Scvi Tools

What it does

When your agent uses it

• Generative modeling
• Reference mapping
• Multimodal analysis
• Model-based uncertainty

Example prompts

• “/scvi-tools”

Requirements

• Python 3
• Compatibility (from SKILL.md): Requires Python 3.12+ and scvi-tools with model-specific dependencies. CPU supported; accelerator requirements depend on PyTorch and hardware. Network access is needed for installation or optional dataset/genome downloads, not local model fitting.

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Confirm modality, raw-data provenance, unique cell/feature IDs, feature order,
2. 2Perform QC and feature selection before model registration. Use count-aware
3. 3Register the exact data object with that model's setup_anndata or
4. 4Inspect validation history, held-out fit, seed stability, batch mixing and
5. 5Extract representations or model-specific normalized outputs; save the model,

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. 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):

  • github.com
  • arxiv.org
  • scviva-tools.readthedocs.io
  • docs.scvi-tools.org
  • doi.org
  • export.arxiv.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.

• Compatibility

  Requires Python 3.12+ and scvi-tools with model-specific dependencies. CPU supported; accelerator requirements depend on PyTorch and hardware. Network access is needed for installation or optional dataset/genome downloads, not local model fitting.

  From compatibility in the SKILL.md frontmatter.

Context cost

Scvi Tools loads about 2.6k tokens when it runs, and up to ~17k if it reads all its reference files. Until then it costs about 90 tokens; SKILL.md has 882 words of instructions outside code blocks.

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

~90

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

~2.6k

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

~17k

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 K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its BSD-3-Clause licence (© K-Dense-AI). 882 words, ~2,559 tokens.

name

scvi-tools

description

Fits probabilistic models for single-cell omics, including scVI batch integration, scANVI annotation, totalVI CITE-seq, MultiVI RNA/ATAC integration, and posterior differential expression. Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty; use scanpy for standard preprocessing and exploratory analysis.

compatibility

Requires Python 3.12+ and scvi-tools with model-specific dependencies. CPU supported; accelerator requirements depend on PyTorch and hardware. Network access is needed for installation or optional dataset/genome downloads, not local model fitting.

license

BSD-3-Clause license

metadata.version

1.4

metadata.skill-author

K-Dense Inc.

metadata.last-reviewed

2026-10-01

metadata.upstream-version

1.5.1

import numpy as np
import scanpy as sc
import scvi
from scipy import sparse

scvi.settings.seed = 0
adata = sc.read_h5ad("rna_counts.h5ad")
assert adata.obs_names.is_unique and adata.var_names.is_unique
assert adata.obs["batch"].notna().all()
x = adata.X
values = x.data if sparse.issparse(x) else np.asarray(x)
assert np.isfinite(values).all() and (values >= 0).all()
assert np.allclose(values, np.rint(values))
assert (np.asarray(x.sum(axis=1)).ravel() > 0).all()
adata.layers["counts"] = x.copy()
sc.pp.filter_genes(adata, min_cells=3)
sc.pp.highly_variable_genes(
    adata, layer="counts", flavor="seurat_v3", batch_key="batch",
    n_top_genes=min(2000, adata.n_vars), subset=True,
)
adata = adata.copy()
assert (np.asarray(adata.layers["counts"].sum(axis=1)).ravel() > 0).all()
scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="batch")
model = scvi.model.SCVI(adata, n_latent=20, gene_likelihood="nb")
model.train(max_epochs=400, early_stopping=True)
adata.obsm["X_scVI"] = model.get_latent_representation()
sc.pp.neighbors(adata, use_rep="X_scVI")
sc.tl.umap(adata)
sc.tl.leiden(adata, flavor="igraph", n_iterations=2, directed=False)
model.save("scvi_model", save_anndata=True)

# SCVI normalized expression: a potentially dense cells-by-genes matrix.
normalized = model.get_normalized_expression(
    gene_list=adata.var_names[:20].tolist(), library_size=1e4,
    n_samples=25, return_mean=True,
)
# This comparison is conditional on the fitted model and selected genes.
de = model.differential_expression(
    groupby="cell_type", group1="TypeA", group2="TypeB",
    mode="change", delta=0.5, fdr_target=0.05,
    batch_correction=False, n_samples_overall=5000,
)

uv venv --python 3.13 .venv-scvi
uv pip install --python .venv-scvi/bin/python "scvi-tools==1.5.1" "scanpy==1.12.4" igraph

© K-Dense-AI, BSD-3-Clause. 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 skills/scvi-tools of K-Dense-AI/scientific-agent-skills.

• 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 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

• lingxling/awesome-skills-cnclaude-scientific-skills/skills/scvi-tools