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tooluniverse-literature-deep-research

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Conduct comprehensive literature research with target disambiguation, evidence grading, and structured theme extraction. Creates a detailed report with mandatory completeness checklist, biological model synthesis, and testable hypotheses. For biological targets, resolves offic...

scientific-skills/Evidence Insight/tooluniverse-literature-deep-research/SKILL.md

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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)


# Literature Deep Research Strategy (Enhanced)

A systematic approach to comprehensive literature research that **starts with target disambiguation** to prevent missing details, uses **evidence grading** to separate signal from noise, and produces a **content-focused report** with mandatory completeness sections.

**KEY PRINCIPLES**:
1. **Target disambiguation FIRST** - Resolve IDs, synonyms, naming collisions before literature search
2. **Right-size the deliverable** - Use *Factoid / Verification Mode* for single, answerable questions; use full report mode for “deep research”
3. **Report-first output** - Default deliverable is a report file; an inline answer is allowed (and recommended) for Factoid / Verification Mode
4. **Evidence grading** - Grade every claim by evidence strength (mechanistic paper vs screen hit vs review vs text-mined)
5. **Mandatory completeness** - All checklist sections must exist, even if "unknown/limited evidence"
6. **Source attribution** - Every piece of information traceable to database/tool
7. **English-first queries** - Always use English terms for literature searches and tool calls, even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language
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