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-cell-communicationgit 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-cell-communication)<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-cell-communication"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-cell-communication/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-cell-communication"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-cell-communication.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 | $0.00031 | $0.00916 |
| Opus 5.5 | $0.00012 | $0.00366 |
| Sonnet 5.5 | $0.00006 | $0.00183 |
| Haiku 4.5 | $0.00003 | $0.00092 |
Grade A, and why
spatial-cell-communication 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spatial Cell Communication
This skill identifies ligand-receptor interactions between annotated cell groups in spatial transcriptomics data.
Methods
| Method | Input Matrix | Notes |
|---|---|---|
builtin |
adata.X log-normalized expression |
Dependency-light curated ligand-receptor scoring with permutation p-values |
liana |
adata.X log-normalized expression |
Optional LIANA+ consensus ranking |
cellphonedb |
adata.X log-normalized expression |
Optional CellPhoneDB statistical permutation test |
fastccc |
adata.X log-normalized expression |
Optional permutation-free FastCCC workflow |
CLI
python skills/spatial/spatial-cell-communication/spatial_cell_communication.py \
--input <preprocessed.h5ad> \
--method builtin \
--cell-type-key cell_type \
--species human \
--output <dir>
python skills/spatial/spatial-cell-communication/spatial_cell_communication.py --demo --output <dir>
spatialclaw run spatial-cell-communication --input <file.h5ad> --output <dir>
Allowed --method values: builtin, liana, cellphonedb, fastccc.
Parameters
| Parameter | Default | Description |
|---|---|---|
--input |
- | Preprocessed spatial .h5ad |
--output |
required | Output directory |
--demo |
off | Run built-in demo |
--method |
builtin |
builtin, liana, cellphonedb, or fastccc |
--cell-type-key |
leiden |
.obs column with cell type or cluster labels |
--species |
human |
Ligand-receptor database species: human, mouse, or zebrafish |
--n-perms |
100 |
Number of permutations for permutation-based methods |
Output
output_directory/
├── report.md
├── result.json
├── processed.h5ad
├── figures/
│ ├── lr_ranked.png
│ ├── lr_dotplot.png
│ ├── lr_heatmap.png
│ ├── lr_chord.png
│ └── lr_spatial.png
└── tables/
├── lr_interactions.csv
└── top_interactions.csv
If SPATIALCLAW_EXTRA_FIGURE_FORMATS=pdf is set, each figure is also exported
as a PDF alongside the default PNG.
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 · 119 lines · 31 tokens per session scan A 0c38be6e64b6
spatial-cell-communication is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 31 tokens to every session and 916 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.
Other skills, from other repositories
sc-cell-communication
Load when computing cell-cell ligand-receptor communication on an annotated scRNA AnnData via builtin scorer, LIANA, CellPhoneDB, CellChat (R), or NicheNet (R). Skip when assigning cell-type labels (use sc-cell-annotation); transcription factor → target regulatory networks (use sc-grn).
spatial-communication
Load when computing ligand-receptor communication on labelled spatial AnnData with LIANA, CellPhoneDB, FastCCC or CellChat. Skip unlabelled data (use spatial-annotate) and non-spatial scRNA analysis (use sc-cell-communication).
Gene Panel Selection Workflow
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…
Developmental Gene Panel Design Workflow
Panel design for DEVELOPING / dynamic systems (embryonic organs, differentiation, regeneration). The target experiment is usually a LATE / terminal stage, but the biology is a trajectory: terminal cell types are end-products of earlier lineage programs. A panel built from the target stage alone resolves terminal…
Spatial Omics Skills Index
Skills for spatial transcriptomics analysis including single-cell to spatial mapping (MOSCOT), 3D visualization (PyVista), and related spatial workflows.
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…