A lightweight performance optimization framework for AI agents based on trajectory analysis and reward feedback.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-optimizer
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 agent-optimizer 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 Agent Optimizer?
Agent Optimizer is a specialized V6.1 framework designed for the continuous performance enhancement of AI agents without the need for external dependencies. It leverages the native capabilities of Openclaw Skills to monitor execution paths, collect reward signals, and iteratively refine prompt strategies. By focusing on structured data collection and feedback loops, it empowers developers to build more efficient, high-ROI agents that learn from every interaction.
This framework provides a systematic approach to agent development, moving beyond trial-and-error by providing concrete metrics and analysis tools. Whether you are building technical assistants or automated scrapers, Agent Optimizer ensures your Openclaw Skills evolve based on real-world performance data and user feedback.
Agent Optimizer Use Cases
- Enhancing technical tutorial generation quality through iterative user rating feedback.
- Optimizing financial ROI prediction accuracy by comparing predicted outcomes against actual results.
- Improving web scraping success rates through automated execution pattern analysis and retry logic.
- Managing and A/B testing multiple prompt versions to find the most effective communication style.
- Calculating and tracking the return on investment for complex multi-agent workflows.
How Agent Optimizer Works
- Initialize the optimizer by establishing a dedicated workspace and configuration file within the sub-agent directory.
- Record execution trajectories automatically, capturing tasks, outputs, tool calls, and time-to-completion metrics.
- Emit reward signals based on specific triggers such as user ratings, task completion status, or ROI calculations.
- Analyze the collected trajectory and reward data to identify patterns associated with high-performance outcomes.
- Generate optimization reports that suggest prompt refinements or logic changes based on historical success.
- Deploy A/B tests to validate improvements and manage prompt versions for safe rollbacks.
Agent Optimizer Setup
Agent Optimizer is natively integrated into the Openclaw Skills environment and requires no additional pip installations. To begin, set up your directory structure:
mkdir -p /workspace/subagents/{agent_id}/optimizer
cat > /workspace/subagents/{agent_id}/optimizer/config.json << 'EOF'
{
"agent_id": "techbot",
"optimization_target": "tutorial_quality",
"metrics": ["user_rating", "completion_rate", "roi"],
"ab_test": true
}
EOF
Agent Optimizer Data Schema & Taxonomy
The skill organizes its data within the agent's workspace to ensure portability and privacy. The schema includes:
| Component | Format | Description |
|---|---|---|
config.json |
JSON | Defines optimization targets, active metrics, and A/B test settings. |
trajectories.jsonl |
JSONL | A structured log of every agent execution, including inputs and prompt versions. |
rewards.jsonl |
JSONL | A record of feedback signals linked to specific execution trajectories. |
optimization_report.json |
JSON | Periodic analysis results, including average rewards and pattern identification. |
prompts/ |
Directory | Stores version-controlled prompt templates (e.g., v1.0.txt, v2.0.txt). |
Agent Optimizer Advanced Features
- Native A/B testing engine to compare the efficacy of different prompt versions in real-time.
- Automated prompt versioning system allowing for seamless transitions and rapid rollbacks.
- Reward trend analysis scripts that visualize performance improvements over time.
- Support for custom ROI-driven optimization strategies for financial and automation tasks.
- Lightweight footprint with zero external dependencies, perfect for self-hosted Openclaw Skills environments.
SKILL.md
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