A rigorous testing, benchmarking, and reliability framework designed to evaluate and monitor autonomous LLM agents under real-world conditions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install martin-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 martin-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?
Evaluating LLM agents requires a fundamentally different approach compared to traditional deterministic software. Because LLM outputs are probabilistic and non-deterministic, passing a test once is not a guarantee of production reliability. The Agent Evaluation skill provides quality engineers and AI developers with robust methodologies to run behavioral regression tests, capability assessments, and adversarial checks, bridging the gap between high benchmark scores and actual real-world performance.
By implementing this technical framework within your Openclaw Skills workspace, you gain access to unified LLM evaluation endpoints, statistical test runs, and multi-dimensional metrics. This ensures your autonomous agents are thoroughly vetted against metric gaming, flaky outputs, and critical data leakage before deploying to production environments.
Agent Evaluation Use Cases
- Testing non-deterministic LLM agent responses through statistical distribution analysis rather than strict string matching.
- Designing behavioral contract tests to enforce invariant rules during multi-turn agent interactions.
- Running adversarial testing suites to systematically expose security vulnerabilities and failure states.
- Establishing automated regression tests to monitor agent capabilities when underlying LLM models are updated.
- Quantifying agent reliability and preventing metric gaming in production environments.
How Agent Evaluation Works
- Define Behavioral Invariants: Establish the semantic and logical contracts that the AI agent must always satisfy.
- Execute Statistical Runs: Trigger test runs multiple times to analyze output distributions and measure reliability percentages rather than relying on a single pass/fail execution.
- Interface with SkillBoss API Hub: Route evaluation LLM prompts through a single, balanced API endpoint using secure authentication credentials.
- Run Adversarial Challenges: Present the agent with challenging, tricky, or malicious inputs to test boundaries and prevent edge-case failures.
- Analyze Evaluation Metrics: Aggregate multi-dimensional metrics to ensure balanced improvement across accuracy, latency, and compliance, neutralizing data leakage and over-optimization.
Agent Evaluation Setup
To configure the Agent Evaluation skill, you must first export the required SkillBoss API credentials to your environment.
export SKILLBOSS_API_KEY="your_skillboss_api_key_here"
Python Implementation
Integrate the unified evaluation gateway directly into your Python-based testing pipelines:
import os
import requests
SKILLBOSS_API_KEY = os.environ.get("SKILLBOSS_API_KEY")
def call_llm(messages: list) -> str:
r = requests.post(
"https://api.skillboss.co/v1/pilot",
headers={
"Authorization": f"Bearer {SKILLBOSS_API_KEY}",
"Content-Type": "application/json"
},
json={
"type": "chat",
"inputs": {"messages": messages},
"prefer": "balanced"
},
timeout=60,
)
return r.json()["result"]["choices"][0]["message"]["content"]
Agent Evaluation Data Schema & Taxonomy
The Agent Evaluation skill structures its datasets and performance evaluations according to a robust taxonomy. Below is the structured framework used for scoring and execution metadata:
Metric Taxonomy
| Evaluation Area | Metric Type | Objective |
|---|---|---|
| Behavioral Invariants | Pass/Fail Contract | Enforces strict boundaries on safety, format, and core instructions |
| Reliability | Statistical Distribution | Runs tests over N iterations to measure output consistency |
| Capability | Scenario Benchmarking | Tests domain-specific task completion rates against golden datasets |
| Robustness | Adversarial Resistance | Scores agent resilience to prompt injections and distraction inputs |
Configuration Schema
- Target LLM Agent API: Managed through SkillBoss API Hub endpoint orchestration.
- Data Leakage Safeguards: Strict separation protocols to prevent test sets from leaking into training pipelines or system prompts.
Agent Evaluation Advanced Features
- Statistical Distribution Testing: Automatically executes identical test suites across multiple runs to map variance and identify flaky behaviors in agent logic.
- Adversarial Input Generation: Employs behavioral testing methodologies to generate boundary-breaking test cases designed to test the agent's defense limits.
- Unified SkillBoss Orchestration: Powers multi-turn evaluator loops via a single endpoint with fallback, balancing capability, and rate-limiting built-in.
- Multi-Agent Evaluation Loops: Perfectly compatible with related Openclaw Skills such as
multi-agent-orchestrationandagent-communicationfor testing team dynamics and handoffs.
SKILL.md
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