immunology-assays

immunology-assays is a skill for Claude Code, Codex from synthetic-sciences/openscience. It costs 79 tokens per session (6,000 once invoked), scanned A, original, Apache-2.0.

A computational toolkit for analyzing immunology experiment results, including gene regulation, antibody tests, cell images, tissue staining, and cytokine measurements.

In plain words
What is it for?
Use it to analyze ATAC-seq data, ELISA plates, immune-cell movement in time-lapse images, immunohistochemistry, antibody titers, cell-cycle timing, and multiplex cytokine assays.
Why use it?
It organizes common laboratory data-processing tasks and applies methods such as four-parameter curve fitting for ELISA results and scoring for tissue staining.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to analyze ATAC-seq data, ELISA plates, immune-cell movement in time-lapse…

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/synthetic-sciences/openscience/immunology-assays
About the project

synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.

synthetic-sciences/openscience · 3,493 stars · on GitHub · openscience.sh

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add synthetic-sciences/openscience --skill immunology-assays
Clone the repo
git clone --depth 1 https://github.com/synthetic-sciences/openscience

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for immunology-assays

README.md
[![agentmods](https://agentmods.dev/badge/skills/synthetic-sciences/openscience/immunology-assays.svg)](https://agentmods.dev/skills/synthetic-sciences/openscience/immunology-assays)
Your own site
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/immunology-assays"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/immunology-assays.svg" alt="Measured on agentmods" height="20"></a>
Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,000 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00079 $0.06000
Opus 5 $0.00039 $0.03000
Sonnet 5 $0.00016 $0.01200
Haiku 4.5 $0.00008 $0.00600

Measured 7d ago against content hash c93552657542, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

immunology-assays scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

The scan reads SKILL.md. This mod also ships 5 executable files (scripts/antibody_titer.py, scripts/atac_peaks.py, scripts/process_elisa.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

result = subprocess.run(cmd, capture_output=True, text=True)
backend/cli/skills/biology/immunology-assays/SKILL.md · 592 lines

How it starts

The opening of the file, as written. The whole thing — 592 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Immunology Assays: Experimental Data Analysis

Overview

Immunology Assays provides computational tools for analyzing data from common immunology experiments. This skill covers ATAC-seq differential accessibility analysis (MACS2 peak calling, motif enrichment), ELISA data processing with 4-parameter logistic standard curve fitting, immune cell tracking from time-lapse microscopy, immunohistochemistry (IHC) quantification with H-score calculation, antibody titer determination from serial dilution ELISA, cell cycle phase duration estimation from dual-nucleoside labeling, and multiplex cytokine assay data processing.

When to Use This Skill

  • Processing ELISA plate data with standard curve fitting (4PL)
  • Quantifying IHC staining intensity (H-score, positive pixel percentage)
  • Analyzing ATAC-seq peaks and differential chromatin accessibility
  • Tracking immune cell migration from microscopy time-lapse data
  • Determining antibody titers from serial dilution experiments
  • Estimating cell cycle phase durations from pulse-labeling data
  • Processing multiplex cytokine/chemokine assay data (Luminex, MSD)

Related Skills: For flow cytometry analysis use flow-cytometry-analysis. For single-cell RNA-seq use scanpy. For bioimage analysis use bioimage-analysis.

Installation

uv pip install scipy scikit-image opencv-python numpy pandas matplotlib

For ATAC-seq (optional):

# conda install -c bioconda macs2 homer

Quick Start

import numpy as np
from scipy.optimize import curve_fit

# 4-Parameter Logistic (4PL) for ELISA standard curve
def four_pl(x, a, b, c, d):
    """a=min, b=Hill slope, c=EC50, d=max"""
    return d + (a - d) / (1 + (x / c) ** b)

# Standard curve data
concentrations = np.array([0, 15.6, 31.25, 62.5, 125, 250, 500, 1000])
od_values = np.array([0.05, 0.12, 0.22, 0.45, 0.82, 1.35, 1.85, 2.15])

popt, pcov = curve_fit(four_pl, concentrations[1:], od_values[1:],
                       p0=[0.05, 1.0, 200, 2.2], maxfev=10000)
print(f"EC50: {popt[2]:.1f} pg/mL")
print(f"Dynamic range: {popt[0]:.3f} - {popt[3]:.3f} OD")

Read the full file on GitHub · 592 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 592 lines · 79 tokens per session scan A c93552657542

Subscribe to this mod's changes

immunology-assays is a skill published in the GitHub repository synthetic-sciences/openscience (3,493 stars, last pushed today), licensed Apache-2.0. It adds 79 tokens to every session and 6,000 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.