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Deep generative models for single-cell omics.
Passed 21 of 22 test cases with the skill, and 13 of 22 without it.
and 9 more in the full report
9 fixed12 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to run RNA velocity analysis using VeloVI on my single-cell RNA-seq dataset. What is the full Python import or module path to access the VeloVI class within the `scvi` library package?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies scvi.external as the namespace module for VeloVI. | Missed | Passed |
The instructions your model reads when the skill loads.
Targets scvi-tools 1.5.1, released September 10, 2026. APIs below were checked against the tagged source and official documentation. The examples are illustrative: the repository tests exercise short CPU synthetic fits for SCVI, SCANVI, TOTALVI, MULTIVI, METHYLVI, PEAKVI and POISSONVI, plus count-preserving preprocessing, DE output contracts, save/load and minification. The tested stack is Python 3.13, Scanpy 1.12.4, AnnData 0.13.4, MuData 0.4.1, PyTorch 2.14.1 and Lightning 2.6.6. Long training, hardware-specific paths and other specialized workflows remain illustrative; these checks do not establish convergence or biological validity.
| Task | Class | Required input |
|---|---|---|
| RNA integration | scvi.model.SCVI | Unnormalized RNA counts |
| RNA annotation | scvi.model.SCANVI | Counts and partial labels |
| CITE-seq | scvi.model.TOTALVI | RNA and protein counts, aligned cells |
| RNA + ATAC integration | scvi.model.MULTIVI | Registered MuData with aligned modality representation |
| ATAC accessibility | scvi.model.PEAKVI | Cells by peaks, binary/count accessibility |
| Quantitative ATAC | scvi.external.POISSONVI | Region-level fragment counts |
| Methylation | scvi.external.METHYLVI, METHYLANVI | MuData with methylated and total coverage counts |
| Cytometry | scvi.external.CYTOVI | Transformed protein intensities |
| Large cross-system effects | scvi.external.SysVI | Normalized, log-transformed expression |
| RNA velocity | scvi.external.VELOVI | Preprocessed spliced/unspliced abundances |
The count rule is model-specific: do not feed raw RNA counts to SysVI or replace methylation coverage with ratios. Likewise, never reconstruct counts by exponentiating log-normalized data. Preserve measured counts and preprocessing provenance.
Read the corresponding references before using a specialized model:
test cases passed, vs 13 / 22 without
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