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 genomics-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/genomics-qc)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/genomics-qc"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/genomics-qc.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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.
- medium Output Handling · line 65 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
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 | $0.00988 |
| Opus 5 | $0.00036 | $0.00494 |
| Sonnet 5 | $0.00014 | $0.00198 |
| Haiku 4.5 | $0.00007 | $0.00099 |
Grade A, and why
genomics-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 8d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
genomics-qc
When to use
The user has a raw FASTQ file (.fastq or .fastq.gz) and wants
standard pre-alignment QC: total reads, mean Phred quality, Q20 /
Q30 rates, GC / N content, mean read length, adapter contamination
percentage, per-base quality profile. This skill mirrors a subset
of FastQC / fastp metrics in pure Python.
It does NOT trim adapters or filter reads — it only measures.
For BAM-level alignment QC use genomics-alignment.
Inputs & Outputs
Inputs
- File types:
.fastq,.fq
Outputs
tables/per_base_quality.csvtables/qc_metrics.csvtables/read_length_distribution.csvreport.mdresult.json
Flow
- Load FASTQ (
--input <reads.fastq[.gz]>) or synthesise demo reads atoutput_dir/demo_reads.fastq(genomics_qc.py:170). - Stream up to
--max-readsrecords (default 500_000); aggregate Phred / GC / N / length stats. - Detect adapter contamination via fixed adapter motif scan.
- Write
tables/qc_metrics.csv(genomics_qc.py:272) +tables/per_base_quality.csv(genomics_qc.py:279) +report.md+result.json.
Gotchas
--max-readsdefaults to 500 000 (genomics_qc.py:247). For very deep libraries this is a hard cap — increase it for full-flowcell QC. Reads beyond the cap are silently ignored.- Empty FASTQ raises
ValueError("No reads found in {fastq_path}")atgenomics_qc.py:138. A truncated upload manifests as exit-1; check the file size first. --inputREQUIRED unless--demo.genomics_qc.py:258raisesValueError("--input required when not using --demo"); non-existent paths raiseFileNotFoundErrorat:261.- No trimming or filtering happens here. This is a pure measurement skill — to actually trim adapters or quality-filter, run fastp / Trimmomatic outside OmicsClaw before re-running this for post-trim QC.
- Phred encoding is assumed Phred+33. Old Solexa / Illumina 1.3+ Phred+64 files would mis-score; the script does NOT auto-detect encoding.
- Demo writes a synthetic FASTQ into
output_dir.genomics_qc.py:170writesdemo_reads.fastqdirectly into the user-supplied output directory — re-running--demooverwrites silently.
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.
- 8d ago First seen · 87 lines · 71 tokens per session scan A 1c225dda8234
genomics-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 988 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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