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/shangbiolab/spatialclaw/spatial-svg-detectionnpx skills add ShangBioLab/SpatialClaw --skill spatial-svg-detectiongit clone --depth 1 https://github.com/ShangBioLab/SpatialClawWrote 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/shangbiolab/spatialclaw/spatial-svg-detection)<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-svg-detection"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-svg-detection.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 | $0.00026 | $0.00822 |
| Opus 5 | $0.00013 | $0.00411 |
| Sonnet 5 | $0.00005 | $0.00164 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
spatial-svg-detection 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 6d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spatial SVG Detection
This skill identifies genes whose expression varies non-randomly across tissue coordinates.
Methods
| Method | Input Matrix | Notes |
|---|---|---|
morans |
adata.X log-normalized expression |
Squidpy Moran's I spatial autocorrelation |
spatialde |
adata.layers["counts"] raw counts when available |
NaiveDE stabilization followed by SpatialDE |
flashs |
adata.layers["counts"] raw counts when available |
Python-native randomized kernel approximation |
CLI
python skills/spatial/spatial-svg-detection/spatial_svg_detection.py \
--input <processed.h5ad> \
--method morans \
--n-top-genes 20 \
--output <dir>
python skills/spatial/spatial-svg-detection/spatial_svg_detection.py --demo --output <dir>
spatialclaw run spatial-svg-detection --input <file.h5ad> --output <dir>
Allowed --method values: morans, spatialde, flashs.
Parameters
| Parameter | Default | Description |
|---|---|---|
--input |
- | Processed spatial .h5ad |
--output |
required | Output directory |
--demo |
off | Run built-in demo |
--method |
morans |
morans, spatialde, or flashs |
--n-top-genes |
20 |
Number of top SVGs to report/plot |
--fdr-threshold |
0.05 |
FDR cutoff for significance |
Output
output_dir/
├── report.md
├── result.json
├── processed.h5ad
├── figures/
│ └── top_svg_spatial.png
└── tables/
└── svg_results.csv
Dependencies
Required Python packages:
scanpysquidpymatplotlibnumpypandas
Optional Python packages:
SpatialDENaiveDEflashs
Safety
- Local-first processing.
- Reports include SPATIALCLAW disclaimers.
- Results are stored in
adata.uns; original data are preserved in the output copy.
Citations
What ships with it
3 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.
- 6d ago First seen · 117 lines · 26 tokens per session scan A 2dd3688ed224
spatial-svg-detection is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 822 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.
Other skills, from other repositories
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End-to-end workflow for gene panel design in scRNA-seq and spatial transcriptomics, that should be STRICTLY followed: dataset understanding + smart downsampling + train/test splits, algorithmic selection (HVG/DE/RF/scGeneFit/SpaPROS), optimal sub-panel discovery (ARI vs size), biological completion with a stability…
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Spatial Omics Skills Index
Skills for spatial transcriptomics analysis including single-cell to spatial mapping (MOSCOT), 3D visualization (PyVista), and related spatial workflows.
consensus-interpret
Load when biologically interpreting a finished verified consensus run (consensus-domains / sc-consensus-clustering) — inline DE, marker-DB lookup, and LLM cell-type naming with mandatory marker citations + evidence-bound next-step recommendations. Skip when the consensus run failed (fix it first); forward query→skill…
spatial-annotate
Load when assigning per-spot cell-type labels on a spatial AnnData via marker-gene scoring or scRNA-reference mapping (Tangram / scANVI / CellAssign). Skip when computing spot-level cell-type proportions for multi-cell-per-spot platforms (use spatial-deconv); tissue-domain detection (use spatial-domains).