Skill Detail
Score model outputs with reusable evaluator prompts and metrics using autoevals
Apply reusable evaluators to model outputs when you need lightweight scoring, rationale capture, or quick eval loops in code.
Code Quality & ReviewMulti-Framework
Code Quality & Review
Multi-Framework
Security Reviewed
⭐ 861 GitHub stars
⬇ 1.8M/wk npm
COPY SKILL INSTRUCTIONS (OPTIONAL)
npx skills add agentskillexchange/skills --skill score-model-outputs-with-reusable-evaluator-prompts-and-metrics-using-autoevals
Uses the third-party skills CLI, not an ASE-owned installer. Check your agent’s compatibility. This copies instructions; complete the upstream tool setup below separately.
At a glance
Tools required
Python or Node.js, access to an OpenAI-compatible model endpoint or Braintrust proxy
Install & setup
Install with
npm install autoevals or pip install autoevals, configure an OpenAI-compatible endpoint, then call the built-in or custom evaluators from code to score outputs and inspect rationales.Author
Braintrust
Publisher
Organization
Last updated
Apr 16, 2026
Quick brief
Use autoevals when the task is to score model outputs programmatically with reusable evaluators instead of standing up a heavier eval platform. It works well for single-example checks, small eval loops, and custom model-graded prompts inside application code or scripts. The boundary is output scoring and evaluator reuse, not full observability, tracing, or end-to-end agent management.