An advanced analytical engine that predicts optimal next steps for AI agent growth by identifying stagnation levels and innovation gaps.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install jpeng-evolution-predictor
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install jpeng-evolution-predictor using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Evolution Predictor?
Evolution Predictor is a sophisticated analysis tool designed to guide the development lifecycle of AI agents. By scrutinizing historical evolution data, it identifies when an agent has plateaued in its capabilities and provides data-driven recommendations for the next development cycle. It is an essential component for developers using Openclaw Skills who want to move beyond simple optimization and toward proactive, innovative agent growth.
This skill provides deep visibility into the health of an AI system, measuring the delicate balance between success rates and the need for novel functionality. Whether your agent needs to stabilize its current operations or force a breakthrough in a new domain, Evolution Predictor offers the roadmap necessary to maintain a competitive edge.
Evolution Predictor Use Cases
- Detecting and breaking repetitive stagnation cycles in AI development.
- Planning long-term innovation strategies based on empirical history data.
- Determining the highest priority focus areas for the next evolution sprint.
- Identifying specific new skills to create that will fill significant capability gaps.
How Evolution Predictor Works
- The skill ingests evolution history and success metrics from the AI agent's recent activity.
- It calculates core KPIs including Success Rate, Stagnation Level, and the Innovation Gap.
- A prediction engine categorizes the required action into one of four tiers: Force Innovate, Prioritize Innovate, Explore New Domains, or Stabilize.
- The system generates a human-readable report with actionable recommendations and a confidence score for the proposed strategy.
Evolution Predictor Setup
To integrate this capability into your project, install the Openclaw Skills package and require the predictor module:
npm install @openclaw/evolution-predictor
const predictor = require('./skills/evolution-predictor');
const prediction = predictor.predictNextAction();
console.log(predictor.formatReport(prediction));
Evolution Predictor Data Schema & Taxonomy
The Evolution Predictor organizes its analysis through a structured response object and specific metric thresholds:
| Attribute | Description | Data Type |
|---|---|---|
| success_rate | Percentage of successful recent cycles | Float (0-1) |
| stagnation_level | Frequency of repetitive signals | Percentage |
| innovation_gap | Ratio of optimization vs. innovation | Percentage |
| prediction.category | Strategic path (e.g., force_innovate, stabilize) | String |
| prediction.priority | Urgency level (Critical to Normal) | String |
Evolution Predictor Advanced Features
- Stagnation Detection: Automatically triggers a critical force_innovate alert when stagnation exceeds 60%.
- Skill Recommendation Engine: Suggests specific novel skills to build based on the current innovation gap.
- Reasoning Transparency: Provides a detailed list of logical reasons behind every prediction to ensure developer trust.
- Cross-Skill Orchestration: Identifies opportunities to combine existing Openclaw Skills for complex problem solving.
SKILL.md
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