tooluniverse-literature-deep-research
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 official IDs (Ensembl/UniProt), synonyms, naming collisions, and gathers expression/pathway context before literature search. Default deliverable is a report file; for single factoid questions, uses a fast verification mode and may include an inline answer. Use when users need thorough literature reviews, target profiles, or t
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-literature-deep-research --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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-languag
- Workflow Overview
- Phase 0: Initial Clarification
- Mandatory Questions
- Mode Selection (CRITICAL)
- Factoid / Verification Mode (Fast Path)
- Detect Target Type
- Phase 1: Target Disambiguation + Profile (Default ON)
- 1.1 Resolve Official Identifiers
- 1.2 Identify Naming Collisions
- 1.3 Protein Architecture & Domains
- 1.4 Subcellular Location
- 1.5 Baseline Expression
- 1.6 GO Terms & Pathway Placement
- Phase 2: Literature Search (Internal Methodology)
What does the tooluniverse-literature-deep-research skill do?
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 official IDs (Ensembl/UniProt), synonyms, naming collisions, and gathers expression/pathway context before literature search. Default deliverable is a report file; for single factoid questions, uses a fast verification mode and may include an inline answer. Use when users need thorough literature reviews, target profiles, or t
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-literature-deep-research --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.