evolution-predictor

Other

Predict optimal next evolution actions based on history analysis, including stagnation detection, innovation gap measurement, and skill recommendations. Use when you need to determine what the next evolution cycle should focus on.

Install

openclaw skills install jpeng-evolution-predictor

Evolution Predictor

Predict optimal next evolution actions based on history analysis.

When to Use

  • Determining next evolution focus
  • Need to break stagnation cycles
  • Planning innovation strategy
  • Want proactive evolution guidance

Quick Start

const predictor = require('./skills/evolution-predictor');

// Get prediction for next action
const prediction = predictor.predictNextAction();
console.log(predictor.formatReport(prediction));

// Get recommended skill to create
const skill = predictor.getRecommendedSkill();
console.log('Recommended:', skill.name);

API

predictNextAction(options)

Analyze evolution history and predict optimal next action.

Returns:

  • prediction: Action recommendation with category, priority, description
  • confidence: Prediction confidence (0-1)
  • reasoning: List of reasons for the prediction
  • metrics: Success rate, stagnation level, innovation gap

getRecommendedSkill()

Get a specific skill recommendation based on prediction.

formatReport(prediction)

Generate human-readable prediction report.

Prediction Categories

force_innovate (Critical)

When stagnation level > 60%

  • Break stagnation cycles
  • Create novel skills
  • Implement cross-skill orchestration

prioritize_innovate (High)

When innovation gap > 70%

  • Increase innovation rate
  • Fill capability gaps
  • Address user feature requests

explore_new_domains (Medium)

When success rate > 90%

  • Expand capabilities
  • Add integrations
  • Improve user experience

stabilize (Normal)

Normal operation mode

  • Continue current pattern
  • Monitor for patterns
  • Optimize existing skills

Metrics

  • Success Rate: Percentage of recent successful cycles
  • Stagnation Level: Based on stagnation signal frequency
  • Innovation Gap: How much the system has been optimizing vs innovating

Example Output

🔮 Evolution Predictor
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 Metrics:
  Success Rate: 98%
  Stagnation Level: 85%
  Innovation Gap: 75%

🎯 Prediction:
  Action: force_innovate
  Category: break_stagnation
  Priority: critical
  Description: Force innovation to break stagnation cycle
  Confidence: 85%

💡 Suggested Skills:
  1. Create a novel skill that addresses an unmet need
  2. Implement cross-skill orchestration
  3. Add predictive capabilities