw agents that simplifies multi-model orchestration. By injecting lightweight, universal guardrails into your existing workspace files (such as AGENTS.md, TOOLS.md, and VOICE.md), this skill ensures that your agent behaves consistently regardless of the underlying LLM provider. Rather than relying on fragile conditional logic or maintaining multiple versions of your context files, the Model Prompt Adapter introduces instructions that act as 'fallback-safe' constraints. These instructions are designed to be redundant for primary, high-reasoning models like Claude Opus, while acting as critical safety nets for fallback models like GPT-5.4 that may be prone to specific failure modes such as prompt leaking, scope creep, or fabricated completion. This approach ensures your agent's personality and operational integrity remain stable during automated failover sequences.

Installation

You can integrate this skill into your project by running the following command in your terminal:

clawhub install openclaw/skills/skills/globalcaos/model-prompt-adapter

Once installed, review the suggested Markdown blocks provided in the documentation and append them to your AGENTS.md, TOOLS.md, and custom configuration files. The total footprint is approximately 600 characters, making it highly efficient for cached prompt tokens.

Use Cases

• Multi-Model Fallback Chains: Ideal for production environments where an agent switches from a primary model to a secondary fallback model if the primary API returns an error.
• Cross-Model Compatibility: Perfect for developers who want to share workspace context files across different LLM families without worrying about provider-specific quirks.
• Sensitive Configuration Protection: When workspace files contain sensitive data (API keys, JIDs), the adapter enforces privacy guardrails that mitigate the risk of prompt leaking by lower-reasoning models.
• System Reliability: Ensures consistent behavior for autonomous agents performing multi-step tasks where reliability is paramount.

Example Prompts

1. "Apply the Model Prompt Adapter guardrails to my current AGENTS.md file to ensure consistency when switching from Claude to GPT-5.4."
2. "Open my TOOLS.md file and verify that the Privacy Guardrail snippet is correctly placed to prevent accidental leakage of system data."
3. "Refactor my VOICE.md file using the Fallback Safety patterns to prevent fabricated tool completion during API downtimes."

Tips & Limitations

• Cache Efficiency: The adapter is designed to be lean (approx. 150 tokens), making it a great candidate for prompt caching strategies.
• Behavior Predictability: While these guardrails are harmless to primary models, they significantly reduce the entropy of secondary models. Always test your agent's behavior in a sandbox environment after implementation.
• Limitations: This is not a substitute for robust error handling in your application code. It serves as an instruction-level optimization layer to guide LLM reasoning, not a replacement for backend request/response validation.