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 genomic-region-annotation-integrationgit 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/genomic-region-annotation-integration)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/genomic-region-annotation-integration"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/genomic-region-annotation-integration/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/genomic-region-annotation-integration"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/genomic-region-annotation-integration.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 | $0.00070 | $0.01506 |
| Opus 5 | $0.00035 | $0.00753 |
| Sonnet 5 | $0.00014 | $0.00301 |
| Haiku 4.5 | $0.00007 | $0.00151 |
Grade A, and why
genomic-region-annotation-integration 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 9d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reconstruct the footprint scoring step that converts bias-corrected ATAC-seq signal into per-base footprint scores
Summary
This skill converts bias-corrected ATAC-seq insertion signal into footprint scores by measuring Tn5 insertion depletion patterns within accessible chromatin regions. It quantifies the magnitude of transcription factor binding footprints at base-pair resolution for downstream differential binding and visualization.
When to use
Apply this skill after bias-correcting ATAC-seq cutsite signal (via ATACorrect) when you have a bias-corrected bigWig file and need to compute per-position footprint scores within defined accessible regions (peaks, called footprints, or regulatory regions) to detect and quantify transcription factor occupancy through characteristic insertion depletion.
When NOT to use
- Input bigWig is uncorrected or not yet bias-corrected; use ATACorrect first.
- You have no defined accessible chromatin regions; define peaks or regulatory regions before scoring.
- The goal is only bulk chromatin accessibility quantification without transcription factor binding inference; standard peak calling and quantification suffices.
Inputs
- bias-corrected bigWig file (from ATACorrect)
- BED file of accessible genomic regions (ATAC-seq peaks or footprint regions)
Outputs
- footprint-score bigWig file (per-base footprint scores across input regions)
How to apply
Load the bias-corrected bigWig file (output from ATACorrect) and supply a BED file of accessible genomic regions where footprint scoring should occur. Use TOBIAS ScoreBigwig to measure signal depletion at each base within those regions, generating a bigWig output where each position reflects the magnitude of observed insertion depletion. The tool operates on the principle that protein-bound sites show visible depletion of Tn5 insertions, quantifying this depletion as a footprint score. Validate the output bigWig for correct format (valid header, coordinate ranges), non-zero score distributions, and consistency with input region boundaries.
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.
- 9d ago First seen · 96 lines · 70 tokens per session scan A c720c5de6201
genomic-region-annotation-integration is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 70 tokens to every session and 1,506 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 2026-09-03.
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