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 agentmods add skills/tiangzlab/omicsclaw/sc-clusteringnpx skills add TianGzlab/OmicsClaw --skill sc-clusteringgit 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/sc-clustering)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-clustering"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-clustering.svg" alt="Measured on agentmods" 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 | $0.00073 | $0.01413 |
| Opus 5 | $0.00036 | $0.00707 |
| Sonnet 5 | $0.00015 | $0.00283 |
| Haiku 4.5 | $0.00007 | $0.00141 |
Grade A, and why
sc-clustering 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 2d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sc-clustering
When to use
The user has a normalised AnnData with PCA / integrated embedding
already populated and wants the standard scRNA neighbour-graph →
embedding → cluster workflow. Combinable in one call: pick an
embedding method (umap default, also tsne / diffmap / phate)
and a clustering method (leiden default, also louvain), with an
explicit resolution or auto-resolution search. Designed to read from
obsm["X_pca"] / obsm["X_harmony"] / etc. via --use-rep.
Inputs & Outputs
Inputs
- Modalities: scrna
- File types:
.h5ad - Requires a preprocessed AnnData (
Xnormalised, PCA/neighbours present) - Expects
obsm:X_pca
Outputs
tables/cell_metadata.csvtables/cluster_qc_summary.csvtables/cluster_summary.csvtables/clustering_summary.csvtables/embedding_points.csvfigures/auto_resolution_search.pngfigures/cluster_qc_heatmap.pngfigures/cluster_size_summary.pngfigures/embedding_clusters.pngfigures/embedding_comparison.pngfigures/pca_scatter.pngfigures/pca_variance.pngfigures/r_cell_barplot.pngfigures/r_cell_proportion.pngfigures/r_embedding_discrete.pngfigures/r_embedding_feature.pnganalysis_summary.txtprocessed.h5adreport.mdresult.json- Processed AnnData (
saves_h5ad) — addsobs:leiden,louvain;obsm:X_<embedding>
Flow
- Load AnnData; pick embedding source (
--use-repor default). - If
--resolutionisauto, run the auto-resolution search and writefigures/auto_resolution_search.png; otherwise parse it as a single float. - Build the neighbour graph (
--n-neighbors×--n-pcs). - Compute the chosen
--embedding-methodlow-dim embedding. - Cluster with
--cluster-methodat the chosen--resolution. - Render the embedding gallery + cluster-summary tables; emit
report.md+result.json.
What ships with it
8 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.
- 2d ago First seen · 123 lines · 73 tokens per session scan A 3ab5ebe80132
sc-clustering is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 1,413 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 2026-09-03.
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…
spatial-domain-identification
Identify tissue regions and spatial niches from preprocessed spatial transcriptomics data using Leiden, Louvain, SpaGCN, STAGATE, GraphST, or BANKSY.
Virtual Embryo — atlas data + knowledge graph
Query the Virtual Embryo knowledge graph (mouse/human developmental biology: genes, anatomy, Theiler/Carnegie stages, gene expression, diseases, papers) and its 3D atlas catalog (anatomical OPT/light-sheet volumes + 3D spatial- transcriptomics datasets), and visualise those datasets in 3D with the volume3d / spatial3d…
pkpd-modeling
Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when…
neuropixels-analysis
Analyze Neuropixels extracellular recordings end-to-end with SpikeInterface. Covers loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift/motion correction, Kilosort4 (and CPU) spike sorting, quality metrics, and unit curation (threshold-based, model-based UnitRefine, and AI-assisted visual review). Use when…
onekgpd
Query the 1000 Genomes Project dataset (3,202 whole-genome-sequenced individuals, GRCh38) at the level of individual participants. Use when a question is about individuals or variants in the 1000 Genomes Project cohort: which individuals carry variants matching specific criteria in a gene or region, which individuals…