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 | $0.01399 |
| Opus 5 | $0.00030 | $0.00700 |
| Sonnet 5 | $0.00012 | $0.00280 |
| Haiku 4.5 | $0.00006 | $0.00140 |
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 6d 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 — 117 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 (R via rpy2; needs raw + filtered matrices).
Doublets are a different problem — use sc-doublet-detection for
multiplet barcodes.
Inputs & Outputs
Inputs
- Input kinds:
file,directory - Modalities: scrna
- File types:
.h5ad,.h5,.loom,.csv,.tsv
Outputs
tables/cell_metadata.csvtables/cellbender_output_cell_barcodes.csvtables/cellbender_output_metrics.csvtables/cells.csvtables/corrected_counts.csvtables/correction_summary.csvtables/gene_expression.csvtables/genes.csvfigures/barcode_rank.pngfigures/count_distribution.pngfigures/counts_comparison.pngfigures/r_ambient_violin.pngREADME.mdanalysis_summary.txtcellbender_output_report.htmlcontamination.jsonprocessed.h5adreport.mdresult.json- Processed AnnData (
saves_h5ad) — addslayers:counts;uns:ambient_correction,soupx,cellbender
Flow
- Load filtered AnnData; optionally load raw matrix (SoupX requires both).
- Validate
--contaminationis in[0, 1)and--expected-cellsis positive when set. - Run the chosen
--methodagainstMETHOD_REGISTRY. - If the requested backend is unavailable, fall back deterministically to
simple. - Stash the pre-correction matrix in
layers["counts"]and overwriteadata.Xwith the corrected counts; record the run params inuns["ambient_correction"|"soupx"|"cellbender"]. - Render diagnostic figures + emit
report.md+result.json.
What ships with it
6 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.
- 6d 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 (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,399 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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