8 Claude Code skills I use for building better AI evals

Summary

This technical deep dive introduces eight specialized Claude Code skills designed to improve the robustness of AI product evaluations (AI Evals). The core message is that teams must analyze real product failures before designing evaluation metrics. The two most critical skills are 'Error Discovery,' which guides the process of finding failure modes by reviewing data traces, and 'Eval Audit,' which systematically checks the entire evaluation setup against common industry mistakes.

Technical details

• Error Discovery Skill 0s

  This skill guides the agent through multiple phases to find failure modes: 1) Data understanding (parsing structure and identifying dimensions of variation). 2) Annotation Interface Design (using principles like Gestalt). 3) Building the review interface. It intelligently samples data, prioritizing diversity (breadth) over frequency, and utilizes an interactive review loop that updates sampling as new errors are found.

• Eval Audit Skill 7s

  This skill performs diagnostic checks on the evaluation pipeline to prevent common mistakes. Checks include ensuring error analysis was performed, confirming failure categories are observed in data, mandating binary pass/fail metrics (avoiding Likert scales), validating LLM judges against human labels, and checking for proper data splitting to prevent overfitting.

• LLM Judge Validation 8s

  To ensure an LLM judge is reliable, the Eval Audit skill mandates checking it against human labels. It also emphasizes proper data hygiene and splitting to prevent the judge from 'cheating' during evaluation.

• Synthetic Data Generation 10s

  The 'Generate Synthetic Data' skill is useful for pre-product stress testing when real user data is unavailable, helping to maximize the diversity of the synthetic data.

Mentioned resources

• The eval skills on GitHub (GitHub Repository)
• AI evals course flashcards (Learning Material)

Channel & topics

• Hamel Husain
• AI Evals
• LLM Evaluation
• Claude Code Skills
• RAG Pipelines
• Data Annotation
• Failure Mode Analysis
• The eval skills on GitHub
• AI evals course flashcards