使い方
- インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
- 必要なツール、認証情報、対応環境を原文で確認します。
- 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/jshsakura/awesome-opencode-skills --skill hallucination-investigator開始前の確認
- 導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
- スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
リポジトリの原文
見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。
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.
