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 GPTomics/bioSkills --skill single-cell-atacgit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/single-cell-atac)<a href="https://agentmods.dev/skills/gptomics/bioskills/single-cell-atac"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/single-cell-atac/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/gptomics/bioskills/single-cell-atac"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/single-cell-atac.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 | $0.00123 | $0.06290 |
| Opus 5 | $0.00062 | $0.03145 |
| Sonnet 5 | $0.00025 | $0.01258 |
| Haiku 4.5 | $0.00012 | $0.00629 |
Grade A, and why
bio-atac-seq-single-cell-atac 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 9d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-atac-seq-single-cell-atac — 98% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: Cell Ranger ATAC 2.1+, Signac 1.13+, Seurat 5.0+, ArchR 1.0.2+, SnapATAC2 2.8+, AMULET 1.1+, scDblFinder 1.16+, scater 1.30+, scvi-tools 1.1+, GenomicRanges 1.54+, JASPAR2024 0.99+, BSgenome.Hsapiens.UCSC.hg38 1.4+, EnsDb.Hsapiens.v86 2.99+, MACS3 3.0+. SnapATAC2 2.8+ uses pp.import_fragments; older 2.5-2.7 used pp.import_data (renamed/removed in 2.9).
Verify before use:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_nameto verify parameters - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
Single-Cell ATAC-seq
"Process my 10X scATAC data from cellranger output" -> Build a per-cell fragment matrix, compute per-cell QC, dimensionality reduction (TF-IDF + LSI / spectral / autoencoder), cluster, call cluster-level pseudobulk peaks, annotate cell types via gene-activity scores, and integrate with paired scRNA-seq if Multiome.
- R:
Signac::CreateChromatinAssay()->Seuratworkflow (TF-IDF + SVD + UMAP + Leiden) - R:
ArchR::createArrowFiles()-> ArchR project (TileMatrix + LSI + UMAP) - Python:
snapatac2.pp.import_fragments()-> SnapATAC2 (spectral / diffusion-map clustering) - CLI (preprocessing):
cellranger-atac count(10X) orchromap(alignment-only fragment files)
Ecosystem Choice (The Most Important Decision)
| Ecosystem | Language | Strength | Fails when | Best for |
|---|---|---|---|---|
| Signac (Stuart 2021) | R, Seurat-based | Tightest scRNA-seq integration; Seurat ecosystem mature | Memory hungry on >100K cells; slower than ArchR | Multiome RNA+ATAC; small-to-medium datasets; Seurat user |
| ArchR (Granja 2021) | R, Arrow/HDF5 | Memory-efficient (Arrow files); fast on 100K-1M cells; built-in trajectory + doublet | Less RNA-seq integration; ArchR-specific format | Large bulk-cohort scATAC; trajectory analysis; ATAC-only |
| SnapATAC2 (Zhang 2024) | Python, AnnData | Memory-efficient; modern Python ecosystem; spectral clustering performant | Newer; benchmarks evolving; ecosystem smaller than R | Python-first labs; very large datasets (>1M cells) |
| Cell Ranger ATAC | CLI (10X-specific) | 10X official preprocessing | Closed; fixed pipeline | Only as preprocessing step; analysis happens elsewhere |
| scATAC-pro | CLI-based pipeline | Alternative preprocessing | Less maintained | Legacy; not recommended for new projects |
What ships with it
2 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.
- 9d ago First seen · 405 lines · 123 tokens per session scan A 80bbbe0d6175
bio-atac-seq-single-cell-atac is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 24d ago), licensed MIT. It adds 123 tokens to every session and 6,290 once invoked, about $0.0006 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.
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