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-chip-seq-differential-bindinggit 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-chip-seq-differential-binding)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-chip-seq-differential-binding"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chip-seq-differential-binding/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-chip-seq-differential-binding"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-chip-seq-differential-binding.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 | $0.00125 | $0.05314 |
| Opus 5 | $0.00063 | $0.02657 |
| Sonnet 5 | $0.00025 | $0.01063 |
| Haiku 4.5 | $0.00013 | $0.00531 |
Grade A, and why
bio-chipseq-differential-binding 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 7d 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
95% identical to bio-chipseq-differential-binding — 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 — 331 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: DiffBind 3.20+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, PyDESeq2 0.5+, NormR 1.28+, MAnorm2 1.2+, ChIPseqSpikeInFree 1.6+.
DiffBind 3.0+ changed defaults: summits=200 (was FALSE), dba.normalize() now required, blacklist filtering on by default, full library size normalization replaces reads-in-peaks. Always run packageVersion('DiffBind') and inspect dba.normalize(obj, bRetrieve=TRUE) to confirm what was applied.
Differential ChIP-seq Binding
"Compare protein-DNA binding between experimental conditions" -> Identify regions where IP signal changes significantly, accounting for sequencing depth, composition bias, trended biases, and global shifts that confound naive normalization.
- R (BAM + peaks):
DiffBind::dba()->dba.count()->dba.normalize()->dba.analyze() - R (count matrix):
DESeq2::DESeq()oredgeR::glmQLFTest()on a peaks-by-samples matrix - R (windows-based, global-shift-robust):
csaw::windowCounts()->csaw::normFactors()->edgeR::glmQLFTest() - R (control-aware):
normr::diffR(chip1.bam, chip2.bam, genome)joint binomial mixture - Python (count matrix):
pydeseq2.DeseqDataSet()
Choice of normalization matters more than choice of test statistic (RLE vs TMM vs csaw bin-TMM on the same reference reads produce nearly identical results). Choose by which of the three normalization problems applies.
The Three Distinct Normalization Problems
| Problem | Symptom on MA plot | Cause | Fix |
|---|---|---|---|
| Composition bias | Loess shifts off y=0 systematically | Few high-signal peaks dominate read counts; small fold changes look large or inverted | TMM on background 10 kb bins (csaw / DiffBind background=TRUE); NOT reads-in-peaks |
| Trended bias (intensity-dependent) | Loess curve sweeps from + to - across abundance | Library-prep efficiency varies with fragment abundance | Non-linear loess offsets (csaw normOffsets); use cautiously — can over-normalize biology |
| Global shift (treatment changes most peaks) | Loess entirely shifted off y=0; mean log2FC ≠ 0 | Drug/perturbation changes the genome-wide level of binding (HDACi, BETi, EZH2i, target KD) | Spike-in scaling (ChIP-Rx); no algorithmic fix works |
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.
- 7d ago First seen · 331 lines · 125 tokens per session scan A 69b976a45992
bio-chipseq-differential-binding is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (403 stars, last pushed today), licensed MIT. It adds 125 tokens to every session and 5,314 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to bio-chipseq-differential-binding, differing in 12 lines, and is treated as a copy.
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