Tooluniverse Spatial Omics Analysis
This is a comprehensive pipeline for extracting biological meaning from spatial transcriptomics data. You feed it spatially variable genes, tissue type, and optional domain annotations, and it runs through eight analysis phases: gene characterization, pathway enrichment, domain comparison, cell-cell interaction prediction, disease/drug context, and literature validation. It's built around the "look up, don't guess" principle, querying databases like OpenTargets, STRING, HPA, and PubMed rather than relying on model knowledge. Works with Visium, MERFISH, and other spatial platforms. The skill is most useful when you have domain-specific gene lists and want to move beyond statistics into mechanisms and therapeutic opportunities. Includes an integration scoring system and evidence grading from clinical (T1) to computational (T4).
- skills.sh installs
- 363
- GitHub stars
- 1.8K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-spatial-omics-analysis --agent claude-codeInstalls into .claude/skills of the current project.
Files
Spatial Multi-Omics Analysis Pipeline
Comprehensive biological interpretation of spatial omics data. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into actionable biological insights.
KEY PRINCIPLES:
- Report-first approach - Create report file FIRST, then populate progressively
- Domain-by-domain analysis - Characterize each spatial region independently before comparison
- Gene-list-centric - Analyze user-provided SVGs and marker genes with ToolUniverse databases
- Biological interpretation - Go beyond statistics to explain biological meaning of spatial patterns
- Disease focus - Emphasize disease mechanisms and therapeutic opportunities when disease context is provided
- Evidence grading - Grade all evidence as T1 (human/clinical) to T4 (computational)
- Multi-modal thinking - Integrate RNA, protein, and metabolite information when available
- Validation guidance - Suggest experimental validation approaches for key findings
- Source references - Every statement must cite tool/database source
- English-first queries - Always use English terms in tool calls
Sponsors
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- Repository
- github.com/mims-harvard/tooluniverse
- Categories
- DevOps & CI/CD, Data Science & ML, Automation & Workflows, Sales & Marketing
- First seen
- Jun 3, 2026
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