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 TianGzlab/OmicsClaw --skill spatial-enrichmentgit 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-enrichment)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/spatial-enrichment"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-enrichment/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/tiangzlab/omicsclaw/spatial-enrichment"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-enrichment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 3 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00059 | $0.01602 |
| Opus 5.5 | $0.00024 | $0.00641 |
| Sonnet 5.5 | $0.00012 | $0.00320 |
| Haiku 4.5 | $0.00006 | $0.00160 |
Grade A, and why
spatial-enrichment 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 14d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
spatial-enrichment
When to use
Interpret group markers with gene sets, or score group-mean expression. The default is a small local OmicsClaw signature library. Hosted libraries require network access; explicit gene-set mappings and local GMT/JSON files work offline. Synthetic demo signatures are not curated biological pathways.
Use from a step
from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("spatial-enrichment")
adata = read_input("processed.h5ad")
library.enrich(adata, groupby="leiden", source="omicsclaw_core")
write_output(library.results(adata), "tables/enrichment_results.csv")
The executable example uses explicit synthetic marker sets and checks their known group enrichment.
For local libraries use sets = read_input("data/sets.gmt", reader=library.read_gene_sets),
then library.enrich(adata, gene_sets=sets). read_input records the file hash.
library.fetch_gene_sets("KEGG_2021_Human") explicitly accesses the network.
Cache that mapping as JSON before a repeatable analysis, then load the cached
file with read_input and this reader. Computation accepts built-in sources or mappings.
API
read_gene_sets(path)
Read a local GMT or JSON gene-set library without writing files.
In a step call read_input(path, reader=library.read_gene_sets) to record the input file hash and resolve the path relative to the project root.
:param path: Local GMT or JSON path supplied by read_input or the CLI. :returns: Term-to-gene mapping retaining its local source metadata. :raises FileNotFoundError: The local file is absent. :raises ValueError: The format or JSON structure is invalid.
fetch_gene_sets(source, *, species='human')
Fetch a remote Enrichr library; this call requires network access.
Cache the mapping as JSON, then use read_input(path, reader=library.read_gene_sets) for repeatable analysis steps. Fetching alone does not record a file hash.
What ships with it
11 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.
- _api.py 7.4 KB runs code
- examples/example_step.py 1.1 KB runs code
- r_visualization/enrichment_publication_template.R 3.3 KB
- r_visualization/README.md 1.7 KB
- references/methodology.md 6.8 KB
- references/output_contract.md 2.0 KB
- references/parameters.md 5.1 KB
- spatial_enrichment.py 62 KB runs code
- tests/test_api.py 6.1 KB runs code
- tests/test_reader_step.py 1.7 KB runs code
- tests/test_spatial_enrichment.py 7.4 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.
- 14d ago Changed · +25 lines · -10 tokens per session ebafad939db6
- 1mo ago First seen · 139 lines · 69 tokens per session scan A 6b6ac9c8cc52
spatial-enrichment is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 14d ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,602 once invoked, about $0.0002 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 2026-09-03.
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