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 ShangBioLab/SpatialClaw --skill spatial-cnvgit 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-cnv)<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-cnv"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-cnv/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/shangbiolab/spatialclaw/spatial-cnv"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-cnv.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 | $0.00023 | $0.00782 |
| Opus 5.5 | $0.00009 | $0.00313 |
| Sonnet 5.5 | $0.00005 | $0.00156 |
| Haiku 4.5 | $0.00002 | $0.00078 |
Grade A, and why
spatial-cnv 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 1mo 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spatial CNV
Spatial CNV infers large-scale chromosomal gains and losses from spatial transcriptomics expression patterns.
Core Capability
- inferCNVpy: Expression-based CNV inference using
adata.Xas log-normalized expression. - Spatial CNV mapping: Overlay CNV scores on spatial coordinates.
- Reproducible reporting: Write
report.md,result.json, processed AnnData, and figures.
Input
| Format | Extension | Required Fields |
|---|---|---|
| AnnData | .h5ad |
X log-normalized expression, gene positions in var; obsm["spatial"] for spatial plots |
CLI
python skills/spatial/spatial-cnv/spatial_cnv.py \
--input <preprocessed.h5ad> \
--method infercnvpy \
--reference-key cell_type \
--reference-cat Normal \
--output <dir>
python skills/spatial/spatial-cnv/spatial_cnv.py --demo --output <dir>
spatialclaw run spatial-cnv --input <file.h5ad> --output <dir>
Parameters
| Parameter | Default | Description |
|---|---|---|
--input |
- | Preprocessed spatial .h5ad |
--output |
required | Output directory |
--demo |
off | Run built-in demo |
--method |
infercnvpy |
CNV backend |
--reference-key |
cell_type |
.obs column identifying reference categories |
--reference-cat |
- | One or more reference categories |
--window-size |
100 |
Genomic smoothing window size |
--step |
10 |
Genomic smoothing step |
Method
inferCNVpy subtracts reference expression in log-space and smooths expression along ordered genomic windows. Genes must have chromosome/start/end annotations in adata.var.
Output
output_directory/
├── report.md
├── result.json
├── processed.h5ad
└── figures/
├── cnv_heatmap.png
└── cnv_spatial.png
Dependencies
Required Python packages:
scanpy
Optional Python package:
infercnvpy
Safety
- Local-first processing.
- Reports include SPATIALCLAW disclaimers.
- Parameters and outputs are recorded for reproducibility.
What ships with it
2 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.
- 1mo ago First seen · 111 lines · 23 tokens per session scan A 0455ca416da2
spatial-cnv is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 23 tokens to every session and 782 once invoked, about $0.0001 per session on Opus 5.5. 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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