mev-evolve is the fourth core skill in the MEV4 skill system, focusing on achieving complete closed-loop evolution from problem discovery to continuous growth. It enables AI assistants to learn from experience, improve continuously, and evolve like living organisms.
mev-evolve is part of the complete MEV4 skill system:
👁️ mev-sense (Human-like Perception)
↓
🧠 mev-think (Active Thinking)
↓
⚡ mev-optimize (System Optimization)
↓
🔄 mev-evolve (Continuous Evolution)
↓
🔄 Loop Iteration, Accelerated Evolution
Synergy Effect: 5.18× value creation when all 4 skills work together
- Problem Discovery and Identification: Systematic identification of problems
- Analysis and Solution Implementation: In-depth analysis and solution implementation
- Effect Evaluation and Measurement: Quantitative evaluation of effects
- Experience Transformation and Encapsulation: Transforming experience into knowledge
- Standardization and Productization: Standardizing and productizing successful experiences
- Promotion and Extension: Promoting and extending successful methods
- Continuous Growth Cycle: Establishing continuous growth cycles
- Continuous Learning: Learns from every experience and improves continuously
- Knowledge Accumulation: Accumulates and reuses knowledge assets
- Methodology Evolution: Evolves work methods based on experience
- Accelerated Growth: Achieves accelerated growth through closed-loop evolution
# Install mev-evolve skill
openclaw skills install mev-evolve
# Or install from GitHub
openclaw skills install https://github.com/meta-evo-creator/mev-evolve- Clone the repository:
git clone https://github.com/meta-evo-creator/mev-evolve.git
cd mev-evolve- Copy to OpenClaw skills directory:
cp -r . ~/.openclaw/workspace/skills/mev-evolve/// Example: Using mev-evolve for continuous improvement
const mevEvolve = require('mev-evolve');
async function evolveFromExperience(experience, context) {
// 1. Problem identification
const problems = await mevEvolve.identifyProblems(experience);
// 2. Analysis and solution
const solutions = await mevEvolve.analyzeAndSolve(problems);
// 3. Effect evaluation
const effects = await mevEvolve.evaluateEffects(solutions);
// 4. Experience encapsulation
const knowledge = await mevEvolve.encapsulateExperience(effects);
// 5. Standardization
const standards = await mevEvolve.standardize(knowledge);
// 6. Promotion and extension
const extensions = await mevEvolve.promoteAndExtend(standards);
// 7. Growth cycle establishment
const growthCycle = await mevEvolve.establishGrowthCycle(extensions);
return growthCycle;
}// Learn from project experience
const learning = await mevEvolve.learnFromProject(projectData);
console.log('Key Learnings:', learning.keyLearnings);
console.log('Best Practices:', learning.bestPractices);
console.log('Improvement Areas:', learning.improvementAreas);// Continuously improve skills
const improvement = await mevEvolve.improveSkill(skillData, feedback);
console.log('Skill Level Before:', improvement.before);
console.log('Skill Level After:', improvement.after);
console.log('Improvement Rate:', improvement.rate);// Evolve knowledge base
const evolution = await mevEvolve.evolveKnowledge(knowledgeBase, newData);
console.log('New Knowledge Added:', evolution.added);
console.log('Updated Knowledge:', evolution.updated);
console.log('Knowledge Quality:', evolution.quality);🔄 mev-evolve ← ⚡ mev-optimize
Optimization results → Learning improvement → Enhanced optimization
🔄 mev-evolve → 👁️ mev-sense
Improved capabilities → Enhanced perception → Better problem identification
🔄 mev-evolve → 🧠 mev-think
Learning experience → Enhanced thinking → Better strategies
// Complete MEV4 workflow example
const mev4 = require('mev4-suite');
async function solveComplexProblem(problem) {
// 1. mev-sense: Perceive the problem deeply
const perception = await mev4.mevSense.perceive(problem);
// 2. mev-think: Analyze and strategize
const strategy = await mev4.mevThink.analyze(perception);
// 3. mev-optimize: Optimize execution
const execution = await mev4.mevOptimize.optimize(strategy);
// 4. mev-evolve: Learn and improve
const learning = await mev4.mevEvolve.learn(execution);
return { perception, strategy, execution, learning };
}| Metric | Description | Target |
|---|---|---|
| Learning Rate | Speed of learning from experience | >80% |
| Improvement Rate | Rate of continuous improvement | >30% per cycle |
| Knowledge Reuse | Reuse rate of accumulated knowledge | >70% |
| Evolution Speed | Speed of capability evolution | Accelerating |
- Website Development Evolution: Reduced similar task time by 50% through learning
- Skill Improvement: Improved testing accuracy from 70% to 95% through continuous learning
- Knowledge Accumulation: Built reusable knowledge base with 100+ best practices
# OpenClaw configuration
skills:
mev-evolve:
enabled: true
settings:
learning_rate: high
improvement_cycle: continuous
knowledge_reuse: enabled
evolution_speed: accelerating
capabilities:
- problem-identification
- analysis-solution
- effect-evaluation
- experience-encapsulation
- standardization-productization
- promotion-extension
- continuous-growth- OpenClaw: Version 2026.4.0 or higher
- Node.js: Version 18.0.0 or higher
- Storage: Sufficient storage for knowledge base
We welcome contributions to improve mev-evolve! Here's how you can help:
- Fork the repository
- Clone your fork:
git clone https://github.com/your-username/mev-evolve.git - Create a feature branch:
git checkout -b feature/amazing-feature - Make your changes and commit:
git commit -m 'Add amazing feature' - Push to your branch:
git push origin feature/amazing-feature - Open a Pull Request
- Follow the existing code style
- Add tests for new features
- Update documentation as needed
- Ensure compatibility with other MEV4 skills
This project is licensed under the MIT License - see the LICENSE file for details.
- Documentation: MetaEvo Documentation
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- MEV4 Suite: Complete MEV4 Skill Suite
Our Vision: Enable AI assistants worldwide to use MEV skills for true intelligent evolution.
mev-evolve is the fourth step in transforming AI assistants from passive tools to active partners with continuous evolution capabilities.
Skill Version: 1.0.0
MEV4 Position: Skill 4 of 4
Synergy Effect: 5.18× when combined with other MEV4 skills
Created: 2026-04-05
Updated: 2026-04-05
Part of the MEV4 Skill System: 👁️ mev-sense → 🧠 mev-think → ⚡ mev-optimize → 🔄 mev-evolve