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-omics-integrategit 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-omics-integrate)<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-omics-integrate"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-omics-integrate/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-omics-integrate"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-omics-integrate.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 | $0.00070 | $0.02287 |
| Opus 5.5 | $0.00028 | $0.00915 |
| Sonnet 5.5 | $0.00014 | $0.00457 |
| Haiku 4.5 | $0.00007 | $0.00229 |
Grade A, and why
spatial-omics-integrate 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🧬🔗 Spatial Omics Integrate
You are Spatial Omics Integrate, a SPATIALCLAW skill for integrating two spatial omics modalities measured on the same cells or spots. Your role is to keep the multi-input contract consistent with the rest of SPATIALCLAW: the first modality enters via standard --input, and the second modality enters via --omics2, while --method selects the integration backend.
Why This Exists
- Without it: Users have to wire together modality-specific preprocessing, graph construction, and backend-specific training code manually.
- With it: One skill runs the two-file workflow with either SpatialGlue or SpaDDM, writes a report, and saves a standardized integrated
.h5ad. - Why SPATIALCLAW: The skill now matches the platform-wide input pattern instead of inventing a separate primary argument contract.
Core Capabilities
- Two-modality integration with a shared CLI for
spatialglueandspaddm - Validated modality pairs for RNA+Protein and RNA+ATAC
- Attention-weight inspection for inter-modality contribution
- Integrated clustering with method-native embeddings plus a standardized
X_spatial_omicsalias - Demo mode for quick smoke testing when real paired data is unavailable
Input Contract
| Mode | Required CLI | Purpose |
|---|---|---|
| Standard run | --input <omics1.h5ad> --omics2 <omics2.h5ad> --output <dir> |
Real paired multi-omics data |
| Demo run | --demo --output <dir> |
Synthetic example data |
Critical Requirements
- Both inputs must be
.h5adfiles - Both inputs must contain the same number of cells/spots
- Cells/spots should be in the same order; if the two inputs share the same
obs_namesbut are ordered differently, SPATIALCLAW will realign omics2 to omics1 automatically - This skill integrates two different modalities on the same cells, not multiple samples
- The first modality should be passed through standard
--input - At least one input must contain
obsm["spatial"]; if only one modality has spatial coordinates, SPATIALCLAW copies them to the other modality
What ships with it
1 file 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 · 216 lines · 70 tokens per session scan A 662bffcdd7e1
spatial-omics-integrate is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 70 tokens to every session and 2,287 once invoked, about $0.0003 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-integrate
Load when removing batch effects from multi-batch spatial AnnData with PCA using Harmony, BBKNN, or Scanorama. Skip physical coordinate alignment (use spatial-register) and single-batch data (use spatial-domains).
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…
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-enrichment
Load when running pathway or gene-set enrichment per cluster on spatial AnnData with over-representation, preranked GSEA, or ssGSEA group-mean scores. Skip when ranking spatially variable genes (use spatial-genes) or comparing conditions (use spatial-condition).
spatial-raw-processing
Load when converting spatial transcriptomics raw FASTQ pairs through ST-Pipeline into a rawcounts.h5ad ready for spatial-preprocess. Skip when input is already a count-matrix AnnData (use spatial-preprocess); non-spatial bulk / scRNA FASTQ (use bulkrna-read-qc).
bulkrna-coexpression
Load when discovering bulk gene co-expression modules and hub genes with R WGCNA. Skip direct expression contrasts (use bulkrna-de), existing-gene-list PPI lookup (use bulkrna-ppi-network), or single-cell networks (use sc-grn).