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Scvi Tools

Checked
ovachiever·vlatest·data

Scvi Tools provides streamlined access to Sci-Kit Learn Vector Imputation (scvi) for enhanced biomedical data analysis and feature engineering.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add ovachiever/droid-tings --skill scvi-tools -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

ovachiever

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned ovachiever/droid-tings at 7acd12a754.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7acd12a754
Manifest
skills/scvi-tools/SKILL.md
Scanned
9 files, 3,380 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
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About this skill

What it does

The ovachiever-scvi-tools skill provides access to single-cell variational inference tools. It allows AI agents to perform data analysis and modeling tasks related to single-cell genomics, including dimensionality reduction, clustering, and differential expression analysis. This tool is particularly useful for researchers working with complex biological datasets.

When to use it

  • Analyzing scRNA-seq data to identify cell types and states.
  • Performing quality control on single-cell datasets.
  • Discovering gene signatures associated with specific cellular processes.
  • Building predictive models based on single-cell transcriptomic profiles.
  • Integrating single-cell data with other omics datasets.

Key capabilities

  • Dimensionality reduction (e.g., PCA, UMAP)
  • Clustering analysis
  • Differential expression analysis
  • Data quality control

Example prompts

  • "Perform dimensionality reduction on this scRNA-seq dataset and visualize the results."
  • "Cluster these single cells based on their gene expression profiles."
  • "Identify genes that are differentially expressed between these two cell populations."

Tips & gotchas

This skill requires a basic understanding of single-cell genomics concepts. Ensure your data is properly formatted according to scvi-tools' requirements for optimal results.