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 spatial-transcriptomicsgit 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/spatial-transcriptomics)<a href="https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/spatial-transcriptomics"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/spatial-transcriptomics/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/spatial-transcriptomics"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/spatial-transcriptomics.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 | $0.11824 |
| Opus 5 | $0.00000 | $0.05912 |
| Sonnet 5 | $0.00000 | $0.02365 |
| Haiku 4.5 | $0.00000 | $0.01182 |
Grade A, and why
spatial-transcriptomics 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.
How it starts
The opening of the file, as written. The whole thing — 1,080 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spatial Transcriptomics
Processing and analysis of spatially resolved transcriptomics across sequencing-based (Visium, Visium HD, Slide-seq, Stereo-seq) and imaging-based (Xenium, MERSCOPE, CosMx) platforms. Covers loading, QC, normalization, and spatially variable gene detection. Dual-language: Python (squidpy/SpatialData) and R (SpatialExperiment/Seurat v5).
When to Use This Skill
Activate when the user requests:
- Visium or Visium HD data loading and quality control
- Xenium, MERSCOPE, CosMx, or Stereo-seq processing
- Spatial quality control and artifact detection
- Spatially variable gene (SVG) detection
- Spatial autocorrelation (Moran's I, Geary's C)
- Conversion between spatial data containers
- Any analysis where transcript measurements carry coordinates
Platform Types
The single most important distinction. It determines every downstream choice.
Sequencing-based (spot/bin resolution, whole transcriptome):
Visium 55 um spots, ~1-10 cells per spot, ~18k genes
Visium HD 2 um bins (binned to 8/16 um), subcellular grid, whole transcriptome
Slide-seq V2 10 um beads
Stereo-seq 220 nm DNB, binned
-> Spots mix cell types. You need DECONVOLUTION.
Imaging-based (single-cell/subcellular, targeted panel):
Xenium ~5k genes max (Prime 5K), segmented cells
MERSCOPE ~500-1000 genes
CosMx ~1000-6000 genes
-> Cells are already segmented. You need CELL TYPING, not deconvolution.
Segmentation quality is the dominant error source.
Inputs
| Platform | Format | Reader |
|---|---|---|
| Visium | Space Ranger outs/ (h5 + tissue_positions.csv + images) |
squidpy.read.visium, VisiumIO::TENxVisium |
| Visium HD | Space Ranger outs/ with binned_outputs/, tissue_positions.parquet |
spatialdata_io.visium_hd, VisiumIO::TENxVisiumHD |
| Xenium | Xenium Onboard Analysis output bundle | spatialdata_io.xenium, XeniumIO::TENxXenium |
| MERSCOPE | Vizgen output (cell_by_gene.csv, cell_metadata.csv) |
spatialdata_io.merscope |
| CosMx | NanoString flat files | spatialdata_io.cosmx, readCosmxSXE |
| Stereo-seq | STOmics GEF/GEM (v7.x only) | spatialdata_io.stereoseq |
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.
- 9d ago First seen · 1,080 lines · 0 tokens per session scan A e8d118807737
spatial-transcriptomics is a skill published in the GitHub repository zamushwani/biomedical-ai-skills (1 stars, last pushed 10d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 11,824 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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