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Bio Flow Cytometry Compensation Transformation

Checked
gptomics·vlatest·data·MIT

This skill automatically corrects for spectral overlap in flow cytometry data, ensuring accurate quantification of cell populations.

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 gptomics/bioskills --skill bio-flow-cytometry-compensation-transformation --full-depth -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

gptomics

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 gptomics/bioskills at d91ed3d563.

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

Commit
d91ed3d563
Manifest
flow-cytometry/compensation-transformation/SKILL.md
Scanned
3 files, 246 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

This skill performs flow cytometry data compensation and transformation. It corrects for spectral overlap between fluorescent channels, allowing for accurate analysis of protein expression levels. The tool can also perform mathematical transformations (e.g., arcsinh) to improve data visualization and statistical analysis.

When to use it

  • Analyzing fluorescence-activated cell sorting (FACS) data where multiple fluorochromes are used simultaneously.
  • Preparing flow cytometry datasets for downstream statistical analyses or machine learning models.
  • Standardizing flow cytometry data across different experiments or laboratories.
  • Generating publication-quality figures from flow cytometry results.

Key capabilities

  • Compensation matrix calculation
  • Spectral overlap correction
  • Mathematical transformations (e.g., arcsinh)
  • Data normalization

Example prompts

  • "Can you compensate this FCS file using the provided compensation matrix?"
  • "Transform these flow cytometry data with an arcsinh transformation."
  • “Apply a compensation matrix and then perform a logicle transformation on this dataset.”

Tips & gotchas

The accuracy of compensation relies heavily on accurate experimental controls (single-stained samples). Ensure your compensation matrix is properly generated for optimal results.