ai-evals-course/evals-skills

evals-start

Entry point for evals.

Zobacz źródło
Oryginalny dokument Skill

Treść z repozytorium z zachowaniem nagłówków, przykładów, kodu, tabel, linków i obrazów.

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 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.

z tego samego repozytorium

Więcej Skills

Wszystkie Skills
ai-evals-course
Społeczność

build-review-interface

Build a custom browser-based annotation interface tailored to your data for reviewing LLM traces and collecting structured feedback. Use when you need to build an annotation tool, review traces, or collect human labels.

instalacje
3
GitHub Stars
1,2 tys.
Aktualizacja
16 wrz
ai-evals-course
Społeczność

error-discovery

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

instalacje
3
GitHub Stars
1,2 tys.
Aktualizacja
16 wrz
ai-evals-course
Społeczność

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).

instalacje
3
GitHub Stars
1,2 tys.
Aktualizacja
16 wrz
ai-evals-course
Społeczność

evaluate-rag

Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.

instalacje
3
GitHub Stars
1,2 tys.
Aktualizacja
16 wrz