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/tiangzlab/omicsclaw/spatial-statisticsnpx skills add TianGzlab/OmicsClaw --skill spatial-statisticsgit clone --depth 1 https://github.com/TianGzlab/OmicsClawWrote 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/tiangzlab/omicsclaw/spatial-statistics)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/spatial-statistics"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-statistics.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 | $0.00058 | $0.02229 |
| Opus 5 | $0.00029 | $0.01115 |
| Sonnet 5 | $0.00012 | $0.00446 |
| Haiku 4.5 | $0.00006 | $0.00223 |
Grade A, and why
spatial-statistics 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 yesterday.
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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
spatial-statistics
When to use
The user has a clustered spatial AnnData (obs[--cluster-key] for
cluster-aware analyses; obsm["spatial"] populated) and wants a
specific spatial-statistics analysis. Pick --analysis-type from
VALID_ANALYSIS_TYPES:
moran/geary— global spatial autocorrelation per gene.local_moran— per-spot LISA + GeoDa quadrants (--local-moran-geoda-quads).getis_ord— per-spot hotspot Z-scores.bivariate_moran— exactly two genes (--genes geneA,geneB).neighborhood_enrichment— squidpy NES between cluster pairs.ripley— Ripley K / L / G / F (--ripley-mode,--ripley-metric).co_occurrence— pairwise label co-occurrence at distance bins (--coocc-interval,--coocc-n-splits).spatial_centrality— graph-centrality per spot.
For per-gene SVG ranking use spatial-genes; for tissue-domain
detection use spatial-domains.
Inputs & Outputs
Inputs
- File types:
.h5ad - Requires a preprocessed AnnData (
Xnormalised, PCA/neighbours present) - Expects
obsm:spatial
Outputs
tables/analysis_results.csvtables/analysis_summary.csvtables/bivariate_moran_summary.csvtables/centrality_scores.csvtables/cluster_summary.csvtables/cooccurrence_curves.csvtables/cooccurrence_pairs.csvtables/neighborhood_counts.csvtables/neighborhood_pairs.csvtables/neighborhood_zscore.csvtables/network_per_cluster.csvtables/network_summary.csvtables/pair_summary.csvtables/per_cluster_metrics.csvtables/ripley_cluster_summary.csvtables/ripley_curves.csvtables/spot_statistics.csvtables/top_results.csvfigures/bivariate_moran_scatter.pngfigures/bivariate_moran_spatial.pngfigures/centrality_scores.pngfigures/centrality_scores_barplot.pngfigures/co_occurrence_curves.pngfigures/co_occurrence_distribution.pngfigures/co_occurrence_top_pairs.pngfigures/geary_pvalue_distribution.pngfigures/geary_ranking.pngfigures/geary_score_vs_significance.pngfigures/moran_pvalue_distribution.pngfigures/moran_ranking.pngfigures/moran_score_vs_significance.pngfigures/neighborhood_enrichment_heatmap.pngfigures/neighborhood_top_pairs.pngfigures/neighborhood_zscore_distribution.pngfigures/network_degree_histogram.pngfigures/network_per_cluster_degree.pngfigures/ripley_cluster_max_stat.pngfigures/ripley_curves.pngfigures/ripley_stat_distribution.pngprocessed.h5adreport.mdresult.json- Processed AnnData (
saves_h5ad) — addsobs:local_moran_<gene>,local_moran_pval_<gene>,local_moran_q_<gene>,getis_ord_<gene>,getis_ord_pval_<gene>
What ships with it
9 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.
- r_visualization/README.md 1.4 KB
- r_visualization/stats_publication_template.R 3.2 KB
- references/methodology.md 5.9 KB
- references/output_contract.md 6.6 KB
- references/parameters.md 7.0 KB
- skill.yaml 11 KB
- spatial_statistics.py 75 KB runs code
- tests/__init__.py 0 B runs code
- tests/test_spatial_statistics.py 6.8 KB runs code
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.
- yesterday First seen · 176 lines · 58 tokens per session scan A 30844a65159c
spatial-statistics is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 58 tokens to every session and 2,229 once invoked, about $0.0003 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 2026-09-03.
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