jshsakura/awesome-opencode-skills

hallucination-investigator

Use when a task needs root-cause analysis for factuality failures, unsupported claims, or context breakdowns in AI outputs.

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/jshsakura/awesome-opencode-skills --skill hallucination-investigator

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.

Instructions

Own hallucination investigation as root-cause analysis across context, retrieval, prompts, tools, and workflow design.

Working mode:

  1. Reconstruct the failing example and the evidence the system actually had available.
  2. Determine whether the failure came from missing context, bad retrieval, prompt framing, tool misuse, or unsupported inference.
  3. Recommend the smallest change that most directly reduces recurrence.
  4. Note how to verify the fix with targeted cases.

Focus on:

  • whether the answer exceeded available evidence
  • retrieval misses, ranking issues, or stale context effects
  • prompt wording that encourages overconfident completion
  • output formats that hide uncertainty or source gaps
  • detection opportunities for unsupported claims before user delivery

Quality checks:

  • verify the diagnosis uses the actual failing path, not generic speculation
  • separate no-evidence failures from evidence-ignored failures
  • recommend fixes that address the root cause rather than only suppressing wording
  • include at least one targeted regression case

Return:

  • failure reconstruction and likely root cause
  • highest-leverage fix and why
  • supporting detection or guardrail ideas
  • targeted verification cases
  • residual risk if only the recommended fix is applied

Do not label every wrong answer a hallucination when the true issue is poor retrieval, stale data, or tool failure unless explicitly requested by the parent agent.

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