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-differential-accessibilitygit 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-differential-accessibility)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-atac-seq-differential-accessibility"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-atac-seq-differential-accessibility/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-differential-accessibility"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-atac-seq-differential-accessibility.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 | $0.05900 |
| Opus 5 | $0.00047 | $0.02950 |
| Sonnet 5 | $0.00019 | $0.01180 |
| Haiku 4.5 | $0.00009 | $0.00590 |
Grade A, and why
bio-atac-seq-differential-accessibility 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 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.
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
97% identical to bio-atac-seq-differential-accessibility — 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 — 358 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: DiffBind 3.12+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, limma 3.58+, GenomicRanges 1.54+, ChIPseeker 1.38+, Subread 2.0+ (featureCounts), sva 3.50+, RUVSeq 1.36+.
Before using code patterns, verify installed versions match:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
Differential Accessibility
"Find chromatin regions that change accessibility between my conditions" -> Build a sample-by-region count matrix, normalize for library size and chromatin compaction, fit a generalized linear model (negative-binomial), and extract regions with significant accessibility change.
- R (consensus-peak workflow):
DiffBind-> count -> normalize -> contrast -> analyze - R (window-based, no peak set):
csaw::windowCounts+filterWindowsGlobal+ edgeR QL F-test - R (existing peak-count matrix):
DESeq2oredgeRdirectly onfeatureCountsoutput
DiffBind is a wrapper around DESeq2 / edgeR with ATAC-aware defaults. csaw is the only peak-free option; it tests fixed-width sliding windows. The choice depends on whether peaks are stable across conditions (use DiffBind) or whether some condition has dramatically different peak structure (use csaw or rebuild consensus peaks).
Algorithmic Taxonomy
| Tool | Model | Input | Min reps | Strength | Fails when |
|---|---|---|---|---|---|
| DiffBind 3.x (default DESeq2) | NB GLM via DESeq2 on consensus peaks | BAM + peak files | 2-3 per group | ATAC-aware defaults; built-in QC; blocking factors. Default in 3.x is normalize=DBA_NORM_LIB with library=DBA_LIBSIZE_FULL (full library size, background-included) |
Peaks differ dramatically between conditions (closed -> open shifts width); fewer than 2 reps per group |
| DiffBind with edgeR backend | NB GLM via edgeR-QL on consensus peaks | Same | 2-3 per group | Robust at low replicates (n=2 OK); QL test calibrates dispersion better than DESeq2 at small n | When global accessibility shifts dominate, switch to spike-in or full-library (library=DBA_LIBSIZE_FULL), never reads-in-peaks |
| DESeq2 directly on peak counts | NB GLM with shrinkage | featureCounts SAF | 3+ | Maximum control; integrates with apeglm shrinkage; modern interface | Need to manually build consensus peakset; per-region pre-filter required (low counts inflate dispersion) |
| edgeR QL F-test on peak counts | NB QL (quasi-likelihood) | featureCounts | 2 | Calibrated FDR at low n (n=2 viable); robust to outlier reps | Manual consensus peakset; small library bias unless normalization explicit |
| csaw (windows) | edgeR-QL on sliding windows | BAM only | 2 | No peak set required; detects diffuse changes peaks miss; merges adjacent windows | Computationally heavy; window size choice biases results; harder to annotate downstream |
| limma-voom | linear model with mean-variance trend | log2(CPM+offset) | 3 | Fast; good calibration at moderate count | Mis-calibrated at very low counts (atac peaks often have dropouts); needs explicit voom normalization |
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 · 358 lines · 94 tokens per session scan A 914abeb9fe30
bio-atac-seq-differential-accessibility 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 5,900 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to bio-atac-seq-differential-accessibility, differing in 12 lines, and is treated as a copy.
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