abstention-under-missing-or-false-premise
Use this skill when the right answer is not to guess, but to say the conversation never established the needed fact. Trigger it for false-premise questions, near-miss memories, or adversarial follow-ups that sound plausible but are unsupported by history. Everyday examples include: 'how many fish are in my 30-gallon tank?' when only other tank sizes were mentioned, 'what did my third sister say?' when no third sister exists in history, and 'which model did I buy?' when the chat only discussed options.
ソース情報
- リポジトリ
- Dingxingdi/paper_fast_search_backup
- ソースの最終更新活動
- 2026年4月8日 15:14
- 検出された SKILL.md の言語
- 英語
- スター
- 0
- フォーク
- 0
インストール方法
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
ソースファイルを確認
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
ファイルエクスプローラー
4 ファイルSKILL.md を表示中
SKILL.md
ソースの指示 · 読み取り専用プレビュー- name
- abstention-under-missing-or-false-premise
- description
- Use this skill when the right answer is not to guess, but to say the conversation never established the needed fact. Trigger it for false-premise questions, near-miss memories, or adversarial follow-ups that sound plausible but are unsupported by history. Everyday examples include: 'how many fish are in my 30-gallon tank?' when only other tank sizes were mentioned, 'what did my third sister say?' when no third sister exists in history, and 'which model did I buy?' when the chat only discussed options.
# Skill: abstention-under-missing-or-false-premise
## 1. Capability Definition & Real Case
* **Professional Definition**: The ability to recognize that the dialogue history does not support the user’s question, especially when the question contains an incorrect assumption, and to answer with calibrated non-knowledge rather than hallucinated completion.
* **Dimension Hierarchy**: Conversational Memory->Persistent Personal Memory->abstention-under-missing-or-false-premise
### Real Case
**[Case 1]**
* **Initial Environment**: The long-term chat history contains mentions of a 10-gallon tank and a 20-gallon tank, but there is no mention of a 30-gallon tank. The user asks a seemingly concrete follow-up that presupposes such a tank exists.
* **Real Question**: How many fish are there in my 30-gallon tank?
* **Real Trajectory**: 1. Search the history for all tank-related mentions. 2. Notice that only 10-gallon and 20-gallon tanks appear. 3. Check whether a 30-gallon tank can be inferred from any update or comparison. 4. Refuse to fabricate and answer that the history does not mention a 30-gallon tank.
* **Real Answer**: You did not mention that you have a 30-gallon tank.
* **Why this demonstrates the capability**: The challenge is resisting the strong urge to complete a plausible but unsupported story. A strong assistant must detect the false premise, ground itself in the actual interaction history, and abstain clearly without inventing fish counts.
## Pipeline Execution Instructions
To synthesize data for this capability, you must strictly follow a 3-phase pipeline. **Do not hallucinate steps.** Read the corresponding reference file for each phase sequentially:
1. **Phase 1: Environment Exploration**
Read the exploration guidelines to discover raw knowledge seeds:
`references/EXPLORATION.md`
2. **Phase 2: Trajectory Selection**
Once Phase 1 is complete, read the selection criteria to evaluate the trajectory:
`references/SELECTION.md`
3. **Phase 3: Data Synthesis**
Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data:
`references/SYNTHESIS.md`
GitHubで見る