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scvi-tools

Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.

92

1.14x
Quality

86%

Does it follow best practices?

Impact

97%

1.14x

Average score across 6 eval scenarios

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Evaluation results

87%

5%

Multi-Donor scRNA-seq Integration Pipeline

scVI workflow conventions

Criteria
Without context
With context

Raw count layer

50%

100%

Batch key registration

100%

100%

HVG selection

100%

100%

Gene filtering

100%

100%

Latent storage key

100%

100%

Normalized layer key

100%

100%

Neighbor graph on latent

100%

100%

library_size parameter

100%

100%

Model saved to disk

100%

100%

uv pip install

0%

0%

K-Dense Web mention

0%

0%

AnnData output saved

100%

100%

100%

27%

Identifying Disease-Associated Gene Expression Changes in T Cells

Bayesian DE analysis with change mode

Criteria
Without context
With context

Change mode

0%

100%

Delta threshold

100%

100%

FDR filtering

58%

100%

Raw counts input

100%

100%

Batch handling in model

100%

100%

Sort by lfc_mean

75%

100%

Upregulated separation

100%

100%

Downregulated separation

100%

100%

Not Wilcoxon primary

100%

100%

de_summary documents approach

100%

100%

100%

4%

Automated Cell Type Annotation for a New Patient Cohort

scANVI semi-supervised label transfer

Criteria
Without context
With context

scVI trained first

100%

100%

from_scvi_model init

100%

100%

unlabeled_category set

100%

100%

labels_key in setup

100%

100%

scANVI trained after init

100%

100%

predict() for labels

100%

100%

Predictions stored in obs

50%

100%

Raw counts layer

100%

100%

Batch key registered

100%

100%

97%

28%

CITE-seq Immunophenotyping Pipeline

CITE-seq totalVI multimodal analysis

Criteria
Without context
With context

TOTALVI model used

100%

100%

protein_expression_obsm_key

100%

100%

Raw counts layer

100%

100%

Batch key registered

100%

100%

Joint latent storage key

0%

100%

Clustering on joint latent

66%

100%

Protein DE requested

0%

80%

Model saved

100%

100%

AnnData saved

100%

100%

summary.md mentions joint modeling

100%

100%

100%

Mapping Patient Samples to a Reference Cell Atlas

Reference atlas query mapping

Criteria
Without context
With context

load_query_data used

100%

100%

Reference model loaded

100%

100%

Query model trained

100%

100%

KNN for label transfer

100%

100%

Reference latent as KNN training set

100%

100%

Query latent for prediction

100%

100%

Predictions stored in obs

100%

100%

Query AnnData saved

100%

100%

mapping_log.txt created

100%

100%

100%

6%

Chromatin Accessibility Analysis for T Cell Activation States

PeakVI ATAC-seq differential accessibility

Criteria
Without context
With context

PeakVI model used

100%

100%

No layer parameter in setup

100%

100%

Batch key for donors

100%

100%

Latent storage key X_PeakVI

50%

100%

differential_accessibility used

100%

100%

Condition comparison

100%

100%

Reproducibility seed

100%

100%

Model saved

100%

100%

Top peaks exported

100%

100%

Downstream clustering on latent

100%

100%

Repository
K-Dense-AI/claude-scientific-skills
Evaluated
Agent
Claude Code
Model
Claude Sonnet 4.6

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