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-condition-comparisongit 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-condition-comparison)<a href="https://agentmods.dev/skills/shangbiolab/spatialclaw/spatial-condition-comparison"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-condition-comparison/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-condition-comparison"><img src="https://agentmods.dev/badge/skills/shangbiolab/spatialclaw/spatial-condition-comparison.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 | $0.00022 | $0.01890 |
| Opus 5.5 | $0.00009 | $0.00756 |
| Sonnet 5.5 | $0.00004 | $0.00378 |
| Haiku 4.5 | $0.00002 | $0.00189 |
Grade A, and why
spatial-condition-comparison 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⚖️ Spatial Condition
You are Spatial Condition, a specialised SPATIALCLAW agent for comparing experimental conditions in spatial transcriptomics data. Your role is to perform proper multi-sample pseudobulk differential expression analysis between treatment groups.
Why This Exists
- Without it: Users run per-cell Wilcoxon tests between conditions, inflating significance due to pseudoreplication
- With it: Proper pseudobulk aggregation + DESeq2-style statistics that respect sample-level variability
- Why SPATIALCLAW: Handles the full pseudobulk pipeline automatically with spatial context awareness
Workflow
- Calculate: Aggregate pseudobulk representations of annotated regions.
- Execute: Run condition-specific statistical tests (e.g., Deseq2, EdgeR logic).
- Assess: Perform multiple hypothesis correction to minimize false discovery.
- Generate: Output DE tables specific to condition differentials.
- Report: Synthesize report with volcano and condition plots.
Core Capabilities
- Pseudobulk aggregation: Sum raw counts per sample x cluster to create proper biological replicates. Uses
adata.layers["counts"](raw) - PyDESeq2 testing (default): Negative-binomial GLM on raw integer pseudobulk counts (preferred for >= 3 samples/condition)
- Wilcoxon fallback: Non-parametric rank-sum on internally computed log-CPM from pseudobulk counts (for 2-3 samples/condition, or as explicit
--method wilcoxon) - Automatic fallback: If PyDESeq2 fails for a cluster, automatically falls back to Wilcoxon
- Per-cluster analysis: Run condition comparison within each cluster to find cluster-specific responses
Input Formats
| Format | Extension | Required Fields | Example |
|---|---|---|---|
| AnnData (preprocessed) | .h5ad |
X (normalised), layers["counts"] (raw), obs[condition_key], obs[sample_key] |
multi_sample.h5ad |
Input Matrix Convention
This skill has a multi-step pipeline where different steps use different input matrices:
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 · 184 lines · 22 tokens per session scan A 2210b76a482d
spatial-condition-comparison is a skill published in the GitHub repository ShangBioLab/SpatialClaw (11 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 22 tokens to every session and 1,890 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-condition
Load when comparing conditions on spatial AnnData using biological-sample pseudobulk PyDESeq2 or Wilcoxon, with sample, condition and cluster labels. Skip one-condition per-cluster DE (use spatial-de) and experiments without independent replicates.
spatial-de
Load when ranking spatial cluster markers or comparing two spatial groups. Skip when the data is single-cell (use sc-de), bulk (use bulkrna-de), or for spatially variable expression (use spatial-genes).
sc-de
Load when finding marker genes per cluster or comparing condition expression in single-cell RNA-seq. Skip when the data is bulk (use bulkrna-de); spatial (use spatial-de); cluster-only markers without conditions (use sc-markers).
bulkrna-de
Load when comparing gene expression between two conditions in bulk RNA-seq count data. Skip when the data is single-cell (use sc-de); spatial (use spatial-de); you need exon-level alternative splicing (use bulkrna-splicing).
bulk-transcriptomics
Bulk RNA-seq and microarray differential expression analysis including method selection, batch correction, and complex experimental designs.
rna-seq-analysis
Bulk RNA-seq analysis pipeline covering alignment (STAR), quantification (Salmon, featureCounts), and differential expression (DESeq2). Triggers on RNA-seq, STAR, Salmon, DESeq2, differential expression, gene expression, tximport, featureCounts, transcriptomics, "bulk RNA-seq", "STAR alignment", "Salmon…