Update to Eval Skills Plugin: Coding Agents Build Review Apps for Error Discovery

X postHamel HusainAnnouncement

Hamel Husain and Shreya Shankar have updated their eval skills plugin. The main addition is an error-discovery skill that allows coding agents to generate custom review apps from AI outputs or trace files.

Key points

  1. Hamel Husain and @sh_reya updated their previously released eval skills plugin following iterative improvements.
  2. The primary update is a new error-discovery skill.
  3. By feeding a file of AI outputs or execution traces to a coding agent, the agent builds a custom review application.

Why it matters

Evaluating AI outputs and traces manually is time-consuming. Equipping coding agents with skills to turn raw execution traces into review interfaces streamlines debugging and assessment workflows.

Analysis

The update focuses on bridging the gap between raw AI trace logs and actionable inspection. Leveraging coding agents to spin up purpose-built review interfaces reduces the overhead required to inspect system failures and refine model evaluations.

Original post

Hamel Husain

@HamelHusain · 17 August 2026

A few months ago,@sh_reya and I released eval skills plugin. We iterated on it a bunch and recently made it better The biggest change is a new error-discovery skill. Give your coding agent a file of AI outputs or traces, and it builds a custom review app w/intelligent

717 likes41 replies1.1k bookmarks71k views

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Selected replies (2)
  • @AgomaMitchell @HamelHusain @sh_reya The key step is turning observed production failures into reusable tests. Sampling and clustering improve coverage, but every retained eval should state the defect it detects and the release decision it informs. Related QA view: t.co/jbnQGhziNe
  • @salisedesign @HamelHusain @sh_reya sounds like a really useful tool for debugging. the error-discovery skill seems super helpful

Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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