Mirofish Swarm Intelligence

What This Skill Does

MiroFish Swarm Intelligence is a high-fidelity multi-agent simulation engine designed to model complex real-world systems. By leveraging an architecture built on the OASIS framework, it transforms static seed data—such as financial signals, policy drafts, news articles, or narrative prose—into a dynamic digital parallel world. Unlike standard LLM interactions, MiroFish populates an environment with thousands of distinct agents, each possessing unique personas, long-term memory via Zep Cloud, and behavioral logic. These agents interact autonomously, allowing users to simulate social trends, economic fluctuations, and narrative outcomes through iterative temporal updates. The system provides a powerful tool for predictive analysis, enabling users to observe how specific inputs propagate through a simulated social or economic structure over time.

Installation

Ensure you have Node.js 18+ and Python 3.11-3.12 installed. Start by cloning the official repository from GitHub. Use the uv package manager for optimal dependency resolution. Navigate to the project directory and duplicate the provided .env.example file to .env. Configure your environment by providing a valid API key for an OpenAI-SDK-compatible LLM (such as Qwen or GPT-4) and your Zep Cloud API key for long-term memory persistence. Finally, execute the installation command: clawhub install openclaw/skills/skills/adisinghstudent/mirofish-swarm-intelligence to integrate the engine into your OpenClaw environment.

Use Cases

• Economic Forecasting: Model the potential market reaction to a new financial policy by observing simulated consumer behavior.
• Social Trend Analysis: Predict the adoption rate of a new technology or cultural movement based on current news sentiment.
• Narrative Expansion: Use the engine to flesh out complex fictional universes, allowing AI agents to generate plot continuations based on established world-building data.
• Policy Impact Assessment: Simulate the long-term societal effects of draft legislation on diverse demographic groups.

Example Prompts

1. "Set up MiroFish swarm intelligence using the provided policy draft for the urban housing project and simulate outcomes for the next ten years."
2. "Configure MiroFish agents with a skeptical political bias and run a prediction engine to see how they react to the latest economic report."
3. "Build a parallel world simulation based on these news articles and predict the public sentiment towards the new central bank policy."

Tips & Limitations

• Memory Constraints: Ensure Zep Cloud is properly connected; without long-term memory, agents lose coherence over extended simulations.
• LLM Selection: Performance depends heavily on the model used. For complex swarm interactions, larger reasoning models like GPT-4o or Qwen-plus are recommended over smaller local models.
• Complexity Scaling: Simulation depth scales with hardware. Start with a smaller agent count to verify model behavior before scaling to thousands of agents.