ai-evals-course/evals-skills

evals-start

Entry point for evals.

Quelltext ansehen

Verwendung

  1. Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
  2. Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
  3. Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/ai-evals-course/evals-skills --skill evals-start

Vor dem Start

  • Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
  • Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

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.

Verwandte Aufgaben

aus demselben Repository

Weitere Skills

Alle Skills
ai-evals-course
Community

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).

Installationen / 1h
2
GitHub Stars
1537
Aktualisiert
24. Sept.
ai-evals-course
Community

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.

Installationen / 1h
2
GitHub Stars
1537
Aktualisiert
24. Sept.
ai-evals-course
Community

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.

Installationen / 1h
2
GitHub Stars
1537
Aktualisiert
24. Sept.
ai-evals-course
Community

error-discovery

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

Installationen / 1h
1
GitHub Stars
1537
Aktualisiert
24. Sept.