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Detectar regressões silenciosas de agentes por diffs de saídas…

Fotografe o comportamento do agente, compare saídas e caminhos de chamadas de ferramentas e bloqueie lançamentos quando uma mudança de modelo ou prompt alterar sutilmente o comportamento.

O que é Detectar regressões silenciosas de agentes por diffs de saídas…?

Esta skill capacita desenvolvedores a detectar regressões silenciosas em agentes de IA monitorando alterações nas saídas e nos rastros de chamadas de ferramentas. Ao criar snapshots do comportamento do agente e compará-los em pipelines de CI, é possível identificar quando uma atualização de modelo ou prompt introduz variações inesperadas. A ferramenta eval-view fornece um loop fechado que vai da detecção à investigação, emissão de veredito com confiança estatística, quarentena da alteração problemática e broadcast dos resultados para a equipe. Suportada em qualquer stack de agente que possa ser executada de forma repetível, a skill é ideal para ambientes de desenvolvimento e CI que exigem rastreabilidade e controle de qualidade contínuos.

Funciona com~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/catch-silent-agent-regressions-by-diffing-outputs-and-tool-traces-in-ci-with-eval-view

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Documentação

Detect regressões silenciosas de agentes por diffs de saídas e rastreios de ferramentas em CI com eval-view

Fotografe o comportamento do agente, compare saídas e caminhos de chamadas de ferramentas e bloqueie lançamentos quando uma mudança de modelo ou prompt alterar sutilmente o comportamento.

Pré-requisitos

Ambiente Python, instalação do eval-view, cenários ou testes de agente repetíveis, runner de CI ou shell local, stack de agente suportada em teste

Instalação

Notas de uso básico ou primeiros passos:

  • O loop se fecha: detecção → investigação → veredito graduado → quarentena → broadcast. Você acorda, acompanha o progresso, faz triagem com drift, confirma com check --statistical, e a equipe vê o resumo antes...

  • | 📉 DRIFTING | Tendência deslizando com confiança graduada (baixa/média/alta) | Execute evalview drift |

  • | 🔎 INVESTIGATE | Camada de veredito quer replay estatístico | Execute evalview check --statistical 5 |

  • Fonte: https://github.com/hidai25/eval-view

  • Extraído dos docs upstream: https://raw.githubusercontent.com/hidai25/eval-view/HEAD/README.md

Documentação

Fonte

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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Add runtime guardrails to TypeScript agents with VoltAgent

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Add temporal context memory to agent workflows with Zep

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Adobe Firefly API Batch Image Generator

Calls Adobe Firefly

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