ai-evals-course/evals-skills

evals-start

Entry point for evals.

Ver código fuente

Cómo usar

  1. Copia el comando y revisa el paquete antes de instalarlo en tu entorno de agente.
  2. Consulta las herramientas, credenciales y entornos necesarios en el documento original.
  3. Describe las entradas y el resultado deseado; contrasta la salida con las fuentes.
npx skills add https://github.com/ai-evals-course/evals-skills --skill evals-start

Antes de empezar

  • La instalación no incluye suscripción al modelo, cuota API ni permisos sobre tus archivos.
  • Las estrellas e instalaciones reflejan actividad, no una prueba independiente de calidad o seguridad.
Documento original del Skill

Contenido del repositorio de origen con títulos, ejemplos, código, tablas, enlaces e imágenes preservados.

Evals Start

This plugin splits eval work into targeted skills. Your job here is small: find the row below that matches the user's situation, tell the user which skill you are loading and why, then load that skill and follow its workflow from start to finish instead of improvising your own version of it.

SituationSkill to load
Has traces, wants to find failure modes, no established taxonomy yeterror-discovery
Has an existing eval pipeline and wants to know if it can be trustedeval-audit
Has a known failure mode that code can check (e.g., format, schema, regex, execution)write-code-eval
Has a known failure mode and wants an LLM judge for itwrite-judge-prompt
Has an LLM judge or evaluator and wants to check its qualityvalidate-evaluator
Has no traces to review yetgenerate-synthetic-data, then error-discovery
Wants a custom annotation interface for some other labeling taskbuild-review-interface
Wants to evaluate a RAG pipelineevaluate-rag

Most requests that mention error analysis with traces in hand mean error-discovery. New users with an existing pipeline usually need eval-audit first. This file holds only routing. When in doubt about which row fits, ask the user instead of guessing. The workflow lives in the targeted skill.

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ai-evals-course
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eval-audit

Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-discovery, write-judge-prompt, or validate-evaluator instead).

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write-code-eval

Write code evaluators for known failure modes with objective rules. Use when code can check the rule from a trace, with or without a reference answer. Use write-judge-prompt when the rule requires interpretation.

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2
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1,5 mil
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write-judge-prompt

Design LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests); use write-code-eval. To validate an existing judge, use validate-evaluator.

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error-discovery

Run error analysis on a dataset. Build a review UI, select diverse samples, monitor annotations, and organize failure modes.

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1
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1,5 mil
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