ai-evals-course/evals-skills

write-code-eval

Write code evaluators for known failure modes with objective rules.

Ver código-fonte

Como usar

  1. Copie o comando e verifique o pacote antes de instalar no seu ambiente de agente.
  2. Consulte no original as ferramentas, credenciais e ambientes necessários.
  3. Descreva os dados de entrada e a entrega esperada; confira a saída com as fontes.
npx skills add https://github.com/ai-evals-course/evals-skills --skill write-code-eval

Antes de começar

  • A instalação não inclui assinatura de modelo, franquia API ou acesso aos seus arquivos.
  • Estrelas e instalações indicam atividade, não um teste independente de qualidade ou segurança.
Documento original do Skill

Renderizado do repositório de origem, preservando títulos, exemplos, código, tabelas, links e imagens.

Write a code evaluator

Start with a failure mode found through error analysis. Write one check for that failure mode, much like a unit test that asserts what should hold for each trace.

  1. State the rule and identify the trace fields or reference data the check needs. If the rule requires interpretation, use write-judge-prompt.
  2. Implement the check in the project's language and eval framework. Return a result and a reason in the format that framework expects.
  3. Test known passes and failures, including borderline cases. Run the check on available traces and inspect mistakes. If the rule uses a proxy for human judgment, compare its results with human labels.

Examples

Failure modePossible check
Invalid output structureParse the output and check required fields
Missing or forbidden textMatch a string or pattern
Citation not in retrieved documentsCompare cited IDs with retrieved IDs
Bad tool callCheck arguments against the tool schema or run the call in a safe test environment
Wrong valueCompare the output with a reference value

Choose the check from the failure rule. For a failure with both objective and interpretive parts, check the objective part with code and use a judge for the rest.

Relacionados por tarefa

do mesmo repositório

Mais Skills

Todos os Skills
ai-evals-course
Comunidade

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).

instalações
2
GitHub Stars
1,5 mil
Atualizado
24 de set.
ai-evals-course
Comunidade

evals-start

Entry point for evals. Use when the user asks for help with evals, does not know where to begin, or asks for something no other skill in this plugin matches. Do NOT use when a more specific skill in this plugin already matches; load that skill directly.

instalações
2
GitHub Stars
1,5 mil
Atualizado
24 de set.
ai-evals-course
Comunidade

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.

instalações
2
GitHub Stars
1,5 mil
Atualizado
24 de set.
ai-evals-course
Comunidade

error-discovery

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

instalações
1
GitHub Stars
1,5 mil
Atualizado
24 de set.