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jpengcheng523-netizen

evolution-predictor

by jpengcheng523-netizen · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install jpeng-evolution-predictor
Description
Predict optimal next evolution actions based on history analysis, including stagnation detection, innovation gap measurement, and skill recommendations. Use...
README (SKILL.md)

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
Usage Guidance
This skill appears to do what it says: it reads local evolution history files, computes metrics, and returns recommendations. Before installing, verify the following: 1) you are comfortable with the skill reading files from the agent workspace (default: /root/.openclaw/workspace/memory/evolution) or supply an explicit, non-sensitive evolutionPath when invoking it; 2) inspect the full index.js (the provided file was truncated at the end) to ensure it exports the expected functions and does not include any unexpected network or write operations; 3) if you plan to run this in a different environment, confirm the file paths and permissions are appropriate so it cannot be pointed at sensitive system paths. If those checks look good, the skill is internally coherent and low-risk.
Capability Analysis
Type: OpenClaw Skill Name: jpeng-evolution-predictor Version: 1.0.0 The evolution-predictor skill analyzes local evolution history logs within the OpenClaw workspace to suggest future development actions. The implementation in index.js reads files from a specific path (/root/.openclaw/workspace/memory/evolution) to calculate metrics like stagnation and success rates. The code and SKILL.md instructions are consistent with the stated purpose and do not exhibit signs of data exfiltration, malicious execution, or prompt injection.
Capability Assessment
Purpose & Capability
The skill claims to analyze evolution history and recommend next actions; index.js reads local evolution history files, computes metrics, and returns recommendations — this matches the stated purpose. No unrelated credentials or external services are required.
Instruction Scope
SKILL.md describes using the module API and the implementation reads files from a specific default path (/root/.openclaw/workspace/memory/evolution). The SKILL.md does not explicitly document this filesystem path or the exact file naming expectations, so users should be aware the skill will read local workspace files.
Install Mechanism
No install spec; the skill is instruction- and code-based only and does not download or install external packages or binaries. This is low-risk from an install-mechanism perspective.
Credentials
The skill requests no environment variables, credentials, or external config paths. Its filesystem access is limited to an evolution workspace path (configurable via options), which is consistent with its functionality.
Persistence & Privilege
always is false and the skill does not modify global agent settings. It only reads local files and produces recommendations; it does not attempt to persist credentials or alter other skills.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install jpeng-evolution-predictor
  3. After installation, invoke the skill by name or use /jpeng-evolution-predictor
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of the evolution-predictor skill. - Predicts the optimal next evolution action by analyzing historical data for stagnation, innovation gaps, and success rates. - Detects stagnation cycles, measures innovation gaps, and recommends actions such as force innovation or domain exploration based on metrics. - Provides actionable skill recommendations and a formatted report for easy interpretation. - Includes categories for prediction priorities: force_innovate, prioritize_innovate, explore_new_domains, and stabilize. - Exposes a simple API: predictNextAction, getRecommendedSkill, and formatReport.
Metadata
Slug jpeng-evolution-predictor
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is evolution-predictor?

Predict optimal next evolution actions based on history analysis, including stagnation detection, innovation gap measurement, and skill recommendations. Use... It is an AI Agent Skill for Claude Code / OpenClaw, with 132 downloads so far.

How do I install evolution-predictor?

Run "/install jpeng-evolution-predictor" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is evolution-predictor free?

Yes, evolution-predictor is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does evolution-predictor support?

evolution-predictor is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created evolution-predictor?

It is built and maintained by jpengcheng523-netizen (@jpengcheng523-netizen); the current version is v1.0.0.

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