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 | $0.01004 |
| Opus 5 | $0.00030 | $0.00502 |
| Sonnet 5 | $0.00012 | $0.00201 |
| Haiku 4.5 | $0.00006 | $0.00100 |
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 5d 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 — 104 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.
Inputs & Outputs
Inputs
- Modalities: scrna
- File types:
.h5ad
Outputs
tables/barcode_rank_curve.csvtables/cell_metadata.csvtables/gene_expression.csvtables/highest_expr_genes.csvtables/qc_metric_correlations.csvtables/qc_metrics_per_cell.csvtables/qc_metrics_summary.csvtables/qc_run_summary.csvfigures/barcode_rank.pngfigures/highest_expr_genes.pngfigures/qc_correlation_heatmap.pngfigures/qc_histograms.pngfigures/qc_scatter.pngfigures/qc_violin.pngfigures/r_qc_violin.pnganalysis_summary.txtprocessed.h5adreport.mdresult.json- Processed AnnData (
saves_h5ad)
Flow
- Load AnnData via the shared single-cell loader.
- Detect mitochondrial / ribosomal gene patterns from species hint (or
var_namesheuristic). - Compute per-cell QC metrics with
scanpy.pp.calculate_qc_metrics. - Emit summary stats + per-cell metrics tables.
- Render violin + scatter figures and
tables/highest_expr_genes.csv. - Emit
report.md+result.json(no cell filtering performed).
Gotchas
- No filtering happens here. Despite the name,
sc-qcdoes not remove cells or genes — it computes metrics and produces figures. Runsc-filternext with thresholds chosen from the QC violins. Theresult.jsonsummarycarriesn_cells/n_genesas observed, not as filtered. - Input file missing → hard fail.
sc_qc.py:635raisesFileNotFoundErrorwhen--inputdoes not resolve. Pre-flight your path before the skill, especially in batch pipelines. expression_sourcerecords which matrix the run used.result.json["summary"]["expression_source"]readslayers.counts,adata.raw, oradata.Xdepending on what the loader picked; QC fractions (mt%, ribo%) are only meaningful on a count-like source. Verify after every run, especially if the input came from outsidesc-standardize-input.
What ships with it
7 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.
- 5d ago First seen · 104 lines · 60 tokens per session scan A 6863f1f208f0
sc-qc is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 60 tokens to every session and 1,004 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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