
Literature Deep Research
FreeStreamline systematic literature reviews with structured reports.
Free · Opens the source repo
What Literature Deep Research does
The Literature Deep Research skill provides a comprehensive framework for conducting systematic literature reviews across multiple databases, including PubMed and EuropePMC. It emphasizes a structured approach to disambiguate queries, ensuring that users can effectively navigate complex topics in biomedical research. By grading evidence from T1 to T4, this skill helps users assess the quality of the information they gather, which is crucial for meta-analyses and systematic reviews.
This skill is designed for researchers, academics, and professionals who require detailed evidence synthesis for their work. It guides users through a multi-phase workflow that begins with clarifying the research question and selecting the appropriate mode of inquiry. Whether you need a quick fact-check report, a mini-review, or an extensive deep-research report, this skill adapts to your needs, producing structured deliverables that include bibliographies and evidence ratings.
The methodology employed by this skill emphasizes using published literature over memory, ensuring that users derive their findings from reliable sources. The skill incorporates advanced query strategies that allow users to refine their searches based on initial results, helping to avoid common pitfalls like irrelevant results or overly broad searches. With built-in tools for domain detection and cross-skill delegation, users can seamlessly integrate findings from specialized research areas, enhancing the depth and accuracy of their literature reviews.
Overall, Literature Deep Research is an essential tool for anyone involved in academic research, providing a robust set of instructions and methodologies to streamline the literature review process, enhance the quality of evidence synthesis, and ensure thorough documentation of findings.
When to use it
Use this skill when you need to conduct a thorough literature review or meta-analysis in biomedical fields, requiring evidence grading and structured reporting.
When not to use it
This skill is not suitable for casual research or when quick, informal information is needed, as it is designed for rigorous academic standards.
What you can build with it
Conducting a Meta-Analysis
Use this skill to gather and synthesize evidence from various studies, grading each source for quality and relevance.
Fact-Checking Research Claims
Quickly generate a fact-check report on a specific question, including evidence ratings and source citations.
Preparing a Literature Review for Publication
Create a comprehensive literature review report that meets academic standards, complete with structured sections and a bibliography.
How to install Literature Deep Research
View source1. Install with the skills CLI
npx skills add mims-harvard/tooluniverse/tooluniverse-literature-deep-research --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by mims-harvardLiterature 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
Frequently asked questions about Literature Deep Research
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