mims-harvard/tooluniverse-literature-deep-research
skills/tooluniverse-literature-deep-research/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add mims-harvard/tooluniverse tooluniverse-literature-deep-researchInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- tooluniverse-literature-deep-research
- description:
- 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.
- disable-model-invocation:
- true
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:
- Extract KEY TERMS (most specific nouns/verbs)
EuropePMC_search_articles(query="term1 term2 term3", limit=5)- No results -> BROADEN (remove most restrictive term)
- Too many -> NARROW (add specific terms)
- Answer usually in abstract of top results
- 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: OpenTargets_get_drug_chembId_by_generic_name, ChEMBL_get_drug, PubChem_get_CID_by_compound_name, drugbank_get_drug_basic_info_by_drug_name_or_id
Targets: ChEMBL_get_drug_mechanisms, OpenTargets_get_associated_targets_by_drug_chemblId, DGIdb_get_drug_gene_interactions
Safety: OpenTargets_get_drug_adverse_events_by_chemblId, OpenTargets_get_drug_indications_by_chemblId, search_clinical_trials
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 CTD_get_chemical_gene_interactions, CTD_get_chemical_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: PubMed_get_cited_by, EuropePMC_get_citations/references, PubMed_get_related, SemanticScholar_get_recommendations, OpenCitations_get_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): PubMed_search_articles, EuropePMC_search_articles, openalex_search_works (query param search/query) or openalex_literature_search (query param search_keywords) — pick one and match its param; mixing them silently returns off-topic results — and SemanticScholar_search_papers
Then add by domain:
| Domain | Add these | Notes |
|--------|-----------|-------|
| Biomedical / clinical | PMC_search_papers (full text), PubTator3_LiteratureSearch (entity & relations: queries), PubMed_Guidelines_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 | ArXiv_search_papers, DBLP_search_publications | arXiv + CS bibliography |
| Physics / HEP / astro | InspireHEP_search_papers | 1.6M+ particle/astro records |
| Broad / hard-to-find / OA | Crossref_search_works, CORE_search_papers, DOAJ_search_articles, Fatcat_search_scholar | DOI registry + OA aggregators + Internet Archive Scholar |
| Regional / EU-funded | OpenAIRE_search_publications, HAL_search_archive | EU open science + French national archive |
| Datasets / software / outputs | Figshare_search_articles, Zenodo_search_records | Citable DOIs for data & code |
| Preprints (latest) | EuropePMC_search_articles(source='PPR'), OSF_search_preprints, BioRxiv_get_preprint/MedRxiv_get_preprint (DOI lookup) | bioRxiv/medRxiv/PsyArXiv etc. |
Multi-source: advanced_literature_search_agent (12+ DBs; needs Azure key -- fallback: query the core set individually).
Citation impact: iCite_search_publications (RCR/APT), iCite_get_publications (by PMID), scite_get_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: PubMed_get_cited_by -> EuropePMC_get_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]_factcheck_report.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
TOOL_NAMES_REFERENCE.md-- 123 tools with parametersREPORT_TEMPLATE.md-- template, domain adaptations, bibliography, completeness checklistFULLTEXT_STRATEGY.md-- three-tier full-text verificationWORKFLOW.md-- compact cheat-sheetEXAMPLES.md-- worked examples
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