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-preprocessinggit 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-preprocessing)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-preprocessing"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-preprocessing/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/sc-preprocessing"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-preprocessing.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 | $0.00058 | $0.03023 |
| Opus 5.5 | $0.00023 | $0.01209 |
| Sonnet 5.5 | $0.00012 | $0.00605 |
| Haiku 4.5 | $0.00006 | $0.00302 |
Grade A, and why
sc-preprocessing 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 17d 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sc-preprocessing
When to use
The user has a QC'd AnnData and wants the standard
"filter → normalise → HVG → PCA" pipeline before clustering or batch
integration. Four interchangeable backends are available: scanpy
(default; CP10k log + HVG seurat flavour), seurat (R-backed
LogNormalize / CLR / RC), sctransform (R-backed regularised NB), and
pearson_residuals (raw-count HVG selection plus Pearson residual
transformation). The skill stops at PCA — UMAP / clustering live in
sc-clustering, multi-sample correction in sc-batch-integration.
Use from a step
pre = load_skill("sc-preprocessing")
adata = read_input("results/01_qc/intermediate/adata_qc.h5ad")
# max_mt_pct 10: PBMC cells above 10% mitochondrial reads are mostly damaged (agreed with the user).
adata = pre.preprocess(adata, method="scanpy", max_mt_pct=10.0)
write_output(pre.run_info(adata)["filter_summary"], "tables/filter_summary.json")
write_output(pre.pca_variance_table(adata), "tables/pca_variance_ratio.csv")
write_output(adata, "intermediate/adata_preprocessed.h5ad")
A complete step that runs on demo data: examples/example_step.py.
If the input already passed through sc-filter, use
preprocess(adata, apply_filters=False). This preserves that step's retained
cells and genes, including its doublet decision, while normalization, HVG
selection and PCA still run.
API
preprocess(adata, *, method: str='scanpy', apply_filters: bool=True, min_genes: int=200, min_cells: int=3, max_mt_pct: float=20.0, n_top_hvg: int | None=None, n_pcs: int=50, normalization_target_sum: float=10000.0, scanpy_hvg_flavor: str='seurat', pearson_hvg_flavor: str='seurat_v3', pearson_theta: float=100.0, seurat_normalize_method: str='LogNormalize', seurat_scale_factor: float=10000.0, seurat_hvg_method: str='vst', sctransform_regress_mt: bool=True, remove_doublets: bool=True, doublet_score_threshold: float=0.25, preserve_var_names: bool=False)
What ships with it
12 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 23 KB runs code
- examples/example_step.py 999 B runs code
- references/methodology.md 2.6 KB
- references/output_contract.md 3.1 KB
- references/parameters.md 2.8 KB
- references/r_visualization.md 622 B
- sc_preprocess.py 33 KB runs code
- tests/__init__.py 0 B runs code
- tests/omicbench_preprocessing.py 7.3 KB runs code
- tests/test_apply_filters.py 2.2 KB runs code
- tests/test_sc_preprocess_methods.py 1.1 KB runs code
- tests/test_sc_preprocess.py 6.9 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.
- 17d ago Changed · +77 lines ab1dd34b0cfa
- 1mo ago First seen · 127 lines · 58 tokens per session scan A 0c13b198f5df
sc-preprocessing is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 17d ago), licensed Apache-2.0. It adds 58 tokens to every session and 3,023 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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