数据与研究

tooluniverse-literature-deep-research

mims-harvard/to…universe

Deep literature review — PubMed, EuropePMC, bioRxiv preprints, citation networks, evidence synthesis. Disambiguates queries, runs collision-aware searches, grades evidence T1-T4, and produces structured reports. Use for systematic literature review, meta-analysis evidence collection, and detailed answer-with-citations workflows.

  • 666 次安装
  • 1800 星标
  • 收录于

安装命令

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

SKILL.md

Literature Deep Research

Systematic literature research: disambiguate, search with collision-aware queries, grade evidence, produce structured reports.

KEY PRINCIPLES: (1) Disambiguate first (2) Right-size deliverable (3) Grade every claim T1-T4 (4) All sections mandatory even if "limited evidence" (5) Source attribution for every claim (6) English-first queries, respond in user's language (7) Report = deliverable, not search log


LOOK UP, DON'T GUESS

Search PubMed/EuropePMC FIRST before reasoning. A published paper beats memory.

Factoid search strategy:

  1. Extract KEY TERMS (most specific nouns/verbs)
  2. EuropePMC_search_articles(query="term1 term2 term3", limit=5)
  3. No results -> BROADEN (remove most restrictive term)
  4. Too many -> NARROW (add specific terms)
  5. Answer usually in abstract of top results
  6. Failed query -> try DIFFERENT TERMS/synonyms, don't repeat