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 HolobiomicsLab/asb-skill-collections --skill chromatin-accessibility-occupancy-predictiongit clone --depth 1 https://github.com/HolobiomicsLab/asb-skill-collectionsWrote 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/holobiomicslab/asb-skill-collections/chromatin-accessibility-occupancy-prediction)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/chromatin-accessibility-occupancy-prediction"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/chromatin-accessibility-occupancy-prediction/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/holobiomicslab/asb-skill-collections/chromatin-accessibility-occupancy-prediction"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/chromatin-accessibility-occupancy-prediction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00060 | $0.02152 |
| Opus 5 | $0.00030 | $0.01076 |
| Sonnet 5 | $0.00012 | $0.00430 |
| Haiku 4.5 | $0.00006 | $0.00215 |
Grade A, and why
chromatin-accessibility-occupancy-prediction 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reconstruct the transcription factor occupancy prediction step that classifies TF binding from footprint scores at motif sites
Summary
This skill uses TOBIAS BINDetect to classify transcription factor occupancy (bound vs. unbound) at specific genomic locations by comparing corrected ATAC-seq footprint scores and Tn5 insertion depletion patterns across conditions and motif positions. It bridges ATAC-seq signal preprocessing and binding site annotation by assigning occupancy states and differential binding metrics to known or predicted TF binding motifs.
When to use
Apply this skill after you have (1) corrected ATAC-seq BAM files for Tn5 insertion bias using ATACorrect, (2) computed per-base footprint scores using ScoreBigwig, (3) obtained a motif database (e.g., JASPAR PWMs in TOBIAS-compatible format), and (4) want to infer which TF binding sites are actually occupied in your experimental samples and how occupancy differs across conditions or cell states. Use it specifically when you observe visible Tn5 insertion depletion (footprints) around regulatory regions and need to map those patterns to known TF motifs.
When NOT to use
- Input data are not ATAC-seq or have not undergone Tn5 bias correction; TOBIAS BINDetect is optimized for bias-corrected Tn5 cutsites and will produce unreliable occupancy calls if applied to uncorrected or non-ATAC data.
- You lack a motif database or prior TF annotation; BINDetect requires known PWMs to scan and match, so de novo motif discovery or ChIP-seq peak matching would be the wrong entry point.
- Your goal is to discover novel TF binding sites rather than classify occupancy at known motifs; use ab initio footprint clustering or machine learning-based motif discovery instead.
- Single-cell ATAC-seq without adequate pseudobulk aggregation; the README notes that single-cell quality and clustering are paramount, and direct single-cell BAM analysis may produce noisy footprints.
Inputs
- Corrected ATAC-seq BAM file (output from TOBIAS ATACorrect)
- Footprint score bigWig files (output from TOBIAS ScoreBigwig)
- Peak/regulatory region BED file
- Motif database in JASPAR or TOBIAS-compatible PWM format
- Sample metadata or condition labels for comparative occupancy analysis
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.
- 11d ago First seen · 109 lines · 60 tokens per session scan A 626badf58e2f
chromatin-accessibility-occupancy-prediction is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed today), licensed Apache-2.0. It adds 60 tokens to every session and 2,152 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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