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 zamushwani/biomedical-ai-skills --skill single-cell-atlasgit clone --depth 1 https://github.com/zamushwani/biomedical-ai-skillsWrote 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/zamushwani/biomedical-ai-skills/single-cell-atlas)<a href="https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/single-cell-atlas"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/single-cell-atlas/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/zamushwani/biomedical-ai-skills/single-cell-atlas"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/single-cell-atlas.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.10558 |
| Opus 5 | $0.00000 | $0.05279 |
| Sonnet 5 | $0.00000 | $0.02112 |
| Haiku 4.5 | $0.00000 | $0.01056 |
Grade A, and why
single-cell-atlas 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 — 1,141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Single-Cell Atlas Construction
Full single-cell RNA-seq pipeline from raw counts to biological interpretation. Covers QC, normalization, batch integration, clustering, annotation, pseudobulk DE, trajectory inference, cell-cell communication, and TF activity. Dual-language: Seurat v5 (R) and scanpy (Python).
When to Use This Skill
Activate when the user requests:
- Single-cell RNA-seq quality control and preprocessing
- 10x Chromium data processing (Cell Ranger output)
- Doublet detection and removal
- Normalization of UMI count matrices
- Feature selection for dimensionality reduction
- Processing scRNA-seq data before integration, clustering, or annotation
- Batch integration across samples or experiments
- Clustering and resolution selection
- Automated or marker-based cell type annotation
- UMAP visualization
- Differential expression between conditions (pseudobulk)
- Trajectory inference and RNA velocity
- Cell-cell communication analysis
- Transcription factor activity and gene regulatory networks
Inputs
| Data Type | Format | Source |
|---|---|---|
| Count matrix | 10x HDF5 (.h5), MEX (matrix.mtx + barcodes + features) |
Cell Ranger, STARsolo |
| Count matrix | AnnData (.h5ad) for scanpy, Seurat object (.rds) for R |
Processed datasets |
| Reference | PBMC 3k, Tabula Muris, HCA datasets | 10x Genomics, HCA |
Loading Data
Seurat v5 (R)
library(Seurat) # v5.4+
# From Cell Ranger output (filtered_feature_bc_matrix/)
obj <- Read10X("path/to/filtered_feature_bc_matrix/") |>
CreateSeuratObject(project = "sample1", min.cells = 3, min.features = 200)
# From HDF5
obj <- Read10X_h5("path/to/filtered_feature_bc_matrix.h5") |>
CreateSeuratObject(project = "sample1", min.cells = 3, min.features = 200)
# Seurat v5 uses Assay5 (layers: counts, data, scale.data)
# Access counts: obj[["RNA"]]$counts or LayerData(obj, layer = "counts")
scanpy (Python)
import scanpy as sc # v1.12+
# From Cell Ranger output
adata = sc.read_10x_mtx("path/to/filtered_feature_bc_matrix/", var_names="gene_symbols")
# From HDF5
adata = sc.read_10x_h5("path/to/filtered_feature_bc_matrix.h5")
# Store raw counts for later
adata.raw = adata.copy()
What ships with it
6 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.
- 12d ago First seen · 1,141 lines · 0 tokens per session scan A 17d968ae3c39
single-cell-atlas is a skill published in the GitHub repository zamushwani/biomedical-ai-skills (1 stars, last pushed 13d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 10,558 tokens. 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 2026-08-31.
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