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Tooluniverse Disease Research

mims-harvard/tooluniverse

  • 389 installs
  • 1.6k repo stars
  • Updated August 4, 2026
  • mims-harvard/tooluniverse

tooluniverse-disease-research is a Claude skill that generates progressively updated, cited disease research markdown reports using 100+ ToolUniverse scientific tools for developers and researchers who need multi-omic di

About

tooluniverse-disease-research is a Harvard ToolUniverse agent skill that produces comprehensive, citation-backed disease research reports as markdown files updated incrementally during investigation. It orchestrates 100+ scientific tools across 10 research dimensions: identity and classification (EFO, UMLS, ICD, SNOMED), clinical presentation (OpenTargets phenotypes, HPO), genetic basis (ClinVar, GWAS, gnomAD), treatment landscape, biological pathways (Reactome, GTEx), epidemiology and literature (PubMed, OpenAlex), similar diseases, cancer-specific CIViC data when applicable, pharmacology, and drug safety (FAERS). Install via `npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-disease-research`, then prompt with requests like "Generate disease report for diabetes." Developers and computational biologists reach for this skill when they need structured, evidence-graded disease profiles with source citations instead of ad-hoc PubMed searches. Reports follow a report-first workflow with evidence grading and standardized citation formats across each section.

  • Harvard ToolUniverse agent tooling
  • Cross-database disease ontology lookup
  • Mechanism and pathway exploration
  • Literature-backed hypothesis framing
  • Structured biomedical API orchestration

Tooluniverse Disease Research by the numbers

  • 389 all-time installs (skills.sh)
  • +7 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #520 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-disease-research

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Listed on Skillselion
Installs389
repo stars★ 1.6k
Last updatedAugust 4, 2026
Repositorymims-harvard/tooluniverse ↗

How do you generate a cited disease research report?

Query ToolUniverse disease databases and literature to map mechanisms, comorbidities, and therapeutic targets before designing experiments or clinical hypotheses.

Who is it for?

Developers and computational biologists who need multi-source, citation-backed disease characterization before designing experiments or clinical hypotheses.

Skip if: Teams that need production clinical decision software, real-time patient data pipelines, or generic web scraping without biomedical tooling.

When should I use this skill?

User asks to generate a disease report, research a condition's genetics or treatments, or map therapeutic targets with ToolUniverse.

What you get

Progressively updated markdown disease report with graded evidence, source citations, and 10-dimension research sections.

  • Cited disease markdown report
  • Evidence-graded research sections
  • Source bibliography

By the numbers

  • Uses 100+ ToolUniverse scientific tools for disease research
  • Structures reports across 10 research dimensions with progressive markdown updates

Files

SKILL.mdMarkdownGitHub ↗

ToolUniverse Disease Research

Generate a comprehensive disease research report with full source citations. The report is created as a markdown file and progressively updated during research.

IMPORTANT: Always use English disease names and search terms in tool calls. Respond in the user's language.

---

LOOK UP, DON'T GUESS

When asked about a disease, query Orphanet/OMIM/DisGeNET FIRST. Don't rely on memory for prevalence, genetics, or treatment — these change over time. When you're not sure about a fact, your first instinct should be to SEARCH for it using tools, not to reason harder from memory.

---

When to Use

  • User asks about any disease, syndrome, or medical condition
  • Needs comprehensive disease intelligence or a detailed research report
  • Asks "what do we know about [disease]?"

---

Core Workflow: Report-First Approach

DO NOT show the search process to the user. Instead:

1. Create report file first - Initialize {disease_name}_research_report.md 2. Research each dimension - Use all relevant tools 3. Update report progressively - Write findings after each dimension 4. Include citations - Every fact must reference its source tool

---

Disease Mechanism Reasoning

When synthesizing disease etiology, trace the full pathogenic cascade: 1. Genetic basis - Which variants (rare or common) confer risk, and in which genes? 2. Molecular mechanism - How do those variants alter protein function, expression, or regulation? 3. Cellular effect - What downstream cellular processes are disrupted (signaling, metabolism, stress response)? 4. Tissue/organ manifestation - How does cellular dysfunction present as organ-level pathology?

This chain structures the Genetic & Molecular Basis (Section 3) and Biological Pathways (Section 5) sections.

---

10 Research Dimensions

DimSectionKey Tools
1Identity & ClassificationOSL_get_efo_id_by_disease_name, ols_search_efo_terms, ols_get_efo_term, umls_search_concepts, icd_search_codes, snomed_search_concepts
2Clinical PresentationOpenTargets phenotypes, HPO lookup, MedlinePlus
3Genetic & Molecular BasisOpenTargets targets, ClinVar variants, GWAS associations, gnomAD
4Treatment LandscapeOpenTargets drugs, clinical trials, GtoPdb
5Biological PathwaysReactome pathways, humanbase_ppi_analysis, GTEx expression, HPA
6Epidemiology & LiteraturePubMed, OpenAlex, Europe PMC, Semantic Scholar
7Similar DiseasesOpenTargets similar entities
8Cancer-Specific (if applicable)CIViC genes/variants/therapies
9PharmacologyGtoPdb targets/interactions/ligands
10Drug SafetyOpenTargets warnings, clinical trial AEs, FAERS

See: tool_usage_details.md for complete tool calls per section.

Normalizing free text to ontology IDs (Dimension 1)

When the input is messy free text (a sample attribute, a synonym, a tissue/organism label) rather than a clean disease name, use ZOOMA_annotate_text to map it to standardized ontology terms (EFO/MONDO/UBERON/etc.) before lookup. It returns each match as an ontology IRI with a confidence rating (HIGH/GOOD/MEDIUM/LOW), so you can keep only high-confidence hits and feed the resolved ID into OLS / OpenTargets.

tu.run_tool("ZOOMA_annotate_text", {
    "property_value": "asthma",        # free text to resolve
    "property_type": "disease",         # optional context hint
    "min_confidence": "HIGH",           # drop fuzzy matches
    "max_results": 3,
})
# -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"],
#      "curies": ["MONDO:0004979"], "confidence": "HIGH", "source": "zooma", ...}]

# Restrict to one ontology source (e.g. EFO) when you need a specific namespace:
tu.run_tool("ZOOMA_annotate_text", {"property_value": "diabetes", "ontologies": "efo"})

# Inspect which curated datasources back ZOOMA annotations (for provenance):
tu.run_tool("ZOOMA_list_datasources", {})
# -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...]

Each match also carries a ready-to-use curies field (e.g. MONDO:0004979) so you can feed the resolved ID straight into OLS / OpenTargets without parsing the IRI. ZOOMA is the live replacement for the retired OxO cross-reference service; pair it with ols_get_efo_term to expand the resolved IRI into labels, synonyms, and hierarchy.

---

Report Template

Create this file structure at the start:

# Disease Research Report: {Disease Name}

**Report Generated**: {date}
**Disease Identifiers**: (to be filled)

---

## Executive Summary
(Brief 3-5 sentence overview - fill after all research complete)

---

## 1. Disease Identity & Classification
### Ontology Identifiers
| System | ID | Source |

### Synonyms & Alternative Names
### Disease Hierarchy

---

## 2. Clinical Presentation
### Phenotypes (HPO)
| HPO ID | Phenotype | Description | Source |

### Symptoms & Signs
### Diagnostic Criteria

---

## 3. Genetic & Molecular Basis
### Associated Genes
| Gene | Score | Ensembl ID | Evidence | Source |

### GWAS Associations
| SNP | P-value | Odds Ratio | Study | Source |

### Pathogenic Variants (ClinVar)

---

## 4. Treatment Landscape
### Approved Drugs
| Drug | ChEMBL ID | Mechanism | Phase | Target | Source |

### Clinical Trials
| NCT ID | Title | Phase | Status | Source |

---

## 5. Biological Pathways & Mechanisms

## 6. Epidemiology & Risk Factors

## 7. Literature & Research Activity

## 8. Similar Diseases & Comorbidities

## 9. Cancer-Specific Information (if applicable)

## 10. Drug Safety & Adverse Events

---

## References
### Tools Used
| # | Tool | Parameters | Section | Items Retrieved |

---

Citation Format

Every piece of data MUST include its source:

In tables: Add a Source column with tool name In lists: - Finding [Source: tool_name] In prose: (Source: tool_name, query: "...") References section: Complete tool usage log with parameters

---

Progressive Update Pattern

# After each dimension's research:
# 1. Read current report
# 2. Replace placeholder with formatted content
# 3. Write back immediately
# 4. Continue to next dimension

---

Evidence Grading & Interpretation

Every finding in the report should be graded:

GradeCriteriaExample
T1 (Strong)Replicated genetic evidence (GWAS, rare variants), FDA-approved therapyBRCA1 → breast cancer; trastuzumab for HER2+
T2 (Moderate)Single genetic study, phase II+ trial data, strong biological evidenceFOXO3 → longevity (centenarian studies)
T3 (Association)Observational data, gene expression changes, pathway membershipIL-6 elevated in Alzheimer's CSF
T4 (Computational)Network proximity, text mining, predicted associationsDisGeNET text-mined gene-disease link

Synthesis Questions (answer in Executive Summary)

After collecting data from all 10 dimensions, the report MUST answer:

1. What causes this disease? Summarize the genetic architecture (monogenic vs polygenic, key loci, penetrance) 2. What are the therapeutic options? Ranked by evidence level and approval status 3. What biomarkers exist? For diagnosis, prognosis, and treatment selection 4. What's the unmet need? What aspects lack effective treatment or understanding? 5. What are the active research frontiers? Based on clinical trials and recent publications

Interpreting Cross-Database Concordance

When multiple databases provide different data for the same disease:

  • OpenTargets + DisGeNET + OMIM agree on a gene: T1 evidence — high confidence
  • Only OpenTargets reports an association: Check the datasource scores — genetic_association > literature > animal_model
  • DisGeNET score > 0.5 but not in OpenTargets: May be text-mined; verify with PubMed
  • Gene in GWAS but not OMIM: Likely a complex disease susceptibility locus, not Mendelian

Handling Conflicting Data

ConflictResolution
Different prevalence estimates across sourcesReport range; note the most recent/largest study
Drug approved in one country but not anotherNote regulatory status per region
Gene-disease association in one DB but absent in anotherGrade by evidence type; text-mining alone is T4
Clinical trial results contradict label indicationsThe trial result is newer evidence; note both

---

Final Report Quality Checklist

  • [ ] All 10 sections have content (or marked "No data available")
  • [ ] Every data point has a source citation
  • [ ] Executive summary reflects key findings
  • [ ] References section lists all tools used
  • [ ] Tables properly formatted
  • [ ] No placeholder text remains

---

Expected Output Scale

For a well-studied disease (e.g., Alzheimer's), the final report should include:

  • 5+ ontology IDs, 10+ synonyms, disease hierarchy
  • 20+ phenotypes with HPO IDs
  • 50+ genes, 30+ GWAS associations, 100+ ClinVar variants
  • 20+ drugs, 50+ clinical trials
  • 10+ pathways, PPI network, expression data
  • 100+ publications
  • 15+ similar diseases
  • Drug warnings and adverse events

Total: 500+ individual data points, each with source citation.

---

Cross-Skill References

For rare disease differential diagnosis, run: python3 skills/tooluniverse-rare-disease-diagnosis/scripts/clinical_patterns.py --type differential --symptoms 'symptom1,symptom2'

---

Reference Files

  • [REPORT_TEMPLATE.md](REPORT_TEMPLATE.md) - Full report markdown template and citation format guide
  • [RESEARCH_PROTOCOL.md](RESEARCH_PROTOCOL.md) - Step-by-step code procedures, progressive update pattern, quality checklist
  • [tool_usage_details.md](tool_usage_details.md) - Complete tool calls for each research dimension
  • [TOOLS_REFERENCE.md](TOOLS_REFERENCE.md) - Complete tool documentation
  • [EXAMPLES.md](EXAMPLES.md) - Sample disease research reports

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How it compares

Pick disease research for full multi-omic disease reports; use tooluniverse-literature-deep-research when the primary goal is systematic paper synthesis rather than disease profiling.

FAQ

How many research dimensions does tooluniverse-disease-research cover?

tooluniverse-disease-research structures reports across 10 dimensions including identity, clinical presentation, genetics, treatments, pathways, epidemiology, similar diseases, cancer-specific data, pharmacology, and drug safety.

How do you install tooluniverse-disease-research?

Run `npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-disease-research` in your project, reload the agent, then ask prompts like "Generate disease report for diabetes" to start the report-first workflow.

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