A meta-analysis tool that examines AI evolution memory graphs to detect stagnation and optimize success rates.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install evolution-state-analyzer
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 evolution-state-analyzer 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 State Analyzer?
The evolution-state-analyzer is a specialized diagnostic tool designed to provide meta-analysis for autonomous evolution processes. By examining the memory_graph.jsonl file, this skill identifies critical patterns in agent behavior, such as repetitive cycles that do not lead to improvement or specific logic clusters that consistently fail. It is a vital component for developers building complex agentic workflows within the Openclaw Skills ecosystem, offering a clear window into the logical progression of AI cycles.
This skill synthesizes raw historical data into a structured report, helping developers understand the efficacy of their gene-based logic. Whether you are troubleshooting a stalled agent or looking to maximize the success rate of your deployments, the evolution-state-analyzer provides the necessary technical insights to guide your future development and evolution strategies using Openclaw Skills.
Evolution State Analyzer Use Cases
- Detecting stagnation in AI agents when they enter repetitive loops without measurable improvement.
- Evaluating gene efficacy to identify which logic components yield the highest success rates.
- Conducting failure cluster analysis to pinpoint systemic issues in the evolution process.
- Generating trend reports to visualize evolution score improvements over long-term operations.
How Evolution State Analyzer Works
- The skill accesses the local memory_graph.jsonl file where evolution data is stored.
- It parses the history of the evolution process to track performance metrics across multiple cycles.
- The analyzer identifies stagnation streaks by comparing consecutive failures or plateaus.
- It calculates the performance of individual genes to determine their contribution to the overall success rate.
- A comprehensive JSON report is generated, offering actionable recommendations for the next Openclaw Skills evolution cycle.
Evolution State Analyzer Setup
To integrate this analyzer into your project, install it via your preferred package manager and reference it in your evolution scripts:
npm install evolution-state-analyzer
Basic implementation in your code:
const analyzer = require('./index');
// Analyze the current state of the memory graph
const insights = await analyzer.analyzeState();
console.log(JSON.stringify(insights, null, 2));
Evolution State Analyzer Data Schema & Taxonomy
The skill produces a structured data output derived from the evolution memory graph:
| Property | Type | Description |
|---|---|---|
| total_cycles | Integer | The total number of evolution cycles analyzed. |
| success_rate | Float | The percentage of cycles that reached a successful conclusion. |
| stagnation_detected | Boolean | Indicates if the agent is stuck in a non-improving loop. |
| top_genes | Array | A list of genes ranked by their success rate within Openclaw Skills. |
| recommendations | Array | Specific suggestions for improving the next evolution cycle. |
Evolution State Analyzer Advanced Features
- Automated intent switching recommendations to break through stagnation streaks.
- Granular gene performance tracking to deprecate low-efficacy logic.
- Systemic failure grouping to help developers debug complex logic errors.
- Seamless integration with other monitoring tools in the Openclaw Skills framework.
SKILL.md
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