A professional toolset for designing evaluation metrics, building test datasets, and analyzing AI agent performance.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skylv-evaluation-benchmark
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 skylv-evaluation-benchmark 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 Evaluation & Benchmark Agent?
The Evaluation & Benchmark skill is a specialized utility within the Openclaw Skills framework designed to provide a rigorous framework for assessing AI agent capabilities. It bridges the gap between development and production by allowing developers to define precise performance indicators and construct high-fidelity test sets. By utilizing this skill, teams can move beyond anecdotal evidence to data-driven validation, ensuring that every iteration of their agent is measurable and meets specific quality thresholds.
Built for developers who demand precision, this skill facilitates a systematic approach to benchmarking. Whether you are testing reasoning capabilities, code generation accuracy, or task completion rates, this component of the Openclaw Skills library provides the necessary infrastructure to automate evaluation workflows and generate comprehensive performance reports.
Evaluation & Benchmark Agent Use Cases
- Designing custom evaluation metrics for domain-specific AI agents.
- Constructing representative test sets for validating code generation performance.
- Executing automated benchmarking suites to ensure regression-free updates.
- Analyzing raw evaluation results to identify systemic reasoning errors or bottlenecks.
How Evaluation & Benchmark Agent Works
- Metric Definition: Identify the key performance indicators (KPIs) relevant to your agent workflow.
- Dataset Construction: Build or import a structured test set containing prompts and expected ground-truth answers.
- Benchmark Execution: Trigger the evaluation process to run the agent against the defined test sets.
- Result Analysis: Generate and review detailed reports to quantify success rates and error patterns within the Openclaw Skills ecosystem.
Evaluation & Benchmark Agent Setup
To begin using the Evaluation & Benchmark skill in your environment, follow these installation steps:
# Navigate to your project directory
cd my-agent-project
# Install the evaluation benchmark skill
openclaw install skylv-evaluation-benchmark
# Initialize the benchmarking configuration
openclaw init evaluation-benchmark
Evaluation & Benchmark Agent Data Schema & Taxonomy
The skill manages performance data through a structured taxonomy to ensure reproducibility across different Openclaw Skills implementations:
| Component | Purpose | Storage Format |
|---|---|---|
| Metrics | Defines the scoring logic and KPIs | YAML |
| Test Sets | Stores inputs, context, and expected outputs | JSONL |
| Run Logs | Captures raw traces of the evaluation execution | JSON |
| Reports | Summarizes performance in a human-readable format | Markdown |
Evaluation & Benchmark Agent Advanced Features
- Automated test set generation based on historical interaction logs.
- Multi-dimensional scoring rubrics for complex, non-deterministic agent outputs.
- Integration hooks to trigger benchmarks automatically during CI/CD pipelines for Openclaw Skills.
- Comparison tools to visualize performance deltas between different model versions or prompts.
SKILL.md
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