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Tooluniverse Proteomics Analysis

Checked
mims-harvard·vlatest·other·Apache-2.0

Analyzes proteomics data from various sources to identify proteins, modifications, and biological insights using advanced algorithms.

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 mims-harvard/tooluniverse --skill tooluniverse-proteomics-analysis -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

mims-harvard

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 mims-harvard/tooluniverse at f5c37eebeb.

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

Commit
f5c37eebeb
Manifest
skills/tooluniverse-proteomics-analysis/SKILL.md
Scanned
12 files, 1,581 lines
Copies in repo
3 — all of them scanned
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 enables AI agents to perform comprehensive proteomics analysis, facilitating the discovery and interpretation of protein data within biological research workflows. It streamlines complex bioinformatics tasks by automating specific analytical steps required for understanding protein structures and functions.

When to use it

  • Analyzing mass spectrometry data to identify and quantify proteins in a sample.
  • Investigating post-translational modifications to understand cellular signaling pathways.
  • Comparing proteomic profiles across different experimental conditions or disease states.
  • Integrating protein-level data with genomic information for systems biology studies.

Key capabilities

  • Execution of standard proteomics analysis pipelines.
  • Processing and interpretation of raw spectral data.
  • Identification of peptide sequences and protein matches.
  • Quantification of protein abundance levels.

Example prompts

  • "Run a full proteomics analysis pipeline on the attached mass spectrometry dataset."
  • "Identify all proteins present in this sample and list their relative abundances."
  • "Compare the protein expression profiles between the control group and the treatment group."

Tips & gotchas

Ensure your input data is pre-processed in a format compatible with standard proteomics tools before initiating analysis. Results may require manual validation by domain experts to confirm biological relevance.