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-domainsnpx skills add TianGzlab/OmicsClaw --skill spatial-domainsgit 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-domains)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/spatial-domains"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-domains.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 | $0.00080 | $0.02029 |
| Opus 5 | $0.00040 | $0.01014 |
| Sonnet 5 | $0.00016 | $0.00406 |
| Haiku 4.5 | $0.00008 | $0.00203 |
Grade A, and why
spatial-domains 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
spatial-domains
When to use
The user has a preprocessed spatial AnnData (obsm["X_pca"] and
obsm["spatial"] populated) and wants tissue regions / niches
identified per spot (obs["spatial_domain"]). Seven methods:
leiden(default) — spatial-weighted Leiden (--resolution,--spatial-weight). No GPU.louvain— spatial-weighted Louvain. No GPU.spagcn— graph convolutional, fixed-K (--n-domains,--epochs,--spagcn-p). Requirestorch+SpaGCN.stagate— graph attention with cell-type-aware regularisation (--stagate-alpha,--pre-resolution,--rad-cutoff/--k-nn). Requirestorch+torch-geometric.graphst— graph-self-supervised (--epochs,--dim-output,--n-domains). Auto-detects 10x platform. Requirestorch+GraphST.banksy— neighbourhood expression matrix + PCA (--lambda-param,--num-neighbours). 0.2 = cell-typing mode, 0.8 = domain mode.cellcharter— niche-graph clustering with auto-k (--auto-k,--auto-k-min/--auto-k-max,--n-layers). Requirescellcharter+pyro-ppl.
For spatially variable genes use spatial-genes; for spot-level
cell-type labels use spatial-annotate.
Inputs & Outputs
Inputs
- File types:
.h5ad - Expects
obsm:spatial
Outputs
tables/domain_assignments.csvtables/domain_counts.csvtables/domain_method_embedding_points.csvtables/domain_neighbor_mixing.csvtables/domain_spatial_points.csvtables/domain_summary.csvtables/domain_umap_points.csvfigures/domain_local_purity_histogram.pngfigures/domain_local_purity_spatial.pngfigures/domain_neighbor_mixing.pngfigures/domain_sizes.pngfigures/pca_domains.pngfigures/spatial_domains.pngfigures/umap_domains.pngprocessed.h5adreport.mdresult.json- Processed AnnData (
saves_h5ad) — addsobs:spatial_domain;obsm:X_stagate,X_graphst,X_banksy_pca,X_cellcharter
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/domains_publication_template.R 1.5 KB
- r_visualization/README.md 1.3 KB
- references/methodology.md 15 KB
- references/output_contract.md 2.9 KB
- references/parameters.md 3.9 KB
- skill.yaml 6.3 KB
- spatial_domains.py 42 KB runs code
- tests/__init__.py 0 B runs code
- tests/test_spatial_domains.py 13 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 · 153 lines · 80 tokens per session scan A b432cf057f00
spatial-domains is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 80 tokens to every session and 2,029 once invoked, about $0.0004 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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