Why agent skills fail in production and how to eval them
_philschmid · x · 2026-07-20
At the aiDotEngineer World’s Fair, philschmid gave a talk on why “vibe-checking” agent skills breaks in production and how to build more reliable evals.
Main points:
- Vague skill descriptions lead to failures; adding negative test cases helps prevent keyword hijacking from unrelated prompts.
- Very large skill files, especially 500+ lines, can hurt model reasoning.
- Simple regex assertions can validate outcomes quickly across 10–20 production prompts.
- Ablation tests, with and without skills, help determine when models have caught up enough that a skill can be retired.
Related event: Why Agent Skills Fail and How to Evaluate Them(2 posts)→
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