Tooluniverse Immune Repertoire Analysis

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by mims-harvard · vlatest · Repository

Analyzes immune repertoires from diverse datasets to identify biomarkers and therapeutic targets using advanced AI modeling.

Install on your platform

We auto-selected Claude Code based on this skill’s supported platforms.

1

Run in terminal (recommended)

terminal
claude mcp add tooluniverse-immune-repertoire-analysis npx -- -y @trustedskills/tooluniverse-immune-repertoire-analysis
2

Or manually add to ~/.claude/settings.json

~/.claude/settings.json
{
  "mcpServers": {
    "tooluniverse-immune-repertoire-analysis": {
      "command": "npx",
      "args": [
        "-y",
        "@trustedskills/tooluniverse-immune-repertoire-analysis"
      ]
    }
  }
}

Requires Claude Code (claude CLI). Run claude --version to verify your install.

About This Skill

The tooluniverse-immune-repertoire-analysis skill enables AI agents to analyze immune system repertoires by processing complex biological datasets. It facilitates the discovery of specific antibody or T-cell receptor sequences and their associated functional properties within immunological studies.

When to use it

  • Investigating personalized vaccine responses based on individual patient immune profiles.
  • Identifying rare antigen-specific B-cell clones in autoimmune disease research.
  • Analyzing high-throughput sequencing data from single-cell RNA-seq experiments.
  • Mapping epitope-binding specificities across diverse human populations.

Key capabilities

  • Processes large-scale immunosequencing datasets to extract repertoire metrics.
  • Identifies clonal expansions and contraction patterns within immune responses.
  • Correlates sequence diversity with functional outcomes in experimental conditions.
  • Supports analysis of both B-cell receptor (BCR) and T-cell receptor (TCR) repertoires.

Example prompts

  • "Analyze the provided V(D)J sequencing data to identify dominant clonal families in this patient sample."
  • "Compare immune repertoire diversity between control and treated groups using statistical metrics."
  • "Extract unique CDR3 sequences from the dataset and predict their potential antigen binding specificities."

Tips & gotchas

Ensure input datasets are properly formatted for immunosequencing analysis to avoid parsing errors. This tool is specialized for biological data; do not use it for general text processing or non-immunological tasks.

Tags

🛡️

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Details

Version
vlatest
License
Author
mims-harvard
Installs
83

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Passed automated security scans.