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 bulkrna-read-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/bulkrna-read-qc)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/bulkrna-read-qc"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/bulkrna-read-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/bulkrna-read-qc"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/bulkrna-read-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 | $0.00071 | $0.00819 |
| Opus 5 | $0.00036 | $0.00409 |
| Sonnet 5 | $0.00014 | $0.00164 |
| Haiku 4.5 | $0.00007 | $0.00082 |
Grade A, and why
bulkrna-read-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 10d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
bulkrna-read-qc
When to use
Run as the first step on raw bulk RNA-seq FASTQ files (single or paired-end) before aligning. Reports per-base Phred quality, GC content, adapter contamination signals, read length distribution, and Q20/Q30 fractions — the metrics needed to decide whether trimming is worth the trouble.
Inputs & Outputs
Inputs
- File types:
.fastq,.fq
Outputs
tables/qc_summary.csvfigures/gc_content.pngfigures/per_base_quality.pngfigures/quality_score_distribution.pngfigures/read_length_distribution.pngreport.mdresult.json
Flow
- Open the FASTQ (auto-decompresses
.gzperbulkrna_read_qc.py:70). - Sample reads, decode Phred quality from header line 4 of each record.
- Compute per-base quality, GC content, length distribution, adapter motif counts.
- Render figures and write
report.md+result.json.
Gotchas
- This is a pure-Python reimplementation of FastQC core metrics, not FastQC itself. Coverage of the more obscure FastQC modules (overrepresented sequences, k-mer enrichment, per-tile quality) is intentionally omitted to keep the skill dependency-free. For full FastQC parity, run FastQC directly and feed the report into MultiQC.
- Phred encoding is assumed to be Phred+33 (Sanger / Illumina 1.8+). Older Illumina 1.3–1.7 platforms used Phred+64 — the per-base quality values will look ~31 points too high if such input is fed in unchanged. Confirm the source platform before trusting Q20/Q30 numbers.
.gzdetection is filename-suffix only (bulkrna_read_qc.py:70checks.endswith(".gz")). A gzipped file misnamed without.gzwill be opened as text and silently produce garbage; rename or symlink before running.
Key CLI
python omicsclaw.py run bulkrna-read-qc --demo
python omicsclaw.py run bulkrna-read-qc --input reads.fastq.gz --output results/
What ships with it
5 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.
- 10d ago First seen · 79 lines · 71 tokens per session scan A 471128b6ec46
bulkrna-read-qc is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 71 tokens to every session and 819 once invoked, about $0.0004 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 2026-08-30.
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