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-cell-communicationnpx 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.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 | $0.00031 | $0.00916 |
| Opus 5 | $0.00015 | $0.00458 |
| Sonnet 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 4d 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.
- 4d 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 3mo ago), licensed Apache-2.0. It adds 31 tokens to every session and 916 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-communication
Load when computing ligand-receptor cell-cell communication on a preprocessed spatial AnnData with obs[celltypekey] (default leiden) via LIANA (default), CellPhoneDB, FastCCC, or CellChat (R). Skip when running scRNA-only L-R inference (use sc-cell-communication); no cell-type labels exist (use spatial-annotate).
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).
citation-management
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure…
literature-review
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and…
scientific-writing
Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting…
peer-review
Systematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and grant review across disciplines.