tooluniverse-disease-research
Generate comprehensive disease research reports using 100+ ToolUniverse tools. Creates a detailed markdown report file and progressively updates it with findings from 10 research dimensions. All information includes source references. Use when users ask about diseases, syndromes, or need systematic disease analysis.
Best use case
tooluniverse-disease-research is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generate comprehensive disease research reports using 100+ ToolUniverse tools. Creates a detailed markdown report file and progressively updates it with findings from 10 research dimensions. All information includes source references. Use when users ask about diseases, syndromes, or need systematic disease analysis.
Teams using tooluniverse-disease-research should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tooluniverse-disease-research/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tooluniverse-disease-research Compares
| Feature / Agent | tooluniverse-disease-research | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generate comprehensive disease research reports using 100+ ToolUniverse tools. Creates a detailed markdown report file and progressively updates it with findings from 10 research dimensions. All information includes source references. Use when users ask about diseases, syndromes, or need systematic disease analysis.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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
| Dim | Section | Key Tools |
|-----|---------|-----------|
| 1 | Identity & Classification | OSL_get_efo_id_by_disease_name, ols_search_efo_terms, ols_get_efo_term, umls_search_concepts, icd_search_codes, snomed_search_concepts |
| 2 | Clinical Presentation | OpenTargets phenotypes, HPO lookup, MedlinePlus |
| 3 | Genetic & Molecular Basis | OpenTargets targets, ClinVar variants, GWAS associations, gnomAD |
| 4 | Treatment Landscape | OpenTargets drugs, clinical trials, GtoPdb |
| 5 | Biological Pathways | Reactome pathways, humanbase_ppi_analysis, GTEx expression, HPA |
| 6 | Epidemiology & Literature | PubMed, OpenAlex, Europe PMC, Semantic Scholar |
| 7 | Similar Diseases | OpenTargets similar entities |
| 8 | Cancer-Specific (if applicable) | CIViC genes/variants/therapies |
| 9 | Pharmacology | GtoPdb targets/interactions/ligands |
| 10 | Drug Safety | OpenTargets warnings, clinical trial AEs, FAERS |
See: tool_usage_details.md for complete tool calls per section.
---
## Report Template
Create this file structure at the start:
```markdown
# 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
```python
# 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:
| Grade | Criteria | Example |
|-------|---------|---------|
| **T1 (Strong)** | Replicated genetic evidence (GWAS, rare variants), FDA-approved therapy | BRCA1 → breast cancer; trastuzumab for HER2+ |
| **T2 (Moderate)** | Single genetic study, phase II+ trial data, strong biological evidence | FOXO3 → longevity (centenarian studies) |
| **T3 (Association)** | Observational data, gene expression changes, pathway membership | IL-6 elevated in Alzheimer's CSF |
| **T4 (Computational)** | Network proximity, text mining, predicted associations | DisGeNET 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
| Conflict | Resolution |
|----------|-----------|
| Different prevalence estimates across sources | Report range; note the most recent/largest study |
| Drug approved in one country but not another | Note regulatory status per region |
| Gene-disease association in one DB but absent in another | Grade by evidence type; text-mining alone is T4 |
| Clinical trial results contradict label indications | The 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 reportsRelated Skills
tooluniverse
Router skill for ToolUniverse tasks. First checks if specialized tooluniverse skills (105+ skills covering disease/drug/target research, gene-disease associations, clinical decision support, genomics, epigenomics, proteomics, comparative genomics, chemical safety, toxicology, systems biology, and more) can solve the problem, then falls back to general strategies for using 2300+ scientific tools. Covers tool discovery, multi-hop queries, comprehensive research workflows, disambiguation, evidence grading, and report generation. Use when users need to research any scientific topic, find biological data, or explore drug/target/disease relationships. ALSO USE for any biology, medicine, chemistry, pharmacology, or life science question — even simple factoid questions like "how many X in protein Y", "what drug interacts with Z", "what gene causes disease W", or "translate this sequence". These questions benefit from database lookups (UniProt, PubMed, ChEMBL, ClinVar, GWAS Catalog, etc.) rather than answering from memory alone. When in doubt about a scientific fact, USE THIS SKILL to verify against real databases.
tooluniverse-variant-to-mechanism
End-to-end variant-to-mechanism analysis: given a genetic variant (rsID or coordinates), trace its functional impact from regulatory context (GWAS, eQTL, RegulomeDB, ENCODE) through target gene identification (GTEx, OpenTargets L2G) to downstream pathway and disease biology (STRING, Reactome, GO enrichment, disease associations). Produces an evidence-graded mechanistic narrative linking genotype to phenotype. Use when asked "how does this variant cause disease?", "what is the mechanism of rs7903146?", "trace variant to pathway", or "connect this GWAS hit to biology".
tooluniverse-variant-interpretation
Systematic clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Aggregates evidence from ClinVar, gnomAD, CIViC, UniProt, and PDB across ACMG criteria. Produces pathogenicity scores (0-100), clinical recommendations, and treatment implications. Use when interpreting genetic variants, classifying variants of uncertain significance (VUS), performing ACMG variant classification, or translating variant calls to clinical actionability.
tooluniverse-variant-functional-annotation
Comprehensive functional annotation of protein variants — pathogenicity, population frequency, structural context, and clinical significance. Integrates ProtVar (map_variant, get_function, get_population) for protein-level mapping and structural context, ClinVar for clinical classifications, gnomAD for population frequency with ancestry data, CADD for deleteriousness scores, and ClinGen for gene-disease validity. Produces a structured variant annotation report with evidence grading. Use when asked about protein variant impact, missense variant pathogenicity, ProtVar annotation, variant functional context, or combining population and structural evidence for a variant.
tooluniverse-variant-analysis
Production-ready VCF processing, variant annotation, mutation analysis, and structural variant (SV/CNV) interpretation for bioinformatics questions. Parses VCF files (streaming, large files), classifies mutation types (missense, nonsense, synonymous, frameshift, splice, intronic, intergenic) and structural variants (deletions, duplications, inversions, translocations), applies VAF/depth/quality/consequence filters, annotates with ClinVar/dbSNP/gnomAD/CADD via ToolUniverse, interprets SV/CNV clinical significance using ClinGen dosage sensitivity scores, computes variant statistics, and generates reports. Solves questions like "What fraction of variants with VAF < 0.3 are missense?", "How many non-reference variants remain after filtering intronic/intergenic?", "What is the pathogenicity of this deletion affecting BRCA1?", or "Which dosage-sensitive genes overlap this CNV?". Use when processing VCF files, annotating variants, filtering by VAF/depth/consequence, classifying mutations, interpreting structural variants, assessing CNV pathogenicity, comparing cohorts, or answering variant analysis questions.
tooluniverse-vaccine-design
Design and evaluate vaccine candidates using computational immunology tools. Covers epitope prediction (MHC-I/II binding via IEDB), population coverage analysis, antigen selection, adjuvant matching, and immunogenicity assessment. Integrates IEDB for epitope prediction, UniProt for antigen sequences, PDB/AlphaFold for structural epitopes, BVBRC for pathogen proteomes, and literature for clinical precedent. Use when asked about vaccine design, epitope prediction, immunogenicity, MHC binding, T-cell epitopes, B-cell epitopes, or population coverage for vaccine candidates.
tooluniverse-toxicology
Assess chemical and drug toxicity via adverse outcome pathways, real-world adverse event signals, and toxicogenomic evidence. Integrates AOPWiki (AOPWiki_list_aops, AOPWiki_get_aop) for mechanism- level pathway tracing, FAERS for post-market adverse event quantification, OpenFDA for label mining, and CTD for chemical-gene-disease evidence. Produces structured toxicity reports with evidence grading (T1-T4). Use when asked about toxicity mechanisms, adverse outcome pathways, AOP mapping, FAERS signal detection, or chemical-disease relationships for drugs or environmental chemicals.
tooluniverse-target-research
Gather comprehensive biological target intelligence from 9 parallel research paths covering protein info, structure, interactions, pathways, expression, variants, drug interactions, and literature. Features collision-aware searches, evidence grading (T1-T4), explicit Open Targets coverage, and mandatory completeness auditing. Use when users ask about drug targets, proteins, genes, or need target validation, druggability assessment, or comprehensive target profiling.
tooluniverse-systems-biology
Comprehensive systems biology and pathway analysis using multiple pathway databases (Reactome, KEGG, WikiPathways, Pathway Commons, BioModels). Performs pathway enrichment, protein-pathway mapping, keyword searches, and systems-level analysis. Use when analyzing gene sets, exploring biological pathways, or investigating systems-level biology.
tooluniverse-structural-variant-analysis
Comprehensive structural variant (SV) analysis skill for clinical genomics. Classifies SVs (deletions, duplications, inversions, translocations), assesses pathogenicity using ACMG-adapted criteria, evaluates gene disruption and dosage sensitivity, and provides clinical interpretation with evidence grading. Use when analyzing CNVs, large deletions/duplications, chromosomal rearrangements, or any structural variants requiring clinical interpretation.
tooluniverse-structural-proteomics
Integrate structural biology data with proteomics for drug target validation. Retrieves protein structures from PDB (RCSB, PDBe), AlphaFold predictions, antibody structures (SAbDab), GPCR data (GPCRdb), binding pocket analysis (ProteinsPlus), and ligand interactions (BindingDB). Use when asked to find structures for a drug target, identify binding site ligands, cross-validate drug binding with structural data, assess structural druggability, or compare experimental vs predicted structures.
tooluniverse-stem-cell-organoid
Research stem cells, iPSCs, organoids, and cell differentiation using ToolUniverse tools. Covers pluripotency marker identification, differentiation pathway analysis, organoid model characterization, cell type annotation, and disease modeling. Integrates CellxGene/HCA for single-cell atlas data, CellMarker for cell type markers, GEO for stem cell datasets, and pathway tools for differentiation signaling. Use when asked about stem cells, iPSCs, organoids, cell reprogramming, pluripotency, differentiation protocols, or 3D culture models.