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 PKU-YuanGroup/OpenAI4S --skill bio-clip-seq-clip-qcgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-clip-seq-clip-qc)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-clip-seq-clip-qc"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clip-seq-clip-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/pku-yuangroup/openai4s/bio-clip-seq-clip-qc"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clip-seq-clip-qc.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00131 | $0.06032 |
| Opus 5 | $0.00066 | $0.03016 |
| Sonnet 5 | $0.00026 | $0.01206 |
| Haiku 4.5 | $0.00013 | $0.00603 |
Grade A, and why
bio-clip-seq-clip-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 9d 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.
This is a copy
98% identical to bio-clip-seq-clip-qc — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 402 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: preseq 3.2+, picard 3.1+, samtools 1.19+, bedtools 2.31+, deeptools 3.5+, idr 2.0.4+, MultiQC 1.21+, RSeQC 5.0+, pysam 0.22+, fastp 0.23+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws unexpected errors, introspect the installed binary and adapt the example to match the actual CLI rather than retrying.
CLIP-seq Quality Control
"Did my CLIP library pass?" -> Assess preprocessing retention, alignment rate, library complexity, replicate reproducibility (IDR), fraction reads in peaks (FRiP), read-distribution metagene, rRNA/snoRNA contamination, fragment-length distribution, and SMInput vs IP enrichment. ENCODE eCLIP compliance is the canonical bar: >= 1M unique fragments per replicate, IDR rescue and self-consistency ratios both < 2, FRiP >= 0.005 (narrow-binding), library complexity rising linearly with depth on preseq lc_extrap. A library can fail at any of these stages, and the failure mode determines whether the data is salvageable.
- CLI (library complexity, primary QC):
preseq lc_extrap -B -P aligned.bam -o complexity.txt - CLI (FRiP after peak calling):
bedtools intersect -c -s -a peaks.bed -b dedup.bam | awk '{s+=$NF} END{print s}'then divide by total reads - CLI (IDR, ENCODE convention):
idr --samples rep1.sorted.bed rep2.sorted.bed --input-file-type bed --rank 5 --output-file idr.out --idr-threshold 0.05 --plot - CLI (read distribution / metagene):
RSeQC read_distribution.py -i dedup.bam -r gencode.v38.bed+geneBody_coverage.py -i dedup.bam -r housekeeping.bed -o gb - CLI (rRNA contamination check):
samtools idxstats dedup.bam | awk '$1 ~ /rRNA|45S|18S|28S/ { sum+=$3 } END {print sum}' - CLI (consolidated report):
multiqc <run_dir>aggregates FastQC + cutadapt + STAR + umi_tools + preseq + samtools stats
What ships with it
2 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.
- 9d ago First seen · 402 lines · 131 tokens per session scan A d4632bd132d7
bio-clip-seq-clip-qc is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 131 tokens to every session and 6,032 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to bio-clip-seq-clip-qc, differing in 12 lines, and is treated as a copy.
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