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BrightGold70BrightGold70/skills/scvi-tools/SKILL.md

scvi-tools

Skill1 starsChanged 5 days agoNo licenceView on GitHub

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.

  • Installs packages

17 other projects use a version of this, 7 of them edited.

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Used with

Projects with it also use esmfold2, fair-esm2 and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • JimLiuSkill

    esmfold2

    Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release: masked-LM logits, hidden states, mutation scoring, contact prediction, and the SAE interpretability head. MIT-licensed weights on HuggingFace org `biohub`. Use this skill when: (1) Predicting complex structures with single-sequence input, (2) Validating designed binders with ESMFold2-Fast, (3) Running ESMFold2 with MSA input, (4) Getting ESMC embeddings or per-residue mutation scores, (5) Choosing kernel backend and sampling-step settings for paper-faithful throughput.

    Also in 3 projects that use this

  • JimLiuSkill

    fair-esm2

    Embed proteins with Meta AI's ESM-2 (`fair-esm` package). Use this skill when: (1) Extracting per-residue or per-sequence embeddings for downstream ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.

    Also in 3 projects that use this

  • naitySkill

    chai1

    Skill for biomolecular structure prediction with Chai-1 from Chai Discovery. Use this skill when a user wants to predict protein structures, protein-ligand complexes, protein-nucleic acid complexes, or multi-chain biomolecular assemblies. Also trigger when the user mentions Chai-1, chai-lab, or wants an AlphaFold3/Boltz-2 alternative that supports proteins, small molecules, RNA, and DNA. Chai-1 is Apache 2.0 licensed (commercial use allowed).

    Also in 3 projects that use this

  • naitySkill

    evo2

    Skill for genomic sequence modeling and design with Evo2 from Arc Institute. Use this skill when a user wants to model or generate DNA sequences, score variant effects at single-nucleotide resolution, extract genomic embeddings, analyze mutations in non-coding or coding regions, design synthetic genomic elements, compute positional entropy across a sequence, or work with genome-scale context (up to 1M base pairs). Also trigger when the user mentions Evo2, Evo 2, Arc Institute DNA model, genomic language models, DNA variant scoring, or genome-scale foundation models. NOTE: Evo2 is a DNA model — it is NOT a protein language model. For protein embeddings or protein variant scoring use ESM2, ESM-C, or ProtT5 instead.

    Also in 3 projects that use this

  • +8 more from JimLiu

The file

We split this skill into the rules it gives an agent

6 rules. 0 are common, 5 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 12 edited versions we read keep these.

  • 5

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 12 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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