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 sc-degit 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/sc-de)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-de"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-de.svg" alt="Measured on agentmods" 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 | $0.00051 | $0.01593 |
| Opus 5 | $0.00026 | $0.00796 |
| Sonnet 5 | $0.00010 | $0.00319 |
| Haiku 4.5 | $0.00005 | $0.00159 |
Grade A, and why
sc-de 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 4d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sc-de
When to use
The user has a preprocessed scRNA-seq AnnData and wants to know either
(a) which genes mark each cluster (Wilcoxon / t-test / logreg ranking) or
(b) which genes change between conditions in a replicate-aware way
(deseq2_r pseudobulk). Five backends are exposed; the wrapper enforces
the matrix contract (normalized expression vs raw counts) per backend
because mixing them is the most common silent-wrong-answer failure mode.
Inputs & Outputs
Inputs
- Modalities: scrna
- File types:
.h5ad - Requires a preprocessed AnnData (
Xnormalised, PCA/neighbours present)
Outputs
tables/counts.csvtables/de_full.csvtables/de_group_summary.csvtables/de_top_markers.csvtables/deseq2_results.csvtables/gene_expression.csvtables/markers_top.csvtables/mast_results.csvtables/metadata.csvtables/pseudobulk_summary.csvfigures/marker_dotplot.pngfigures/pseudobulk_group_summary.pngfigures/r_de_heatmap.pngfigures/r_de_manhattan.pngfigures/r_de_volcano.pngfigures/r_feature_cor.pngfigures/r_feature_violin.pngfigures/rank_genes_groups.pnginput.h5adprocessed.h5adreport.mdresult.json- Processed AnnData (
saves_h5ad)
Flow
- Load the AnnData and inspect which matrix is in
X(normalized vs raw). - Validate the requested method's matrix contract — fail fast on mismatch.
- For exploratory paths: run Scanpy
rank_genes_groupsand export marker dotplot + rank summary. - For
mast: hand the log-normalized matrix to the R MAST bridge. - For
deseq2_r: pseudobulk-aggregate bysample_key×celltype_key, drop bins under--pseudobulk-min-cells/--pseudobulk-min-counts, run R DESeq2. - Render direct figures (and R-enhanced ones if
--r-enhanced). - Write
processed.h5ad, tables,figure_data/manifest.json,report.md,result.json, and reproducibility script.
What ships with it
8 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.
- 4d ago First seen · 123 lines · 51 tokens per session scan A 4443e57bc72c
sc-de is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 51 tokens to every session and 1,593 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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