Llm Evaluation
wshobson/agentsYou can measure how well an AI model performs using automated metrics, human feedback, and benchmarking. These methods help you catch problems before releasing the model to users. You can compare different AI models or prompts to find the best one.
By tracking scores over time you can see improvements from changes you make. This skill teaches you to set up a complete evaluation framework. It covers simple scores like accuracy and deeper checks like coherence and safety.
You will learn to use stronger AI models to judge weaker ones. This gives you a scalable way to test quality without needing human reviewers for every test. The result is more reliable and trustworthy AI applications.
Add Llm Evaluation skill to your workflow
Global
mkdir -p ~/.claude/skills/llm-evaluationProject
mkdir -p .claude/skills/llm-evaluationSource Repository
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