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-qcgit 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-qc)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-qc"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-qc/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-qc"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-qc.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 | $0.00060 | $0.02115 |
| Opus 5.5 | $0.00024 | $0.00846 |
| Sonnet 5.5 | $0.00012 | $0.00423 |
| Haiku 4.5 | $0.00006 | $0.00212 |
Grade A, and why
sc-qc 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sc-qc
When to use
The user has a single-cell AnnData (post-counting / post-standardisation)
and wants to review cell quality — counts, detected genes, mitochondrial
percentage, ribosomal percentage — before any filtering. This skill
reports, it does not remove cells. Use sc-filter to actually drop
cells based on these metrics.
Use from a step
qc = load_skill("sc-qc")
adata = qc.calculate_qc(read_input("data/pbmc.h5ad"), species="human")
write_output(qc.qc_summary(adata), "tables/qc_metrics_summary.csv")
write_output(qc.qc_figure(adata), "figures/qc_histograms.png")
write_output(adata, "intermediate/adata_qc.h5ad")
A complete step that runs on demo data: examples/example_step.py.
API
calculate_qc(adata, *, species: str='human', calculate_ribo: bool=True)
Bring the input into the OmicsClaw scRNA contract and add per-cell QC metrics to obs.
The count-like matrix becomes X and layers['counts'], raw keeps a
counts snapshot, and gene names are made unique. Then scanpy's
calculate_qc_metrics adds n_genes_by_counts, total_counts,
pct_counts_mt and pct_counts_ribo (when the gene prefixes match), plus
log10_total_counts and log10_n_genes_by_counts; var gets mt and
ribo flags. How the input was read is recorded for :func:run_info.
:param species: "human" (MT-, RPS/RPL prefixes) or "mouse"
(mt-, Rps/Rpl). Default "human", the CLI default. Set it to the
organism of the data: with the wrong one no mitochondrial gene matches and
pct_counts_mt is not computed at all.
:param calculate_ribo: Also compute pct_counts_ribo. Default True, as the CLI does.
:returns: A new AnnData (the standardized copy), with the metrics in obs.
:raises ValueError: no count-like matrix can be found in X, layers or raw.
run_info(adata, *, keep: bool=True) -> dict
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.
- 17d ago Changed · +60 lines 330a61fd3b51
- 1mo ago First seen · 104 lines · 60 tokens per session scan A 6863f1f208f0
sc-qc is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 17d ago), licensed Apache-2.0. It adds 60 tokens to every session and 2,115 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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