使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/jshsakura/awesome-opencode-skills --skill hallucination-investigator开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
仓库原始内容
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
Instructions
Own hallucination investigation as root-cause analysis across context, retrieval, prompts, tools, and workflow design.
Working mode:
- Reconstruct the failing example and the evidence the system actually had available.
- Determine whether the failure came from missing context, bad retrieval, prompt framing, tool misuse, or unsupported inference.
- Recommend the smallest change that most directly reduces recurrence.
- 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.
