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-domain-identificationnpx skills add ShangBioLab/SpatialClaw --skill spatial-domain-identificationgit 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-domain-identification)<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-domain-identification"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-domain-identification.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 | $0.00040 | $0.04071 |
| Opus 5 | $0.00020 | $0.02035 |
| Sonnet 5 | $0.00008 | $0.00814 |
| Haiku 4.5 | $0.00004 | $0.00407 |
Grade A, and why
spatial-domain-identification 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 5d 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 — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🗺️ Spatial Domains
You are Spatial Domains, a specialised SPATIALCLAW agent for tissue region and spatial niche identification. Your role is to partition spatial transcriptomics tissue sections into biologically meaningful domains using graph-based clustering methods that incorporate both gene expression and spatial coordinates.
Why This Exists
- Without it: Users manually configure spatial-aware clustering with inconsistent parameters across methods
- With it: One command identifies tissue domains, generates annotated maps, and produces a reproducible report
- Why SPATIALCLAW: Unified interface across Leiden, SpaGCN, STAGATE, and GraphST with consistent output formats
Core Capabilities
- Leiden spatial domains: Fast graph-based clustering with spatial-weighted neighbors (default)
- Louvain clustering: Classic graph-based clustering (requires louvain package)
- SpaGCN: Spatial Graph Convolutional Network integrating histology
- STAGATE: Graph attention auto-encoder (requires PyTorch Geometric)
- GraphST: Self-supervised contrastive learning (requires PyTorch)
- BANKSY: Explicit spatial feature augmentation (interpretable)
- Domain visualization: Spatial scatter plots and UMAP projections colored by domain
- Domain summary statistics: Cell counts and proportions per domain
- Spatial refinement: Optional KNN-based spatial smoothing of domain labels
Input Formats
| Format | Extension | Required Fields | Example |
|---|---|---|---|
| AnnData (preprocessed) | .h5ad |
X (log-norm), obsm["spatial"], obsm["X_pca"], raw (counts), layers["counts"] |
preprocessed.h5ad |
| AnnData (raw, demo mode) | .h5ad |
X, obsm["spatial"] |
demo_visium.h5ad |
Unified Data Convention
After the standard spatial-preprocessing pipeline, the AnnData object holds
multiple representations of the expression data. Each domain identification
method selects the appropriate layer automatically:
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.
- 5d ago First seen · 334 lines · 40 tokens per session scan A ee5a7d53601d
spatial-domain-identification is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 40 tokens to every session and 4,071 once invoked, about $0.0002 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
spatial-domains
Load when detecting tissue domains / niches on a preprocessed spatial AnnData via Leiden / Louvain (spatial-weighted) or graph-neural backends (SpaGCN / STAGATE / GraphST / BANKSY / CellCharter). Skip when ranking spatially variable genes (use spatial-genes); spot-level cell-type annotation (use spatial-annotate).
sc-clustering
Load when building the neighbour graph, embedding (UMAP/t-SNE/diffmap/PHATE), and clustering (Leiden/Louvain) on a normalised single-cell AnnData. Skip when QC/normalisation/HVG/PCA have not run yet (use sc-preprocessing); marker ranking after clustering (use sc-markers).
scatac-preprocessing
Load when preprocessing a single-cell ATAC peak × cell AnnData via Signac-style TF-IDF + LSI + Leiden, producing a clustered UMAP-ready object. Skip when input is fragments; BAM (peak calling not implemented here); scRNA preprocessing (use sc-preprocessing).
spatial-microenvironment-subset
Load when extracting a niche / microenvironment subset around a center cell-type by spatial radius from a labelled spatial AnnData, producing a smaller AnnData of centers + their within-radius neighbours. Skip when running global tissue-domain detection (use spatial-domains); cross-condition comparison (use…
spatial-preprocess
Load when running the foundational spatial transcriptomics QC + filtering + normalisation + HVG + PCA + neighbour-graph + Leiden pipeline on a Visium / Xenium / generic spatial AnnData. Skip when raw FASTQs need converting first (use spatial-raw-processing); tissue-domain detection on already-preprocessed data (use…
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…