Scvi Tools

What it does

When your agent uses it

• Probabilistic modeling
• Batch correction
• Dimensionality reduction
• Differential expression

Example prompts

• “/scvi-tools”

Requirements

• Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

1. 1Single-Cell RNA-seq Analysis
2. 2Chromatin Accessibility (ATAC-seq)
3. 3Multimodal & Multi-omics Integration
4. 4Spatial Transcriptomics
5. 5Specialized Modalities

What it can do on your machine

Read from SKILL.md and the folder at commit 46b4d8b. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • uv

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • docs.scvi-tools.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scvi Tools loads about 1.8k tokens when it runs, and up to ~24k if it reads all its reference files. Until then it costs about 98 tokens; SKILL.md has 527 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~98

When it runs · the whole SKILL.md, loaded when a task matches

~1.8k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~24k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit 46b4d8b, republished under its MIT licence (© davila7). 527 words, ~1,808 tokens.

name

scvi-tools

description

This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.

# 1. Load and preprocess data (AnnData format)
import scvi
import scanpy as sc

adata = scvi.data.heart_cell_atlas_subsampled()
sc.pp.filter_genes(adata, min_counts=3)
sc.pp.highly_variable_genes(adata, n_top_genes=1200)

# 2. Register data with model (specify layers, covariates)
scvi.model.SCVI.setup_anndata(
    adata,
    layer="counts",  # Use raw counts, not log-normalized
    batch_key="batch",
    categorical_covariate_keys=["donor"],
    continuous_covariate_keys=["percent_mito"]
)

# 3. Create and train model
model = scvi.model.SCVI(adata)
model.train()

# 4. Extract latent representations and normalized values
latent = model.get_latent_representation()
normalized = model.get_normalized_expression(library_size=1e4)

# 5. Store in AnnData for downstream analysis
adata.obsm["X_scVI"] = latent
adata.layers["scvi_normalized"] = normalized

# 6. Downstream analysis with scanpy
sc.pp.neighbors(adata, use_rep="X_scVI")
sc.tl.umap(adata)
sc.tl.leiden(adata)

de_results = model.differential_expression(
    groupby="cell_type",
    group1="TypeA",
    group2="TypeB",
    mode="change",  # Use composite hypothesis testing
    delta=0.25      # Minimum effect size threshold
)

# Save model
model.save("./model_directory", overwrite=True)

# Load model
model = scvi.model.SCVI.load("./model_directory", adata=adata)

# Register batch information
scvi.model.SCVI.setup_anndata(adata, batch_key="study")

# Model automatically learns batch-corrected representations
model = scvi.model.SCVI(adata)
model.train()
latent = model.get_latent_representation()  # Batch-corrected

uv pip install scvi-tools
# For GPU support
uv pip install scvi-tools[cuda]

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (references) in cli-tool/components/skills/scientific/scvi-tools of davila7/claude-code-templates.

• SKILL.md
• references/differential-expression.md
• references/models-atac-seq.md
• references/models-multimodal.md
• references/models-scrna-seq.md
• references/models-spatial.md
• references/models-specialized.md
• references/theoretical-foundations.md
• references/workflows.md

Open the folder on GitHubat commit 46b4d8b

Used in 11 other repositories

We found 15 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 11 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

• jimmc414/Kosmoskosmos-claude-scientific-skills/scientific-skills/scvi-tools
• Microck/ordinary-claude-skillsskills_all/scvi-toolsskills_categorized/lab-tools/scvi-tools+2 more
• aiskillstore/marketplaceskills/davila7/scvi-toolsskills/k-dense-ai/scvi-tools
• lamm-mit/scienceclawskills/scvi-tools
• agent-skills-hub/agent-skills-hubskills/scvi-tools
• LeonChaoX/qinyan-academic-skillsskills/05-生物信息与基因组学/scvi-tools
• wu-yc/LabClawskills/bio/scvi-tools
• FreedomIntelligence/OpenClaw-Medical-Skillsskills/scvi-tools
• BioTender-max/awesome-bio-agent-skillsskills/kdense/scvi-tools
• infometa/workbuddyskillsplugins/codebuddy-plugins-official/external_plugins/scientific-skills/scvi-tools
• ai4nucleome/BioMaster.opencode/bioskills/kdense-scvi-tools