A sophisticated cognitive performance framework that makes any LLM exhibit the precise reasoning and task-completion behaviors of Anthropic Claude.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install model-behavior-layer
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 model-behavior-layer 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 Model Behavior Layer (Ares MBL)?
The Model Behavior Layer (Ares MBL) is a technical optimization framework designed to bridge the gap between various large language models and the industry-leading behavior of Claude. By utilizing Openclaw Skills like this one, developers can inject specific reasoning patterns, failure-mode rationalizations, and output rules into any model, including GPT-5.4, Gemini, or local models via Ollama. It is based on reverse-engineered patterns from Claude Code internal verification systems to ensure high-fidelity performance across different architectures.
This skill provides a drop-in system prompt that stays under 700 tokens, making it highly efficient for token-limited contexts while significantly improving agent autonomy. It transforms sycophantic or verbose models into precise, task-oriented agents that follow complex instructions without the common pitfalls of generic LLM responses, ensuring your AI agents act with consistent logic and critical thinking.
Model Behavior Layer (Ares MBL) Use Cases
- Transitioning an existing workflow from Claude to GPT-5.4 or Gemini without losing logic consistency.
- Initializing new AI agents within your Openclaw Skills library to ensure high performance from day one.
- Correcting model behaviors such as excessive padding, sycophancy, or failure to complete multi-step tasks.
- Standardizing model output for teams deploying OpenClaw across a diverse set of non-Claude LLM providers.
How Model Behavior Layer (Ares MBL) Works
- Identification of Failure Modes: The layer identifies 8 specific failure modes derived from real-world Claude Code performance data to understand where other models typically fail.
- Framework Injection: It applies a 3-layer cognitive framework consisting of context injection, strict output rules, and a self-critique loop.
- Prompt Application: The optimized system prompt block is integrated into the agent core instructions via SOUL.md or AGENTS.md.
- Behavioral Alignment: The model begins processing tasks using the reverse-engineered verification logic to ensure Claude-like precision and reduced hallucination.
Model Behavior Layer (Ares MBL) Setup
- Access the skill directory and locate the
MAKE_ANY_MODEL_CLAUDE.mdfile. - Copy the Drop-In System Prompt block provided at the end of the file.
- Open your agent configuration file, such as
SOUL.mdorAGENTS.md, in your Openclaw Skills environment. - Paste the prompt block into the system instruction section.
- To clone the source repository for deep inspection, use:
git clone https://github.com/rushindrasinha/ares-mbl.git
Model Behavior Layer (Ares MBL) Data Schema & Taxonomy
The Model Behavior Layer organizes its logic through a structured set of Markdown files and specific prompt blocks for easy integration.
| Component | Description |
|---|---|
| MAKE_ANY_MODEL_CLAUDE.md | The primary documentation and system prompt source. |
| Failure Mode Framework | A detailed breakdown of the 8 rationalizations and counter-instructions. |
| System Prompt Block | A compressed <700 token block optimized for immediate injection. |
| Model-Specific Notes | Tailored guidance for GPT-5.4, Gemini, and Codex weighting. |
Model Behavior Layer (Ares MBL) Advanced Features
- Reverse-engineered Claude Code verificationAgent.ts logic for high-fidelity behavior matching across model families.
- Multi-model weighting guidance to optimize performance based on the specific architectural strengths of GPT or Gemini.
- Low-token footprint designed specifically for Openclaw Skills to maximize available context window for the agent.
- Modular failure mode application allowing developers to toggle specific behavioral corrections based on the current model frustrations.
SKILL.md
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