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-footprintinggit 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-footprinting)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-atac-seq-footprinting"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-atac-seq-footprinting/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-footprinting"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-atac-seq-footprinting.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 | $0.00074 | $0.04894 |
| Opus 5 | $0.00037 | $0.02447 |
| Sonnet 5 | $0.00015 | $0.00979 |
| Haiku 4.5 | $0.00007 | $0.00489 |
Grade A, and why
bio-atac-seq-footprinting 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
94% identical to bio-atac-seq-footprinting — 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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: TOBIAS 0.16+, RGT HINT-ATAC 1.0.2+, Wellington (pyDNase) 0.3+, scprinter 0.1+, samtools 1.19+, bedtools 2.31+, pyBigWig 0.3+, MEME suite 5.5+.
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 package and adapt rather than retrying.
TF Footprinting
"Identify TF binding footprints in my ATAC-seq data" -> Detect short DNA stretches (typically 6-20 bp) of reduced Tn5 cleavage within accessible regions, where a bound TF physically protects DNA. Requires (1) Tn5 sequence-bias correction, (2) per-base footprint scoring, (3) motif-anchored detection.
- CLI:
TOBIAS ATACorrect->TOBIAS ScoreBigwig(formerlyFootprintScores) ->TOBIAS BINDetect - CLI:
rgt-hint footprinting --atac-seq(HINT-ATAC, single-step) - CLI:
wellington_footprints.py(legacy DNase, adapted for ATAC) - Python:
scprinter(multi-scale, single-cell aware; Hu 2025 Nature)
Tn5 has a strong sequence preference (Karabacak Calviello 2019), reading approximately +/- 4 bp around the insertion site. Without bias correction, "footprints" reflect Tn5 sequence preference rather than TF binding. This is the single most important step.
Algorithmic Taxonomy
| Tool | Bias model | Scoring | Min depth | Strength | Fails when |
|---|---|---|---|---|---|
| TOBIAS (BINDetect) | +/-12 bp k-mer window (--k_flank 12), dinucleotide weight matrix (DWM) |
Two-step: continuous footprint score then motif-anchored bound/unbound classification | >= 50M nuclear reads | Mature, peer-reviewed (Bentsen 2020), differential support, modular pipeline | Below 50M reads; sequencing errors near motif inflate background |
| HINT-ATAC | Hidden-Markov + dinucleotide bias correction | HMM emits open/footprint/closed states; calls ranked footprints | >= 50M | Single-step; integrates motif matching; handles DNase too | Less control over individual stages; HMM occasionally over-segments |
| Wellington (pyDNase) | DNase-developed; ATAC adaptation by post-shift | Cleavage-rate Poisson Z-score | >= 50M (DNase >= 80M) | Original footprinting framework; well-validated for DNase | Designed for DNase II; ATAC-specific bias not corrected as carefully |
| PIQ | Bayesian latent variable on cut sites | Genome-wide PWM scan + cleavage profile | >= 30M (lower because of model) | Per-TF posterior probabilities; works on lower depth | Outdated; not actively maintained; harder to install |
| scprinter | Multi-scale CNN-based footprint and TF activity | Resolves footprints at multiple TF size scales (CTCF vs nuclear receptors) | >= 1M cells (sc) or 50M (bulk) | Modern ML approach; single-cell; multi-scale resolves problematic TF families | Newer tool; benchmarks evolving; GPU recommended |
| TOBIAS + scprinter combination | TOBIAS bias correction + scprinter scoring | Two-step bridging | >= 50M | Combines the best bias model with multi-scale scoring | Manual pipeline, no single CLI |
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 · 299 lines · 74 tokens per session scan A 5fe6ced0856d
bio-atac-seq-footprinting is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 4,894 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to bio-atac-seq-footprinting, differing in 12 lines, and is treated as a copy.
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