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-ambient-removalgit 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-ambient-removal)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/sc-ambient-removal"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-ambient-removal/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-ambient-removal"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/sc-ambient-removal.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 | $0.02200 |
| Opus 5.5 | $0.00024 | $0.00880 |
| Sonnet 5.5 | $0.00012 | $0.00440 |
| Haiku 4.5 | $0.00006 | $0.00220 |
Grade A, and why
sc-ambient-removal 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sc-ambient-removal
When to use
The user has filtered (or raw + filtered) droplet-based scRNA-seq counts
and suspects ambient RNA from cell-free droplets is inflating per-cell
expression — typical for 10X data with high droplet density. Three
backends share the CLI: simple (a deterministic ambient-profile
subtraction, default), cellbender (Python, requires GPU for sensible
runtime), and soupx (Rscript; needs raw + filtered matrices).
Doublets are a different problem — use sc-doublet-detection for
multiplet barcodes.
Use from a step
ambient = load_skill("sc-ambient-removal")
adata = ambient.remove_ambient(read_input("counts.h5ad"), contamination=0.05)
write_output(ambient.correction_summary(adata), "tables/correction_summary.csv")
write_output(ambient.correction_figure(adata), "figures/counts_comparison.png")
write_output(adata, "intermediate/adata_corrected.h5ad")
With raw droplets, use remove_ambient_soupx(filtered, raw=raw_droplets).
CellBender remains CLI-only because it uses an external process and files.
The simple-method example is in examples/example_step.py.
API
remove_ambient(adata, *, contamination: float=0.05)
Subtract a mean ambient profile from count-like expression in place.
Select layers['counts'], aligned raw or X; retain that matrix in layers['counts'] and replace X with nonnegative corrected values. The mean cell profile is only an approximation when empty droplets are absent.
:param adata: AnnData with a count-like expression matrix. :param contamination: Fraction to subtract before clipping at zero, in [0, 1). Default 0.05, matching the CLI. :returns: The same AnnData; run_info reports the matrix source and reduction. :raises ValueError: No count-like matrix exists or contamination is invalid.
remove_ambient_soupx(adata, *, raw)
Return SoupX-corrected cells, using raw droplet counts to estimate ambient RNA.
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 · +82 lines 46d6a5c202f0
- 1mo ago First seen · 117 lines · 59 tokens per session scan A 6a10c804524e
sc-ambient-removal is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 17d ago), licensed Apache-2.0. It adds 59 tokens to every session and 2,200 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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