scvi-tools
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
- Installs
- 179
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/anthropics/life-sciences --skill scvi-tools- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads scvi-tools automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
scvi-tools Deep Learning Skill
This skill provides guidance for deep learning-based single-cell analysis using scvi-tools, the leading framework for probabilistic models in single-cell genomics.
How to Use This Skill
- Identify the appropriate workflow from the model/workflow tables below
- Read the corresponding reference file for detailed steps and code
- Use scripts in
scripts/to avoid rewriting common code - For installation or GPU issues, consult
references/environment_setup.md - For debugging, consult
references/troubleshooting.md
When to Use This Skill
- When scvi-tools, scVI, scANVI, or related models are mentioned
- When deep learning-based batch correction or integration is needed
- When working with multi-modal data (CITE-seq, multiome)
- When reference mapping or label transfer is required
- When analyzing ATAC-seq or spatial transcriptomics data
- When learning latent representations of single-cell data
Model Selection Guide
| Data Type | Model | Primary Use Case |
|---|---|---|
| scRNA-seq | scVI | Unsupervised integration, DE, imputation |
| scRNA-seq + labels | scANVI | Label transfer, semi-supervised integration |
| CITE-seq (RNA+protein) | totalVI | Multi-modal integration, protein denoising |
| scATAC-seq | PeakVI | Chromatin accessibility analysis |
| Multiome (RNA+ATAC) | MultiVI | Joint modality analysis |
| Spatial + scRNA reference | DestVI | Cell type deconvolution |
| RNA velocity | veloVI | Transcriptional dynamics |
| Cross-technology | sysVI | System-level batch correction |
Workflow Reference Files
| Workflow | Reference File | Description |
|---|---|---|
| Environment Setup | references/environment_setup.md | Installation, GPU, version info |
| Data Preparation | references/data_preparation.md | Formatting data for any model |
| scRNA Integration | references/scrna_integration.md | scVI/scANVI batch correction |
| ATAC-seq Analysis | references/atac_peakvi.md | PeakVI for accessibility |
| CITE-seq Analysis | references/citeseq_totalvi.md | totalVI for protein+RNA |
| Multiome Analysis | references/multiome_multivi.md | MultiVI for RNA+ATAC |
| Spatial Deconvolution | references/spatial_deconvolution.md | DestVI spatial analysis |
| Label Transfer | references/label_transfer.md | scANVI reference mapping |
| scArches Mapping | references/scarches_mapping.md | Query-to-reference mapping |
| Batch Correction | references/batch_correction_sysvi.md | Advanced batch methods |
| RNA Velocity | references/rna_velocity_velovi.md | veloVI dynamics |
| Troubleshooting | references/troubleshooting.md | Common issues and solutions |
CLI Scripts
Modular scripts for common workflows. Chain together or modify as needed.
Pipeline Scripts
| Script | Purpose | Usage |
|---|---|---|
prepare_data.py | QC, filter, HVG selection | python scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch |
train_model.py | Train any scvi-tools model | python scripts/train_model.py prepared.h5ad results/ --model scvi |
cluster_embed.py | Neighbors, UMAP, Leiden | python scripts/cluster_embed.py adata.h5ad results/ |
differential_expression.py | DE analysis | python scripts/differential_expression.py model/ adata.h5ad de.csv --groupby leiden |
transfer_labels.py | Label transfer with scANVI | python scripts/transfer_labels.py ref_model/ query.h5ad results/ |
integrate_datasets.py | Multi-dataset integration | python scripts/integrate_datasets.py results/ data1.h5ad data2.h5ad |
validate_adata.py | Check data compatibility | python scripts/validate_adata.py data.h5ad --batch-key batch |
Example Workflow
# 1. Validate input data
python scripts/validate_adata.py raw.h5ad --batch-key batch --suggest
# 2. Prepare data (QC, HVG selection)
python scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch --n-hvgs 2000
# 3. Train model
python scripts/train_model.py prepared.h5ad results/ --model scvi --batch-key batch
# 4. Cluster and visualize
python scripts/cluster_embed.py results/adata_trained.h5ad results/ --resolution 0.8
# 5. Differential expression
python scripts/differential_expression.py results/model results/adata_clustered.h5ad results/de.csv --groupby leiden
Python Utilities
The scripts/model_utils.py provides importable functions for custom workflows:
| Function | Purpose |
|---|---|
prepare_adata() | Data preparation (QC, HVG, layer setup) |
train_scvi() | Train scVI or scANVI |
evaluate_integration() | Compute integration metrics |
get_marker_genes() | Extract DE markers |
save_results() | Save model, data, plots |
auto_select_model() | Suggest best model |
quick_clustering() | Neighbors + UMAP + Leiden |
Critical Requirements
-
Raw counts required: scvi-tools models require integer count data
adata.layers["counts"] = adata.X.copy() # Before normalization scvi.model.SCVI.setup_anndata(adata, layer="counts") -
HVG selection: Use 2000-4000 highly variable genes
sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key="batch", layer="counts", flavor="seurat_v3") adata = adata[:, adata.var['highly_variable']].copy() -
Batch information: Specify batch_key for integration
scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="batch")
Quick Decision Tree
Need to integrate scRNA-seq data?
├── Have cell type labels? → scANVI (references/label_transfer.md)
└── No labels? → scVI (references/scrna_integration.md)
Have multi-modal data?
├── CITE-seq (RNA + protein)? → totalVI (references/citeseq_totalvi.md)
├── Multiome (RNA + ATAC)? → MultiVI (references/multiome_multivi.md)
└── scATAC-seq only? → PeakVI (references/atac_peakvi.md)
Have spatial data?
└── Need cell type deconvolution? → DestVI (references/spatial_deconvolution.md)
Have pre-trained reference model?
└── Map query to reference? → scArches (references/scarches_mapping.md)
Need RNA velocity?
└── veloVI (references/rna_velocity_velovi.md)
Strong cross-technology batch effects?
└── sysVI (references/batch_correction_sysvi.md)
Key Resources
FAQ
What does the scvi-tools skill do?
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
How do I install scvi-tools?
Run npx skills add https://github.com/anthropics/life-sciences --skill scvi-tools in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/scvi-tools/ (personal) or .claude/skills/scvi-tools/ (project).
Which AI agents can use scvi-tools?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is scvi-tools safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains scvi-tools?
It is published by anthropics in anthropics/life-sciences, and it is an official skill from the maker of the technology it covers. It has 179 recorded installs.
More from anthropics/life-sciences
scientific-problem-selection
anthropics/life-sciences
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
single-cell-rna-qc
anthropics/life-sciences
Open the skill to read its SKILL.md instructions.
nextflow-development
anthropics/life-sciences
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
clinical-trial-protocol-skill
anthropics/life-sciences
Open the skill to read its SKILL.md instructions.
instrument-data-to-allotrope
anthropics/life-sciences
Open the skill to read its SKILL.md instructions.
Popular education & science skills
teach
mattpocock/skills
Teach the user a new skill or concept, within this workspace.
scaffold-exercises
mattpocock/skills
Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.
paper-context-resolver
lllllllama/rigorpilot-skills
Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default.
caveman-learn
juliusbrussee/caveman
Act on a Caveman learn report - review the ranked token sinks, apply cost-lowering fixes with per-edit consent, and report what those fixes returned. Use when asked to lower an agent's token cost, what caveman has saved, to trim a heavy CLAUDE.md, or to offload re-pasted context into cavemem.
gsap-plugins
greensock/gsap-skills
Official GSAP skill for GSAP plugins — registration, ScrollToPlugin, ScrollSmoother, Flip, Draggable, Inertia, Observer, SplitText, ScrambleText, SVG and physics plugins, CustomEase, EasePack, CustomWiggle, CustomBounce, GSDevTools. Use when the user asks about a GSAP plugin, scroll-to, flip animations, draggable, SVG drawing, or plugin registration.
teach-impeccable
pbakaus/impeccable
DEPRECATED: Use /impeccable teach instead. This command has been folded into the impeccable skill.