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)→
More from coding & agent
- Reticle builds a verification layer that lets AI coding agents check their own generated code — Scobleizer · 2026-10-27
- Dev training a guard model to block prompt injection, tool and memory poisoning — gnukeith · 2026-10-27
- Dev trains a ~4GB-VRAM sentinel model to guard against prompt injection and poisoned memory — gnukeith · 2026-10-27
- Superset rebrands as Paradev, raises $11.5M seed led by USV — massimosgrelli · 2026-10-27
- Ex-Big Tech managers may thrive in the AI era: manage agents like you managed teams — lxfater · 2026-10-27
- Claude debugs airline and hotel sites; Fasano's date picker is all NaN and invalid dates — lilyraynyc · 2026-10-27