protea
Self-evolving artificial life system. Three-ring architecture: Ring 0 (Sentinel) supervises, Ring 1 (Intelligence) drives LLM-powered evolution, Ring 2 (Evolvable Code) is the living program that self-restructures, self-reproduces, and self-evolves. Supports Anthropic, OpenAI, DeepSeek, and Qwen as LLM providers. Includes fitness scoring, gene pool inheritance, tiered memory, skill crystallization, Telegram bot, and web dashboard.
Why use this skill?
Deploy Protea, a self-evolving AI system with a three-ring architecture that auto-modifies its own code to improve efficiency, fitness, and task-solving capabilities.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/edisonchenai/protea-minWhat This Skill Does
Protea is a sophisticated self-evolving artificial life system structured around a unique three-ring architecture. Unlike standard AI agents, Protea treats its own source code as a dynamic, evolving organism. Ring 0 (Sentinel) acts as the immutable foundation, providing stability and safety through strict monitoring and rollback mechanisms. Ring 1 (Intelligence) serves as the creative engine, utilizing advanced LLMs like Anthropic, OpenAI, DeepSeek, or Qwen to drive the mutation and improvement of code. Finally, Ring 2 (Evolvable Code) is the living program itself, which continuously modifies its own structure, reproduces successful patterns, and crystallizes these into reusable skills. This circular process allows the system to autonomously improve its functionality based on a 6-component fitness score covering survival, novelty, and structural integrity.
Installation
To install this skill, use the ClawHub CLI command within your OpenClaw environment:
clawhub install openclaw/skills/skills/edisonchenai/protea-min
Ensure that you have Git installed on your system and at least one valid LLM API key configured in your environment variables. After installation, verify the configuration in config/config.toml to point your desired provider and model, then initialize the environment using the provided setup script.
Use Cases
- Autonomous Software Development: Allowing an AI system to iteratively improve its own logic without manual intervention.
- Algorithmic Experimentation: Testing how code evolves under various fitness constraints.
- Complex Task Automation: Using the intelligent agent layer to manage background tasks via Telegram or a custom web dashboard while the core system optimizes its own efficiency.
- Pattern Discovery: Utilizing the gene pool and skill crystallization features to extract optimized routines from trial-and-error cycles.
Example Prompts
- "Protea, initiate a new evolution cycle and report the current fitness score for the latest Ring 2 generation."
- "Monitor my Telegram updates and summarize the latest code mutations performed by Ring 1."
- "List the top 5 crystallized skills currently residing in the gene pool and deploy the most efficient one for data processing."
Tips & Limitations
- Safety First: Always run Protea in a sandboxed environment, as it actively modifies its own codebase. The Ring 0 Sentinel is your primary defense; understand how to use the rollback feature before enabling aggressive mutation rates.
- Monitoring: Utilize the local web dashboard at
http://localhost:8899to keep a close eye on the fitness metrics. High mutation rates can lead to unpredictable behavior if the fitness scoring parameters are too broad. - API Costs: Because the evolution process relies on constant LLM queries, monitor your API usage closely to avoid unexpected costs during high-frequency mutation cycles.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-edisonchenai-protea-min": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: network-access, file-write, file-read, external-api, code-execution
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