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-atac-seq-nucleosome-positioninggit 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-atac-seq-nucleosome-positioning)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-atac-seq-nucleosome-positioning"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-atac-seq-nucleosome-positioning/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-atac-seq-nucleosome-positioning"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-atac-seq-nucleosome-positioning.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 | $0.00094 | $0.05204 |
| Opus 5 | $0.00047 | $0.02602 |
| Sonnet 5 | $0.00019 | $0.01041 |
| Haiku 4.5 | $0.00009 | $0.00520 |
Grade A, and why
bio-atac-seq-nucleosome-positioning scanned grade A with 1 finding 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 13d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
for r in bam.fetch(chrom, max(0, center - flank), center + flank): This is a copy
95% identical to bio-atac-seq-nucleosome-positioning — 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 — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: NucleoATAC 0.3.4+, ATACseqQC 1.26+, DANPOS 3.1+, samtools 1.19+, pysam 0.22+, pyBigWig 0.3+, BSgenome.Hsapiens.UCSC.hg38 1.4+, TxDb.Hsapiens.UCSC.hg38.knownGene 3.18+.
NucleoATAC is unmaintained since 2018 but remains the canonical ATAC-specific nucleosome caller; ATACseqQC, DANPOS3, and scprinter are actively developed alternatives. Verify versions before use:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_nameto verify parameters - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
Nucleosome Positioning
"Where are the nucleosomes in my ATAC-seq data?" -> Use fragment-size classes (Tn5 cuts twice through naked DNA generating short fragments; once on each side of a single nucleosome generating ~147+linker fragments) to call nucleosome centers, occupancy scores, and the spacing pattern around regulatory elements.
- CLI:
nucleoatac run --bed regions.bed --bam sample.bam --fasta genome.fa - R:
ATACseqQC::splitGAlignmentsByCut()-> fragment classes;factorFootprints()-> per-TF flanking nuc analysis - CLI:
python danpos.py dpos sample.bam(alternative; supports MNase, ATAC, DNase) - Python:
scprinterfor multi-scale nucleosome inference
Nucleosome Physics for ATAC
A nucleosome wraps ~147 bp DNA in 1.65 turns. Adjacent nucleosomes are separated by 20-50 bp linker; mean nucleosome repeat length (NRL) is species-dependent:
| Cell type / organism | NRL | Notes |
|---|---|---|
| Yeast S. cerevisiae | 165 bp | Tightly packed; less linker |
| Drosophila S2 | 175-185 bp | |
| Mouse ES cells | 188-196 bp | |
| Human HEK293 / K562 | 196-200 bp | Standard somatic |
| Human cortical neurons | 211 bp | Longer linker |
| Sperm chromatin | 240-250 bp | Tight packaging via protamines |
| Active gene bodies | -10 bp shorter than genome avg | Active transcription disrupts |
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.
- 13d ago First seen · 348 lines · 94 tokens per session scan A 089c59468c8b
bio-atac-seq-nucleosome-positioning is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 94 tokens to every session and 5,204 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 95% identical to bio-atac-seq-nucleosome-positioning, differing in 12 lines, and is treated as a copy.
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