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-scanpy-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-scanpy-core-analysis)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/scrnaseq-scanpy-core-analysis"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/scrnaseq-scanpy-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-scanpy-core-analysis"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/scrnaseq-scanpy-core-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 121 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.04749 |
| Opus 5 | $0.00006 | $0.02374 |
| Sonnet 5 | $0.00002 | $0.00950 |
| Haiku 4.5 | $0.00001 | $0.00475 |
Grade A, and why
Single-Cell RNA-seq Core 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 10d 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 — 290 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
When to Use This Skill
- Analyze 10X Chromium data (CellRanger output, H5 files, raw/filtered matrices)
- Process Drop-seq, Smart-seq2, or inDrop single-cell RNA-seq data
- Integrate multi-batch data using scVI, scANVI, or Harmony
- Annotate cell types manually or with automated reference-based methods
- Compare conditions using pseudobulk differential expression (multi-sample data)
Don't use for: Bulk RNA-seq (use bulk-rnaseq-counts-to-de-deseq2), R-based scRNA-seq (use scrnaseq-seurat-core-analysis), Spatial transcriptomics (coming soon)
Installation
| Package | Version | License | Commercial Use | Installation |
|---|---|---|---|---|
| scanpy | ≥1.9 | BSD-3-Clause | Permitted | pip install scanpy |
| anndata | ≥0.8 | BSD-3-Clause | Permitted | pip install anndata |
| numpy | ≥1.20 | BSD-3-Clause | Permitted | pip install numpy |
| pandas | ≥1.3 | BSD-3-Clause | Permitted | pip install pandas |
| matplotlib | ≥3.4 | PSF | Permitted | pip install matplotlib |
| seaborn | ≥0.12 | BSD-3-Clause | Permitted | pip install seaborn |
| adjustText | ≥0.8 | MIT | Permitted | pip install adjustText |
| scrublet | ≥0.2.3 | MIT | Permitted | pip install scrublet |
| scvi-tools | ≥1.0 | BSD-3-Clause | Permitted | pip install scvi-tools |
| harmonypy | ≥0.0.9 | GPL-3 | Permitted | pip install harmonypy |
| celltypist | ≥1.0 | MIT | Permitted | pip install celltypist |
| pydeseq2 | ≥0.4 | MIT | Permitted | pip install pydeseq2 |
Install all: pip install scanpy anndata numpy pandas matplotlib seaborn adjustText scrublet
Minimum versions: Python ≥3.8, scanpy ≥1.9, anndata ≥0.8
Inputs
Required:
- Raw or filtered count matrix: CellRanger output (
filtered_feature_bc_matrix/), H5 files (.h5), AnnData (.h5ad), or count matrices (CSV/TSV)
What ships with it
28 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 18 KB
- references/integration_methods.md 21 KB
- references/marker_gene_database.md 9.4 KB
- references/pbmc3k_info.md 6.9 KB
- references/pseudobulk_de_guide.md 12 KB
- references/qc_guidelines.md 11 KB
- references/scanpy_best_practices.md 11 KB
- references/troubleshooting_guide.md 14 KB
- references/workflow-details.md 16 KB
- scripts/annotate_celltypes.py 17 KB runs code
- scripts/cluster_cells.py 8.4 KB runs code
- scripts/export_results.py 20 KB runs code
- scripts/filter_cells.py 20 KB runs code
- scripts/find_markers.py 11 KB runs code
- scripts/find_variable_genes.py 6.2 KB runs code
- scripts/integrate_scvi.py 24 KB runs code
- scripts/integration_diagnostics.py 22 KB runs code
- scripts/load_example_data.py 1.9 KB runs code
- scripts/normalize_data.py 9.5 KB runs code
- scripts/plot_dimreduction.py 11 KB runs code
- scripts/plot_qc.py 9.0 KB runs code
- scripts/pseudobulk_de.py 21 KB runs code
- scripts/qc_metrics.py 14 KB runs code
- scripts/remove_ambient_rna.py 10 KB runs code
- scripts/run_umap.py 4.7 KB runs code
- scripts/scale_and_pca.py 12 KB runs code
- scripts/setup_and_import.py 8.8 KB runs code
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.
- 10d ago First seen · 290 lines · 12 tokens per session scan A 908a6527b90d
Single-Cell RNA-seq Core Analysis (Scanpy) 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 4,749 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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