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.
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.
npx skills add synthetic-sciences/openscience --skill immunology-assaysgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/immunology-assays)<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>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.
| Model | Per session | Once 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 |
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.
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) 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")
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.
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.
- 7d ago First seen · 592 lines · 79 tokens per session scan A c93552657542
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.
Other skills, from other repositories
songsee
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
arxiv
Search arXiv papers by keyword, author, category, or ID.
research-paper-writing
Write ML papers for NeurIPS/ICML/ICLR: design→submit.
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-plot-stub
Plot a results CSV (x, ybaseline, yours) as a two-line matplotlib chart and write a PDF. Demo-only.
google-workspace-setup
One-time setup for gws: install, OAuth, scopes, auto-approve.