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-deconvolutiongit 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-deconvolution)<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-deconvolution"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-deconvolution/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-deconvolution"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-deconvolution.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 | $0.00028 | $0.01309 |
| Opus 5.5 | $0.00011 | $0.00524 |
| Sonnet 5.5 | $0.00006 | $0.00262 |
| Haiku 4.5 | $0.00003 | $0.00131 |
Grade A, and why
spatial-deconvolution 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spatial Transcriptomics Deconvolution
Cell-type deconvolution for spatial transcriptomics: infer the proportion of each cell type per spot by mapping a single-cell RNA-seq reference onto the spatial data.
Supported methods: Tangram (default), Stereoscope, GraphST-guided.
Why This Exists
- The Problem: Spatial platforms (Visium, Slide-seq…) capture multiple cells per spot, losing single-cell resolution.
- The Solution: Align a high-resolution scRNA-seq reference to spatial data to estimate cell-type proportions per spot.
- Why SPATIALCLAW: Wraps three complementary algorithms with a unified
--methodinterface, handles gene-ID matching, and stores results under both method-specific and unified obsm keys.
Methods Overview
| Method | Algorithm | Best For | Key Dependency |
|---|---|---|---|
tangram |
Deep-learning cell→space mapping (clusters mode) | General purpose, GPU-friendly | tangram-sc |
stereoscope |
Negative-Binomial probabilistic model | Count-data accuracy, multiple cell types | scvi-tools |
graphst |
GraphST spatial embedding + NNLS | Noisy/sparse ST data, captures spatial context | GraphST |
Output Keys
All methods write to two obsm slots:
| Key | Content |
|---|---|
<method>_ct_pred |
Method-specific proportions (e.g., tangram_ct_pred) |
deconvolution_ct_pred |
Unified key — always present, same data |
The unified deconvolution_ct_pred key lets downstream skills (visualisation, enrichment) work regardless of which method was used.
Workflow
- Load spatial (.h5ad) and scRNA reference (.h5ad).
- Intersect genes shared by both datasets.
- Deconvolve using the chosen method.
- Output updated spatial .h5ad with proportion matrices.
CLI Reference
# Tangram (default)
python spatialclaw.py run spatial-deconvolution \
--input <st_data.h5ad> \
--reference <sc_data.h5ad> \
--cell-type-key <column>
# Stereoscope
python spatialclaw.py run spatial-deconvolution \
--input <st_data.h5ad> \
--reference <sc_data.h5ad> \
--cell-type-key <column> \
--method stereoscope \
--n-epochs 10000
# GraphST-guided
python spatialclaw.py run spatial-deconvolution \
--input <st_data.h5ad> \
--reference <sc_data.h5ad> \
--cell-type-key <column> \
--method graphst
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 · 164 lines · 28 tokens per session scan A 1af56730b0cd
spatial-deconvolution is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 28 tokens to every session and 1,309 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
spatial-deconv
Load when deconvolving spot-level cell-type proportions on a Visium-style spatial AnnData using a labelled scRNA reference (FlashDeconv / Cell2location / RCTD / DestVI / Tangram / others). Skip when each spot is a single cell already (Xenium / MERFISH) (use spatial-annotate); tissue-domain detection (use…
spatial-annotate
Load when assigning per-spot cell-type labels on a spatial AnnData via marker-gene scoring or scRNA-reference mapping (Tangram / scANVI / CellAssign). Skip when computing spot-level cell-type proportions for multi-cell-per-spot platforms (use spatial-deconv); tissue-domain detection (use spatial-domains).
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).
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…
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…
Spatial Omics Skills Index
Skills for spatial transcriptomics analysis including single-cell to spatial mapping (MOSCOT), 3D visualization (PyVista), and related spatial workflows.