OpenBioMed is an agent platform and toolkit collection for biomedical research and drug discovery, covering areas such as molecular design, protein analysis, and single-cell data analysis. It is intended for researchers and provides the biomedical skills listed in the catalogue as workflows for Claude Code.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/PharMolix/OpenBioMednpx agentmods add skills/pharmolix/openbiomed/single-cell-scrna-seq-analysis-scanpyWrote 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/pharmolix/openbiomed/single-cell-scrna-seq-analysis-scanpy)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-scrna-seq-analysis-scanpy"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-scrna-seq-analysis-scanpy/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pharmolix/openbiomed/single-cell-scrna-seq-analysis-scanpy"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/single-cell-scrna-seq-analysis-scanpy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00085 | $0.02861 |
| Opus 5 | $0.00043 | $0.01430 |
| Sonnet 5 | $0.00017 | $0.00572 |
| Haiku 4.5 | $0.00009 | $0.00286 |
Grade A, and why
single-cell-scrna-seq-analysis-scanpy scanned grade A with 0 findings 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 12d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 361 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scanpy Single-Cell Analysis
Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.
What it does
- Loads diverse data formats: Ingests 10X Genomics (
.h5,.mtx),.h5ad, or.csvfiles into the AnnData structure. - Automates Quality Control (QC): Identifies mitochondrial genes, calculates QC metrics, and filters low-quality cells/genes.
- Preprocesses and Normalizes: Performs log-transformation, total-count normalization (typically 10k/cell), and highly variable gene (HVG) selection, safely backing up raw counts.
- Reduces Dimensionality: Computes PCA and builds neighborhood graphs, followed by UMAP or t-SNE embeddings.
- Clusters Cells: Applies Leiden community detection across multiple resolutions to find optimal granularities.
- Identifies Marker Genes: Uses statistical testing (Wilcoxon) to rank genes driving cluster identity, enabling manual or automated cell type annotation.
- Generates Publication-Ready Plots: Produces highly customized, high-DPI violin plots, UMAPs, heatmaps, and dot plots.
Why this exists
This skill encodes the current best practices for scRNA-seq:
- Enforces strict tracking of metadata (
adata.obsandadata.var). - Utilizes the more robust Leiden algorithm over Louvain.
- Bundles automated, reproducible scripts for both QC and full-pipeline analysis.
- Provides immediate access to trajectory inference (PAGA/DPT) and gene set scoring.
Usage
Quick Start
Basic Import and Setup
import scanpy as sc
import pandas as pd
import numpy as np
# Configure settings
sc.settings.verbosity = 3
sc.settings.set_figure_params(dpi=80, facecolor='white')
sc.settings.figdir = './figures/'
Loading Data
# From 10X Genomics
adata = sc.read_10x_mtx('path/to/data/')
adata = sc.read_10x_h5('path/to/data.h5')
# From h5ad (AnnData format)
adata = sc.read_h5ad('path/to/data.h5ad')
# From CSV
adata = sc.read_csv('path/to/data.csv')
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.
- 12d ago First seen · 361 lines · 85 tokens per session scan A d66a2af94935
single-cell-scrna-seq-analysis-scanpy is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 85 tokens to every session and 2,861 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
single-cell
Single-cell analysis pipeline covering scRNA-seq, snRNA-seq, and CyTOF (mass cytometry) — QC, normalization, integration, clustering, annotation, differential expression, trajectory, cell communication, and TF activity inference.
Bulk RNAseq differential expression (DeSeq2)
Core DESeq2 workflow for RNA-seq differential expression analysis with count data.
Single-Cell RNA-seq Core Analysis (Seurat)
Complete workflow for single-cell RNA-seq analysis using Seurat v5. Process raw data through quality control, normalization, clustering, and cell type annotation with publication-ready visualizations.
Pooled CRISPR Screen Analysis
Analyze pooled CRISPR screens with single-cell RNA-seq readout using a tiered workflow: fast screening → target validation → rigorous differential expression.
Single-Cell RNA-seq Core Analysis (Scanpy)
Complete workflow for single-cell RNA-seq analysis using Scanpy and the scverse ecosystem. Process raw data through quality control, normalization, clustering, and cell type annotation with publication-ready visualizations.