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-conditiongit 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-condition)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/spatial-condition"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-condition/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-condition"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/spatial-condition.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 | $0.00055 | $0.01395 |
| Opus 5.5 | $0.00022 | $0.00558 |
| Sonnet 5.5 | $0.00011 | $0.00279 |
| Haiku 4.5 | $0.00006 | $0.00139 |
Grade A, and why
spatial-condition 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
spatial-condition
Use from a step
from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("spatial-condition")
adata = library.compare_conditions(read_input("data/samples.h5ad"))
write_output(library.results(adata), "tables/pseudobulk_de.csv")
When to use
Compare conditions within each expression cluster using independent biological samples. Both PyDESeq2 and Wilcoxon operate on sample-level pseudobulk counts. Splitting spots from one sample does not create biological replicates. For per-cluster marker genes use spatial-de.
Inputs & Outputs
Input: AnnData with raw integer counts and sample/condition columns.
Counts are read from layers["counts"], then raw, then X.
Nonfinite, negative or fractional counts are rejected, not rounded.
Missing default leiden labels trigger expression clustering; other missing
cluster columns raise.
Functions return the same AnnData plus accessible result tables and a figure.
CLI writes processed.h5ad, report.md, result.json,
tables/pseudobulk_de.csv, per-cluster and skipped-contrast tables,
and a diagnostic gallery. See references/output_contract.md
for file names and generation conditions.
Key CLI
python skills/spatial/spatial-condition/spatial_condition.py --input samples.h5ad --output results/condition --condition-key condition --sample-key sample_id --reference-condition control
python skills/spatial/spatial-condition/spatial_condition.py --demo --output /tmp/spatial_condition
examples/example_step.py checks sample-level counts on simulated data.
references/parameters.md lists all backend flags.
Gotchas
tables/skipped_contrasts.csvexplains missing comparisons; absence is not evidence of no differential expression.pseudobulk_de.csvincludes per-row method and sample counts. PyDESeq2 fit failures can use Wilcoxon;run_info()["fallbacks"]records the reason and requested/executed methods. Missing PyDESeq2 raises with an installer hint.layers["counts"]must be real counts, not rounded log-normalized expression.condition_keyandsample_keymust differ, and each sample must belong to exactly one condition.n_samples_referenceandn_samples_othercount samples, never spots. Two replicates per condition are the default minimum, not a power guarantee.
What ships with it
10 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 5.9 KB runs code
- examples/example_step.py 1.0 KB runs code
- r_visualization/condition_publication_template.R 7.4 KB
- r_visualization/README.md 1.6 KB
- references/methodology.md 7.4 KB
- references/output_contract.md 2.1 KB
- references/parameters.md 3.1 KB
- spatial_condition.py 47 KB runs code
- tests/test_api.py 1.5 KB runs code
- tests/test_spatial_condition.py 5.6 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 · -3 lines · -28 tokens per session ad120806b247
- 1mo ago First seen · 133 lines · 83 tokens per session scan A 089735455fa1
spatial-condition is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 14d ago), licensed Apache-2.0. It adds 55 tokens to every session and 1,395 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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