OpenBioMed is an agent platform and toolkit collection for biomedical research and drug discovery, covering areas such as molecular design, protein analysis, and single-cell data analysis. It is intended for researchers and provides the biomedical skills listed in the catalogue as workflows for Claude Code.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/PharMolix/OpenBioMednpx agentmods add skills/pharmolix/openbiomed/single-cell-multi-omics-analysis-scviWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-multi-omics-analysis-scvi)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-multi-omics-analysis-scvi"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-multi-omics-analysis-scvi/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-multi-omics-analysis-scvi"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-multi-omics-analysis-scvi.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00094 | $0.01947 |
| Opus 5 | $0.00047 | $0.00974 |
| Sonnet 5 | $0.00019 | $0.00389 |
| Haiku 4.5 | $0.00009 | $0.00195 |
Grade A, and why
single-cell-multi-omics-analysis-scvi scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
scvi-tools Single-Cell Multi-Omics Analysis
scvi-tools is a comprehensive Python framework for probabilistic models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it provides deep generative models using variational inference for analyzing diverse single-cell data modalities.
What it does
scvi-tools provides models organized by data modality:
1. Single-Cell RNA-seq Analysis
Core models for expression analysis, batch correction, and integration. See references/models-scrna-seq.md for:
- scVI: Unsupervised dimensionality reduction and batch correction
- scANVI: Semi-supervised cell type annotation and integration
- AUTOZI: Zero-inflation detection and modeling
- VeloVI: RNA velocity analysis
- contrastiveVI: Perturbation effect isolation
2. Chromatin Accessibility (ATAC-seq)
Models for analyzing single-cell chromatin data. See references/models-atac-seq.md for:
- PeakVI: Peak-based ATAC-seq analysis and integration
- PoissonVI: Quantitative fragment count modeling
- scBasset: Deep learning approach with motif analysis
3. Multimodal & Multi-omics Integration
Joint analysis of multiple data types. See references/models-multimodal.md for:
- totalVI: CITE-seq protein and RNA joint modeling
- MultiVI: Paired and unpaired multi-omic integration
- MrVI: Multi-resolution cross-sample analysis
4. Spatial Transcriptomics
Spatially-resolved transcriptomics analysis. See references/models-spatial.md for:
- DestVI: Multi-resolution spatial deconvolution
- Stereoscope: Cell type deconvolution
- Tangram: Spatial mapping and integration
- scVIVA: Cell-environment relationship analysis
5. Specialized Modalities
Additional specialized analysis tools. See references/models-specialized.md for:
- MethylVI/MethylANVI: Single-cell methylation analysis
- CytoVI: Flow/mass cytometry batch correction
- Solo: Doublet detection
- CellAssign: Marker-based cell type annotation
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 225 lines · 94 tokens per session scan A 3df6c391e37a
single-cell-multi-omics-analysis-scvi is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 94 tokens to every session and 1,947 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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