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self-evolve-agent

Run A/B tests and evaluate experiments for AI agent self-evolution.

10|2|Updated Apr 3, 2026
Search Tags:#self-improvement#meta-skill#a/b testing#autonomous-agent#ai-evolution
One-click install
npx skills add https://github.com/dglijin-oss/chinese-metaphysics-skills --skill self-evolve-agent
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: self-evolve-agent
Source: https://github.com/dglijin-oss/chinese-metaphysics-skills/tree/main/self-evolve-agent
Command: npx skills add https://github.com/dglijin-oss/chinese-metaphysics-skills --skill self-evolve-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, pandas, numpy, scikit-learn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of AI agents hitting external bottlenecks and the need for proactive self-upgrade. It allows agents to find and test their own solutions, adapt, and permanently upgrade their meta-capabilities or workflows without human intervention.

Core Features & Use Cases

  • Autonomous Evolution: Enables AI agents to evolve through a continuous loop of "Sense Gap → Search Solutions → Run A/B Tests → Pick Winner → Solidify".
  • Proactive Self-Upgrade: Allows agents to install new tools, optimize cron schedules, and integrate with other skills like dmn-default-mode-network.
  • Integration with DMN: Serves as the "Experiment Runner" for ideas generated by the dmn-default-mode-network.

Quick Start

To start the self-evolve process, use the command: self-evolve tick

Frequently Asked Questions about self-evolve-agent

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I enable autonomous self-improvement for an AI agent?▼

Autonomous self-improvement for AI agents is enabled by running a continuous loop that senses capability gaps, searches for solutions, runs A/B tests, and solidifies winning workflows without human intervention.

How does A/B testing work for autonomous AI agent evolution?▼

A/B testing for autonomous AI agent evolution works through a state-machine triggered by ticks, which executes experiments to evaluate new solutions and permanently upgrade the agent's meta-capabilities.

Do I need Python and scikit-learn to run autonomous agent evolution experiments?▼

Yes, you need Python along with pandas, numpy, and scikit-learn to execute the autonomous agent evolution engine and observe its A/B testing and evaluation processes.

Can I integrate the self-evolve agent with the dmn-default-mode-network skill?▼

Yes, you can integrate the self-evolve agent with the dmn-default-mode-network skill, where it functions as the experiment runner for ideas generated by the default mode network.

What is the best way to proactively upgrade an AI agent's tools and cron schedules?▼

The best way to proactively upgrade an AI agent's tools and cron schedules is to trigger an autonomous evolution tick, allowing the agent to install new tools and optimize workflows automatically.

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