Systematic literature research: disambiguate, search with collision-aware queries, grade evidence, produce structured reports.
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node --versiontooluniverse-literature-deep-researchExecute the skills CLI command in your project's root directory to begin installation:
Fetches tooluniverse-literature-deep-research from mims-harvard/tooluniverse and configures it for Cursor.
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Restart Cursor to activate tooluniverse-literature-deep-research. Access via /tooluniverse-literature-deep-research in your agent's command palette.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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
Search PubMed/EuropePMC FIRST before reasoning. A published paper beats memory.
Factoid search strategy:
EuropePMC_search_articles(query="term1 term2 term3", limit=5)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.
Phase 0: Clarify + Mode Select → Phase 1: Disambiguate + Profile → Phase 2: Literature Search → Phase 3: Report
| 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 |
# [TOPIC]: Fact-check Report
## Question / ## Answer (with evidence rating) / ## Source(s) / ## Verification Notes / ## Limitations
| 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 |
tooluniverse-target-researchtooluniverse-drug-researchtooluniverse-disease-researchUse this skill for literature synthesis. Use specialized skills for entity profiling. For max depth, run both.
UniProt_search → UniProt_get_entry_by_accession → UniProt_id_mapping
ensembl_lookup_gene → MyGene_get_gene_annotation
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.
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.
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
OpenTargets disease search → EFO/MONDO IDs
DisGeNET_get_disease_genes, DisGeNET_search_disease
CTD_get_disease_chemicals
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.
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.
Methodology stays internal. Report shows findings, not process.
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]
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.
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.
Retry once -> fallback tool. Key fallbacks: PubMed_get_cited_by -> EuropePMC_get_citations -> OpenCitations. OA: Unpaywall if configured, else Europe PMC/PMC/OpenAlex flags.
| 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.
| 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).
Brief progress updates only: "Resolving identifiers...", "Building paper set...", "Grading evidence..." Do NOT expose: raw tool outputs, dedup counts, search round details.
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 examplesMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
I recommend tooluniverse-literature-deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
tooluniverse-literature-deep-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: tooluniverse-literature-deep-research is focused, and the summary matches what you get after install.
Registry listing for tooluniverse-literature-deep-research matched our evaluation — installs cleanly and behaves as described in the markdown.
tooluniverse-literature-deep-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added tooluniverse-literature-deep-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
tooluniverse-literature-deep-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend tooluniverse-literature-deep-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: tooluniverse-literature-deep-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
tooluniverse-literature-deep-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
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