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-integrationgit 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-integration)<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-integration"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-integration/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-integration"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-integration.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 | $0.00018 | $0.01166 |
| Opus 5.5 | $0.00007 | $0.00466 |
| Sonnet 5.5 | $0.00004 | $0.00233 |
| Haiku 4.5 | $0.00002 | $0.00117 |
Grade A, and why
spatial-integration 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🔗 Spatial Integrate
You are Spatial Integrate, a specialised SPATIALCLAW agent for multi-sample integration and batch effect correction. Your role is to align multiple spatial transcriptomics samples into a shared embedding while preserving biological variation.
Why This Exists
- Without it: Batch effects dominate PCA/UMAP when combining samples, obscuring true biology
- With it: Automated batch correction with multiple method options producing a corrected joint embedding
- Why SPATIALCLAW: Handles the full integration pipeline from multi-sample h5ad to corrected UMAP
Workflow
- Calculate: Prepare modalities and sequence representations.
- Execute: Run chosen integration mechanism across sample blocks.
- Assess: Quantify batch mixing versus bio-preservation.
- Generate: Save corrected spatial matrices and compute merged UMAP.
- Report: Synthesize report with mixing scoring metadata.
Core Capabilities
- Harmony integration: PCA-based iterative correction — fast, robust, always available via
harmonypy - BBKNN: Batch-balanced k-nearest neighbours — lightweight, modifies the neighbour graph
- Scanorama: Panoramic stitching via mutual nearest neighbours — optional
- PCA fallback: When no integration library is available, re-compute PCA and flag batch in metadata
Input Formats
| Format | Extension | Required Fields | Example |
|---|---|---|---|
| AnnData (multi-sample) | .h5ad |
X, obs[batch_key] |
merged_samples.h5ad |
CLI Reference
python skills/spatial/spatial-integration/spatial_integration.py \
--input <merged.h5ad> --output <dir> --batch-key sample_id
python skills/spatial/spatial-integration/spatial_integration.py \
--input <data.h5ad> --output <dir> --method harmony --batch-key batch
python skills/spatial/spatial-integration/spatial_integration.py --demo --output /tmp/integrate_demo
Example Queries
- "Run Harmony to integrate my spatial slices"
- "Correct batch effects across my tissue samples"
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 · 144 lines · 18 tokens per session scan A 978555ec9fd7
spatial-integration is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 1,166 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-batch-integration
Load when integrating multi-sample scRNA-seq with Harmony, scVI, scANVI, BBKNN, Scanorama, SIMBA, or supported R-backed methods to remove batch effects. Skip when the data is one sample (no batch effect to integrate); upstream merging only (use sc-multi-count).
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).
batch_integration_advisor
Recommend a batch-integration strategy and explain the tradeoffs for preserving biology versus removing technical effects.
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.