Run scvi-tools single-cell workflows. Use when a task asks for scVI/scANVI setup, latent embeddings, batch correction, differential expression, cell annotation, or reproducible AnnData analysis.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# scvi-tools
Use this skill for reproducible scvi-tools analysis.
Workflow:
1. Record AnnData path, organism, assay, batch keys, label keys, covariates, train/test split, and filtering choices.
2. Verify Python environment, GPU/CPU route, package version, and data availability before training.
3. Save preprocessing notebook, model parameters, training logs, latent embeddings, differential-expression tables, and plots.
4. Check batch mixing, biological separation, marker consistency, and sensitivity to preprocessing choices.
5. Attach exact commands and artifact paths to the final summary.
Keep raw counts, normalized values, and model-derived latent variables clearly separated.
The line at the top of this page pins the label git:20261014.9f2a5e0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
GET https://markdownregistry.com/api/v1/artifacts/art_pjsrzlyxbx3z4ecw
GET https://markdownregistry.com/api/v1/resolve?ref=yichen-zju/claude-for-research/scvi-tools
GET https://markdownregistry.com/api/v1/blob/eb31f53f93f7adf63bc6296b5cf06aad53081653f0d4998dc7ccc36d385dbd39
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