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-feature-annotation-overlapgit 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-feature-annotation-overlap)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/genomic-feature-annotation-overlap"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/genomic-feature-annotation-overlap/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-feature-annotation-overlap"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/genomic-feature-annotation-overlap.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 | $0.00027 | $0.01641 |
| Opus 5 | $0.00014 | $0.00821 |
| Sonnet 5 | $0.00005 | $0.00328 |
| Haiku 4.5 | $0.00003 | $0.00164 |
Grade A, and why
genomic-feature-annotation-overlap 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
genomic-feature-annotation-overlap
Summary
Classify differentially methylated bases or regions by their overlap with gene annotation features (promoters, exons, introns) and CpG island contexts (islands vs. shores) using genomation's annotateWithGeneParts() and annotateWithFeatureFlank() functions. This skill quantifies the genomic distribution of methylation signals to identify which regulatory and structural features are enriched for differential methylation.
When to use
Apply this skill after you have identified a set of differentially methylated bases or regions (e.g., from calculateDiffMeth() in methylKit) and need to determine whether these sites are enriched in specific gene annotation contexts (promoters, exons, introns, intergenic regions) or CpG island landscapes (CpGi islands vs. shores). Use it when you need to report the percentage breakdown of your differential methylation signal across these annotation categories.
When NOT to use
- Your input is raw methylation call files that have not yet been filtered or merged across samples—first use methRead(), unite(), and calculateDiffMeth() to generate a methylDiff object.
- You lack appropriate gene annotation or CpG island annotation files for your reference genome; annotation must be in BED format and correspond to the correct genome build.
- Your analysis goal is to identify novel regulatory elements or perform de novo peak calling—this skill requires pre-existing annotation and only classifies known features.
Inputs
- methylDiff object from calculateDiffMeth() containing differentially methylated bases with q-value and percent methylation difference statistics
- RefSeq gene annotation BED file (e.g., refseq.hg18.bed.txt) as GRanges object
- CpG island annotation BED file (e.g., cpgi.hg18.bed.txt) as GRanges object
Outputs
- Percentage overlap table classifying differentially methylated bases by promoter/exon/intron features
- Percentage overlap table classifying differentially methylated bases by CpG island vs. shore context
- Summary statistics matching vignette format with counts and percentages per feature class
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 · 104 lines · 27 tokens per session scan A 2ff6280fe2c8
genomic-feature-annotation-overlap is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 27 tokens to every session and 1,641 once invoked, about $0.0001 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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