tooluniverse-literature-deep-research

Resolve biological target identities and grade evidence in literature reviews.

1.6k|244|Updated Mar 3, 2025

Search Tags:#bibliography #themes #hypotheses #literature-review #biological-targets #target-disambiguation #evidence-grading

Ready to run

npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-literature-deep-research

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tooluniverse-literature-deep-research
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/skills/tooluniverse-literature-deep-research
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-literature-deep-research

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a structured approach to conducting thorough literature reviews on biological targets, including precise identity resolution, evidence-based grading, and thematic synthesis to produce actionable insights.

Core Features & Use Cases

Target Disambiguation: Resolve gene/protein identities, detect collisions, and build a clean target profile before literature search.
Evidence Grading & Theming: Apply a standardized T1–T4 evidence scale to claims and cluster findings into reproducible themes.
Comprehensive Reporting: Generate a full narrative report plus a deduplicated bibliography, suitable for grant writing and strategic planning.
Model Synthesis: Create an integrated biological model with testable hypotheses based on the literature.

Quick Start

Resolve the target identity, aliases, and collisions, then run the full deep-research workflow.
Trigger high-precision seed searches, followed by citation expansion and collision-filtered broad queries.
Review the generated [topic]_report.md and [topic]_bibliography.json for synthesis and evidence grading.

Frequently Asked Questions about tooluniverse-literature-deep-research

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I conduct a literature review with biological target disambiguation?▼

A literature review with biological target disambiguation resolves gene/protein identities and detects collisions before searching, ensuring your research targets the correct entity. This structured approach prevents irrelevant results and establishes a clean target profile for comprehensive synthesis.

What is evidence grading in a literature review?▼

Evidence grading in a literature review applies a standardized T1–T4 scale to claims, allowing you to assess the strength of findings. This process clusters results into reproducible themes, ensuring your knowledge synthesis is reproducible and actionable.

How do I generate a deduplicated bibliography for grant writing?▼

To generate a deduplicated bibliography for grant writing, perform high-precision seed searches followed by citation expansion and collision-filtered broad queries. This produces a clean bibliography file alongside a full narrative report suitable for strategic planning.

Can I synthesize testable biological hypotheses from existing literature?▼

Yes, you can synthesize testable biological hypotheses from literature by creating an integrated biological model. This model extracts structured themes and graded evidence to formulate actionable, testable hypotheses for strategic planning and grant-ready reviews.

Does this literature review approach handle gene and protein name collisions?▼

Yes, this literature review approach explicitly handles gene and protein name collisions through phased target identity resolution. It detects and filters naming collisions before executing broad queries, ensuring your search results remain highly relevant.