8 Claude Code skills I use for building better AI evals
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.
Key takeaways
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Prioritize Failure Analysis Over Planning
The biggest mistake in AI product development is writing evaluation plans before analyzing actual failures observed in the product. The speaker recommends starting with the Error Discovery and Eval Audit skills.