tooluniverse-literature-deep-research

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

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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

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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

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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.

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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.

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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

Biomedical: PubMed_search_articles, PMC_search_papers, EuropePMC_search_articles, PubTator3_LiteratureSearch Biology (ecology/evolution/plant): EuropePMC as PRIMARY (PubMed returns 0-1 for non-clinical biology). Also openalex_literature_search. CS/ML: ArXiv_search_papers, DBLP_search_publications, SemanticScholar_search_papers General: openalex_literature_search, Crossref_search_works, CORE_search_papers, DOAJ_search_articles Preprints: BioRxiv_get_preprint, MedRxiv_get_preprint, OSF_search_preprints, EuropePMC_search_articles(source='PPR') Multi-source: advanced_literature_search_agent (12+ DBs; needs Azure key -- fallback: query PubMed+ArXiv+SemanticScholar+OpenAlex individually) Citation impact: iCite_search_publications (RCR/APT), iCite_get_publications (by PMID), scite_get_tallies (support/contradict). PubMed-only; for CS use SemanticScholar.

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.

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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.

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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).

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Communication

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

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References

• TOOL_NAMES_REFERENCE.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