A specialized framework for testing and benchmarking LLM agents through behavioral assessment, reliability metrics, and production monitoring.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-evaluation
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-evaluation 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 Evaluation?
The Agent Evaluation skill is designed for developers and quality engineers who need to move beyond traditional software testing. Since LLM agents are non-deterministic, this skill focuses on behavioral regression tests, capability assessments, and reliability metrics to ensure agents perform predictably in real-world scenarios. By integrating these Openclaw Skills into your workflow, you can bridge the gap between high benchmark scores and actual production reliability.
This skill helps you navigate the complexities of agentic behavior where "correct" answers aren't always binary. It provides the tools necessary to analyze result distributions and define behavioral invariants, ensuring that your agents remain robust even when faced with adversarial inputs or edge cases.
Agent Evaluation Use Cases
- Evaluating LLM agent reliability before deploying to production environments.
- Designing custom benchmarks to measure specific agentic capabilities.
- Performing behavioral regression testing after updating prompts or underlying models.
- Running adversarial tests to identify and fix reasoning failures.
How Agent Evaluation Works
- Define behavioral contracts and invariants that the agent must adhere to during execution.
- Execute statistical test evaluation by running scenarios multiple times to capture the distribution of results.
- Implement capability assessments to benchmark the agent against domain-specific tasks.
- Analyze reliability metrics to identify flakiness or performance degradation.
- Monitor for data leakage to ensure test integrity is maintained throughout the evaluation lifecycle.
Agent Evaluation Setup
To begin using this skill within your Openclaw Skills ecosystem, initialize your testing environment with the following commands:
# Install the evaluation module
openclaw install agent-evaluation
# Configure your benchmarking suite
agent-eval init --suite standard-benchmarks
Agent Evaluation Data Schema & Taxonomy
The Agent Evaluation skill organizes its data to provide clear insights into agent performance:
| Component | Description |
|---|---|
| Behavioral Invariants | Specific rules or logic gates the agent must satisfy to pass a test. |
| Result Distributions | Aggregated data from multiple runs used to calculate statistical reliability. |
| Reliability Metrics | Key performance indicators such as success rate, reasoning depth, and token efficiency. |
| Evaluation Logs | Detailed traces of agent decision-making processes during benchmarks. |
Agent Evaluation Advanced Features
- Statistical distribution analysis for handling non-deterministic AI outputs.
- Automated adversarial input generation to stress-test agent boundaries.
- Multi-dimensional scoring systems to prevent agents from gaming specific metrics.
- Integrated leakage detection to prevent training data from contaminating test sets.
SKILL.md
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