Eval Harness
Eval Harness allows you to systematically test and evaluate AI model outputs against a defined set of criteria for consistent performance & reliability.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add eval-harness npx -- -y @trustedskills/eval-harness
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"eval-harness": {
"command": "npx",
"args": [
"-y",
"@trustedskills/eval-harness"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The eval-harness skill provides a framework for evaluating and testing AI models, allowing users to define custom evaluation metrics, run benchmarks, and analyze model performance systematically. It supports structured testing scenarios and integrates with various model interfaces.
When to use it
- You need to benchmark multiple AI models against the same set of tasks or datasets.
- You want to measure how well a model performs on specific metrics like accuracy, speed, or response quality.
- You are developing new AI agents and require a standardized way to validate their capabilities before deployment.
Key capabilities
- Custom evaluation metric configuration
- Integration with multiple model APIs
- Benchmarking across different datasets
- Performance analysis and reporting
Example prompts
- "Run a benchmark test on the latest language model using the standard evaluation dataset."
- "Evaluate this AI agent's performance on sentiment classification tasks."
- "Compare the accuracy of two models using the eval-harness framework."
Tips & gotchas
- Ensure that all required dependencies are installed before running evaluations.
- The skill may require access to specific datasets or APIs, so verify compatibility with your environment.
Tags
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