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Scvi Tools

Checked
jackspace·vlatest·ai-ml·MIT

Scvi Tools provides streamlined access to SCVI embeddings and related analysis for single-cell data, accelerating research insights.

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 jackspace/claudeskillz --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

jackspace

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Install target

Live repository

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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

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  • No credential access: passed

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  • Pinned to a commit: passed

    Scanned jackspace/claudeskillz at aef4e299d1.

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Commit
aef4e299d1
Manifest
skills/scientific-pkg-scvi-tools/SKILL.md
Scanned
2 files, 204 lines
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About this skill

What it does

This skill provides access to scvi-tools, a Python library designed for single-cell variational inference. It enables users to perform tasks like data integration, batch correction, and dimensionality reduction on single-cell datasets using state-of-the-art machine learning techniques. Specifically, it allows for the construction of latent spaces from complex biological data.

When to use it

  • Analyzing single-cell RNA sequencing (scRNA-seq) data to identify cell types and their relationships.
  • Integrating multiple scRNA-seq datasets generated at different times or in different labs.
  • Performing batch correction to remove technical variation between experiments.
  • Reducing the dimensionality of high-dimensional single-cell data for visualization and downstream analysis.
  • Building predictive models based on single-cell gene expression profiles.

Key capabilities

  • Data integration
  • Batch correction
  • Dimensionality reduction
  • Single-cell variational inference
  • Latent space construction

Example prompts

  • "Integrate these two scRNA-seq datasets using scvi-tools."
  • "Perform batch correction on this single-cell data with the scvi-tools library."
  • "Reduce the dimensionality of this dataset and visualize it in a 2D latent space using scvi-tools."

Tips & gotchas

  • Requires familiarity with Python and basic bioinformatics concepts.
  • scvi-tools can be computationally intensive, especially for large datasets. Ensure sufficient computational resources are available.