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 TianGzlab/OmicsClaw --skill scrnaseq-seurat-core-analysisgit clone --depth 1 https://github.com/TianGzlab/OmicsClawWrote 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/tiangzlab/omicsclaw/scrnaseq-seurat-core-analysis)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/scrnaseq-seurat-core-analysis"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/scrnaseq-seurat-core-analysis/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/tiangzlab/omicsclaw/scrnaseq-seurat-core-analysis"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/scrnaseq-seurat-core-analysis.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 | $0.00012 | $0.07644 |
| Opus 5 | $0.00006 | $0.03822 |
| Sonnet 5 | $0.00002 | $0.01529 |
| Haiku 4.5 | $0.00001 | $0.00764 |
Grade A, and why
Single-Cell RNA-seq Core Analysis (Seurat) 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 11d 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 — 607 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
When to Use This Skill
Use this skill when you need to:
- ✅ Analyze 10X Chromium data (CellRanger output, H5 files, raw/filtered matrices)
- ✅ Process Drop-seq, Smart-seq2, or inDrop single-cell RNA-seq data
- ✅ Perform complete QC workflow with adaptive thresholds and doublet detection
- ✅ Integrate multi-batch data using Harmony or Seurat CCA/RPCA
- ✅ Discover cell populations via graph-based clustering with validation
- ✅ Annotate cell types manually or with automated reference-based methods
- ✅ Compare conditions using pseudobulk differential expression (multi-sample data)
Don't use this skill for:
- ❌ Bulk RNA-seq data → Use bulk-rnaseq-counts-to-de-deseq2
- ❌ Python-based scRNA-seq analysis → Use scrnaseq-scanpy-core-analysis
- ❌ Spatial transcriptomics → Use spatial-transcriptomics-seurat (coming soon)
Key Concept: Single-cell RNA-seq captures individual cell transcriptomes, revealing cell type heterogeneity, rare populations, and cell states invisible to bulk methods. This workflow implements Seurat v5 best practices for robust, reproducible analysis.
Installation
Required Software
| Package | Version | License | Commercial Use | Installation |
|---|---|---|---|---|
| Seurat | ≥5.0 | MIT | ✅ Permitted | install.packages("Seurat") |
| ggplot2 | ≥3.4 | MIT | ✅ Permitted | install.packages("ggplot2") |
| ggprism | ≥1.0.4 | GPL-3 | ✅ Permitted | install.packages("ggprism") |
| dplyr | ≥1.0 | MIT | ✅ Permitted | install.packages("dplyr") |
| patchwork | ≥1.1 | MIT | ✅ Permitted | install.packages("patchwork") |
| DoubletFinder | ≥2.0.3 | CC0 | ✅ Permitted | install.packages("DoubletFinder") |
| harmony | ≥0.1.0 | GPL-3 | ✅ Permitted | install.packages("harmony") |
| SoupX | ≥1.5 | GPL-2 | ✅ Permitted | install.packages("SoupX") |
| DESeq2 | ≥1.36 | LGPL | ✅ Permitted | BiocManager::install("DESeq2") |
| muscat | ≥1.10 | GPL-3 | ✅ Permitted | BiocManager::install("muscat") |
| SingleR | ≥2.0 | GPL-3 | ✅ Permitted | BiocManager::install("SingleR") |
| celldex | ≥1.8 | GPL-3 | ✅ Permitted | BiocManager::install("celldex") |
What ships with it
31 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.
- references/ambient_rna_correction.md 9.9 KB
- references/common-patterns.md 16 KB
- references/decision-guide.md 12 KB
- references/integration_methods.md 20 KB
- references/marker_gene_database.md 9.4 KB
- references/pbmc3k_info.md 6.9 KB
- references/pseudobulk_de_guide.md 10 KB
- references/qc_guidelines.md 9.5 KB
- references/seurat_best_practices.md 10 KB
- references/troubleshooting_guide.md 10 KB
- references/workflow-details.md 17 KB
- scripts/annotate_celltypes.R 9.7 KB
- scripts/cluster_cells.R 7.1 KB
- scripts/export_results.R 10.0 KB
- scripts/filter_cells.R 19 KB
- scripts/find_markers.R 9.7 KB
- scripts/find_variable_features.R 3.2 KB
- scripts/fix_install.R 2.7 KB
- scripts/install_packages.R 2.3 KB
- scripts/integrate_batches.R 15 KB
- scripts/integration_diagnostics.R 17 KB
- scripts/load_example_data.R 2.8 KB
- scripts/normalize_data.R 5.9 KB
- scripts/plot_dimreduction.R 8.3 KB
- scripts/plot_qc.R 7.6 KB
- scripts/pseudobulk_de.R 9.1 KB
- scripts/qc_metrics.R 12 KB
- scripts/remove_ambient_rna.R 9.0 KB
- scripts/run_umap.R 3.4 KB
- scripts/scale_and_pca.R 9.4 KB
- scripts/setup_and_import.R 5.9 KB
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.
- 11d ago First seen · 607 lines · 12 tokens per session scan A 3a876fe95444
Single-Cell RNA-seq Core Analysis (Seurat) is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 12 tokens to every session and 7,644 once invoked, about $0.0001 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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