mims-harvard/tooluniverse

tooluniverse-literature-deep-research

Deep literature review — PubMed, EuropePMC, bioRxiv preprints, citation networks, evidence synthesis.

First seen Feb 4, 2026

Installation

$ npx skills add mims-harvard/tooluniverse --skill tooluniverse-literature-deep-research

Summary

  • 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.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 1.7K
License LICENSE
Default branch main
Open issues 9
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 9,781 B
  • docs SUMMARY.md 377 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 643 installs

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. EuropePMCsearcharticles(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

COMPUTE, DON'T DESCRIBE

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

Workflow

Phase 0: Clarify + Mode Select → Phase 1: Disambiguate + Profile → Phase 2: Literature Search → Phase 3: Report

Phase 0: Mode Selection

Mode When Deliverable
Factoid Single concrete question 1-page fact-check report + bibliography
Mini-review Narrow topic 1-3 page narrative
Full Deep-Research Comprehensive overview 15-section report + bibliography

Factoid Mode (Fast Path)

# [TOPIC]: Fact-check Report
## Question / ## Answer (with evidence rating) / ## Source(s) / ## Verification Notes / ## Limitations

Domain Detection

Pattern Domain Action
Gene/protein symbol Biological target Full bio disambiguation
Drug name Drug Drug disambiguation (1.5)
Disease name Disease Disease disambiguation (1.6)
CS/ML topic General academic Skip bio tools, literature-only
Cross-domain Interdisciplinary Resolve each entity in its domain

Cross-Skill Delegation

  • Gene/protein deep-dive: tooluniverse-target-research
  • Drug profile: tooluniverse-drug-research
  • Disease profile: tooluniverse-disease-research

Use this skill for literature synthesis. Use specialized skills for entity profiling. For max depth, run both.


Phase 1: Subject Disambiguation + Profile

1.1 Biological Target Resolution

UniProt_search → UniProt_get_entry_by_accession → UniProt_id_mapping
ensembl_lookup_gene → MyGene_get_gene_annotation

1.2 Naming Collision Detection

Check first 20 results. If >20% off-topic, build negative filter: NOT [collision1] NOT [collision2]. Gene family: "ADAR" NOT "ADAR2" NOT "ADARB1". Cross-domain: add context terms.

1.3 Baseline Profile (Bio Targets)

InterPro_get_protein_domains, UniProt_get_ptm_processing_by_accession, HPA_get_subcellular_location,
GTEx_get_median_gene_expression, GO_get_annotations_for_gene, Reactome_map_uniprot_to_pathways,
STRING_get_protein_interactions, intact_get_interactions, OpenTargets_get_target_tractability_by_ensemblID

GPCR targets: delegate to tooluniverse-target-research.

1.5 Drug Disambiguation

Identity: OpenTargetsgetdrugchembIdbygenericname, ChEMBLgetdrug, PubChemgetCIDbycompoundname, drugbankgetdrugbasicinfobydrugnameorid Targets: ChEMBLgetdrugmechanisms, OpenTargetsgetassociatedtargetsbydrugchemblId, DGIdbgetdruggeneinteractions Safety: OpenTargetsgetdrugadverseeventsbychemblId, OpenTargetsgetdrugindicationsbychemblId, searchclinicaltrials

1.6 Disease Disambiguation

OpenTargets disease search → EFO/MONDO IDs
DisGeNET_get_disease_genes, DisGeNET_search_disease
CTD_get_disease_chemicals

1.7 Compound Queries (e.g., "metformin in breast cancer")

Resolve both entities, then cross-reference via CTDgetchemicalgeneinteractions, CTDgetchemical_diseases, OpenTargets drug-target/drug-disease tools. Intersect shared targets/pathways.

1.8 General Academic / 1.9 Interdisciplinary

Non-bio: skip bio tools, use ArXiv/DBLP/OSF. Cross-domain: resolve bio entities with 1.1-1.3, search CS/general in parallel, merge and cross-reference.


Phase 2: Literature Search

Methodology stays internal. Report shows findings, not process.

2.1 Query Strategy

Step 1: Seeds (15-30 core papers): domain-specific title searches with date/sort filters. Step 2: Citation expansion: PubMedgetcitedby, EuropePMCgetcitations/references, PubMedgetrelated, SemanticScholargetrecommendations, OpenCitationsget_citations Step 3: Collision-filtered broader queries: "[TERM]" AND ([context]) NOT [collision]

2.2 Literature Tools — core set + adaptive by domain

Run the core multi-field set on every review (catches what any single index misses), then add the domain rows that match the subject. Don't fire every source blindly — 6–10 well-chosen indexes beat 20 noisy ones.

ALWAYS run (core, all disciplines): PubMedsearcharticles, EuropePMCsearcharticles, openalexsearchworks (query param search/query) or openalexliteraturesearch (query param searchkeywords) — pick one and match its param; mixing them silently returns off-topic results — and SemanticScholarsearch_papers

Then add by domain:

Domain Add these Notes
Biomedical / clinical PMCsearchpapers (full text), PubTator3LiteratureSearch (entity & relations: queries), PubMedGuidelines_Search (clinical guidelines) PubTator normalizes gene/drug/disease entities
Biology (ecology/evolution/plant) EuropePMC as PRIMARY + OpenAlex PubMed returns 0–1 for non-clinical biology
CS / ML / AI ArXivsearchpapers, DBLPsearchpublications arXiv + CS bibliography
Physics / HEP / astro InspireHEPsearchpapers 1.6M+ particle/astro records
Broad / hard-to-find / OA Crossrefsearchworks, COREsearchpapers, DOAJsearcharticles, Fatcatsearchscholar DOI registry + OA aggregators + Internet Archive Scholar
Regional / EU-funded OpenAIREsearchpublications, HALsearcharchive EU open science + French national archive
Datasets / software / outputs Figsharesearcharticles, Zenodosearchrecords Citable DOIs for data & code
Preprints (latest) EuropePMCsearcharticles(source='PPR'), OSFsearchpreprints, BioRxivgetpreprint/MedRxivgetpreprint (DOI lookup) bioRxiv/medRxiv/PsyArXiv etc.

Multi-source: advancedliteraturesearchagent (12+ DBs; needs Azure key -- fallback: query the core set individually). Citation impact: iCitesearchpublications (RCR/APT), iCitegetpublications (by PMID), sciteget_tallies (support/contradict). PubMed-only; for CS use SemanticScholar.

A domain-specific index returning 0 (e.g. ArXiv on a pure-clinical topic) is normal — only worry if the whole core set is empty.

2.3-2.4 Full-Text & PubMed Zero-Result Fallback

Full-text: see FULLTEXT_STRATEGY.md for three-tier strategy.

CRITICAL: PubMed returns 0 for ~30% of valid queries. Always retry with EuropePMC when PubMed returns empty. This is not optional.

2.5 Tool Failure / OA Handling

Retry once -> fallback tool. Key fallbacks: PubMedgetcitedby -> EuropePMCget_citations -> OpenCitations. OA: Unpaywall if configured, else Europe PMC/PMC/OpenAlex flags.


Phase 3: Evidence Grading

Tier Label Bio Example CS/ML Example
T1 Mechanistic CRISPR KO + rescue, RCT Formal proof, controlled ablation
T2 Functional siRNA knockdown phenotype Benchmark with baselines
T3 Association GWAS, screen hit Observational, case study
T4 Mention Review article Survey, workshop abstract

Inline: Target X regulates Y [T1: PMID:12345678]. Per theme: summarize evidence distribution.


Report Output

File Mode
[topic]_report.md Full
[topic]factcheckreport.md Factoid
[topic]_bibliography.json + .csv All

Progressive update: create report with all section headers immediately. Fill after each phase. Write Executive Summary LAST.

Use 15-section template from REPORT_TEMPLATE.md. Domain adaptations: bio (architecture/expression/GO/disease), drug (properties/MOA/PK/safety), disease (epi/patho/genes/treatments), general (history/theories/evidence/applications).


Communication

Brief progress updates only: "Resolving identifiers...", "Building paper set...", "Grading evidence..." Do NOT expose: raw tool outputs, dedup counts, search round details.


References

  • TOOLNAMESREFERENCE.md -- 123 tools with parameters
  • REPORT_TEMPLATE.md -- template, domain adaptations, bibliography, completeness checklist
  • FULLTEXT_STRATEGY.md -- three-tier full-text verification
  • WORKFLOW.md -- compact cheat-sheet
  • EXAMPLES.md -- worked examples