Safe Evolver is a robust AI evolution engine that analyzes execution logs to identify performance bottlenecks and apply safety-constrained behavioral improvements.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install safe-evolver
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 safe-evolver 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 Safe Evolver?
Safe Evolver is a specialized framework designed to facilitate the continuous improvement of autonomous agents within the Openclaw Skills ecosystem. It functions by capturing detailed runtime history, including tool interactions, response quality, and error states, and then synthesizing this data to propose or execute safe optimizations.
The core value proposition of this skill lies in its ability to bridge the gap between static agent configurations and dynamic, learning systems. By leveraging protocol-constrained evolution, Safe Evolver ensures that any behavioral shifts remain within security boundaries, preventing unauthorized or dangerous logic changes while maximizing efficiency through data-driven insights.
Safe Evolver Use Cases
- Optimizing agent execution speed by identifying and merging redundant tool calls.
- Detecting and correcting persistent error patterns such as file permission issues or API timeouts.
- Monitoring and throttling resource usage (CPU/Memory) during heavy autonomous workloads.
- Generating audit trails for AI behavior to ensure compliance with organizational security standards within Openclaw Skills.
How Safe Evolver Works
- Interaction Capture: The engine records real-time metrics for every tool call, response, and error generated by the AI agent.
- Pattern Analysis: Using built-in heuristics or LLM-based analysis, it evaluates behavior against efficiency and quality benchmarks.
- Scoring: Safe Evolver calculates a multi-dimensional efficiency score (0-1) based on tool usage, response accuracy, and recovery speed.
- Improvement Generation: The system identifies specific optimization opportunities, such as caching frequent reads or refining error handling logic.
- Controlled Application: Depending on the safety configuration, improvements are either automatically applied or presented for human review to ensure operational safety.
Safe Evolver Setup
To integrate this skill into your environment, use the following commands:
# Install via ClawHub for seamless Openclaw Skills integration
clawhub install safe-evolver
# Alternative installation via NPM
npm install safe-evolver
Basic initialization in your project:
const SafeEvolver = require('safe-evolver');
const evolver = new SafeEvolver({
mode: 'auto',
safetyLevel: 'high',
logPath: './logs/evolution.log'
});
Safe Evolver Data Schema & Taxonomy
Safe Evolver organizes its analytical data using the following structure to maintain transparency within Openclaw Skills:
| Component | Data Type | Purpose |
|---|---|---|
| Interaction Logs | JSON Array | Stores raw metrics of tool calls, durations, and success rates. |
| Evolution Report | JSON Object | Contains the summary of average efficiency and identified issues. |
| Efficiency Metrics | Float (0-1) | Quantitative score of tool, response, and error recovery performance. |
| Audit Trail | Log File | A human-readable history of every change made to the agent's behavior. |
Safe Evolver Advanced Features
- LLM-Powered Analysis: Optionally use models like Qwen to perform deep semantic analysis of agent failures.
- Protocol Constraints: Hard-coded safety boundaries that prevent the agent from evolving into prohibited behavior patterns.
- Granular Analysis Toggles: Enable or disable specific dimensions like resource usage or response quality analysis.
- Automated Action Hooks: Trigger custom code paths when specific improvement thresholds are met in Openclaw Skills.
- Manual Review Interface: Support for 'Human-in-the-loop' workflows to approve suggested behavioral updates.
SKILL.md
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