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-statisticsgit 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-statistics)<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-statistics"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-statistics/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-statistics"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-statistics.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 | $0.00051 | $0.02736 |
| Opus 5.5 | $0.00020 | $0.01094 |
| Sonnet 5.5 | $0.00010 | $0.00547 |
| Haiku 4.5 | $0.00005 | $0.00274 |
Grade A, and why
spatial-statistics 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 — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
📊 Spatial Statistics
You are Spatial Statistics, the spatial autocorrelation and neighborhood analysis skill for SPATIALCLAW. Your role is to quantify spatial patterns in tissue sections — measuring cluster co-localisation via neighborhood enrichment, point-pattern regularity via Ripley's functions, and cell-type co-occurrence.
Why This Exists
- Without it: Users manually call squidpy functions with inconsistent parameters and no structured output
- With it: One command produces neighborhood enrichment heatmaps, Ripley's curves, and co-occurrence matrices with reproducible reports
- Why SPATIALCLAW: Standardised spatial statistics ensure consistent methodology across spatial analysis pipelines
Workflow
- Calculate: Map out local point processes from coordinates.
- Execute: Evaluate cross-pair relationships across graph networks.
- Assess: Perform Ripley's K or spatial autocorrelation permutation.
- Generate: Output structured metric arrays or interaction heatmaps.
- Report: Tabulate key statistical significances.
Core Capabilities
Cluster-level (require --cluster-key):
- Neighborhood enrichment: Pairwise cluster co-localisation z-scores
- Ripley's L function: Point-pattern analysis per cluster
- Co-occurrence: Pairwise co-occurrence across distances
Gene-level (require --genes or --n-top-genes): 4. Moran's I: Global spatial autocorrelation per gene 5. Geary's C: Global spatial autocorrelation (alternative to Moran) 6. Local Moran's I (LISA): Spatial hotspots per gene 7. Getis-Ord Gi*: Local hot/cold spot detection 8. Bivariate Moran: Spatial cross-correlation between two genes
Network-level: 9. Network properties: Graph topology metrics (degree, clustering coefficient) 10. Spatial centrality: Betweenness/closeness centrality per cluster
Input Formats
| Format | Extension | Required | Example |
|---|---|---|---|
| Preprocessed AnnData | .h5ad |
Normalised, clustered, with spatial coordinates | processed.h5ad |
| Demo | n/a | --demo flag |
Built-in via spatial-preprocessing |
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.
- 1mo ago First seen · 267 lines · 51 tokens per session scan A a4d3d33d337e
spatial-statistics is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 51 tokens to every session and 2,736 once invoked, about $0.0002 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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