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

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)

markdown
# [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. If the opt-in Noodle MCP is connected (noodle_*, needs NOODLE_MCP_URL), its bounded citation/semantic graph traversal is another angle on the same PubMed corpus -- a discovery signal, not evidence of causality or validity, same caveat as the others. 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, Consensus_search_papers | DOI registry + OA aggregators + Internet Archive Scholar; Consensus (220M+ papers) adds an AI takeaway + study-design metadata per paper -- useful for fast triage, not a substitute for reading the source | | 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.

Last resort when every structured index above is empty (a brand-new preprint, a dataset page, a project site with no DOI): the opt-in exa_* tools (general neural web search, no key needed for casual use) can still find it, but it's general internet retrieval, not a scientific database -- verify anything it surfaces against a real source before citing, don't grade it T1-T4 as if it were literature.

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.

Triaging a large candidate set before reading in full: Consensus_search_papers returns study type and sample size per paper, a fast first pass for provisional tiering -- confirm against the actual paper before citing, its metadata is a starting point, not the grade itself.

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 -- 130+ 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