Skip to content
TrustedSkillsbeta
Back to all skills

Scvi Tools

Official
anthropics·vlatest·other·Apache-2.0

Scvi Tools provides access to powerful Scvi models for predicting protein structure and function, accelerating scientific discovery.

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 anthropics/knowledge-work-plugins --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

anthropics

Matched to the vendor's own GitHub organisation.

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 anthropics/knowledge-work-plugins at da38ec1ee8.

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

Commit
da38ec1ee8
Manifest
bio-research/skills/scvi-tools/SKILL.md
Scanned
22 files, 6,853 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.
What each check looks for →

About this skill

What it does

The anthropics-scvi-tools skill provides access to Single-Cell Variational Inference (scVI) tools, a powerful framework for analyzing single-cell gene expression data. It enables tasks like denoising datasets, integrating multiple datasets, and predicting latent variables representing cellular states. This tool is specifically designed for life sciences applications involving complex biological data.

When to use it

  • Analyzing Single-Cell RNA Sequencing (scRNA-seq) Data: Use this skill when you need to process and understand scRNA-seq data from experiments.
  • Integrating Multiple Datasets: Combine datasets from different labs or experimental conditions for a more comprehensive analysis.
  • Identifying Novel Cell Types: Discover previously unknown cell types within a complex tissue sample by analyzing latent variables.
  • Predicting Cellular States: Infer the underlying state of cells based on their gene expression profiles.

Key capabilities

  • Denoising scRNA-seq datasets
  • Integrating multiple single-cell datasets
  • Prediction of latent variables representing cellular states
  • Single-Cell Variational Inference (scVI) framework support

Example prompts

  • "Can you denoise this scRNA-seq dataset using the scVI tools?"
  • "Integrate these two scRNA-seq datasets and identify shared cell populations."
  • "Predict the latent variables for each cell in this dataset to understand their cellular state."

Tips & gotchas

  • This skill requires a foundational understanding of single-cell genomics and scRNA-seq data analysis.
  • The computational demands of scVI can be significant; ensure sufficient resources are available.