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 methylation-region-genomic-context-assignmentgit 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/methylation-region-genomic-context-assignment)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/methylation-region-genomic-context-assignment"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/methylation-region-genomic-context-assignment/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/methylation-region-genomic-context-assignment"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/methylation-region-genomic-context-assignment.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 | $0.00054 | $0.01786 |
| Opus 5 | $0.00027 | $0.00893 |
| Sonnet 5 | $0.00011 | $0.00357 |
| Haiku 4.5 | $0.00005 | $0.00179 |
Grade A, and why
methylation-region-genomic-context-assignment 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
methylation-region-genomic-context-assignment
Summary
Assign differentially methylated bases and regions to genomic annotation features (promoters, exons, introns, intergenic regions) and CpG island contexts (CpG islands vs. shores) using overlap-based annotation functions. This skill quantifies the spatial distribution of methylation changes across functional and sequence-context categories.
When to use
After identifying differentially methylated bases or regions (via calculateDiffMeth() and getMethylDiff()), when you need to characterize WHERE these methylation changes occur relative to gene structure and CpG density landscapes. Use this skill to generate percentage-overlap tables that answer: Are hyper-methylated bases enriched in promoters or gene bodies? Do hyper-methylated regions cluster in CpG islands or shores?
When NOT to use
- Input methylDiff object has not been filtered by q-value and methylation difference thresholds — apply getMethylDiff() first to define the set of differentially methylated bases.
- Gene annotation or CpG island BED files are from a different genome build than the methylation data (e.g., mixing hg18 and hg19 coordinates) — coordinates will not match.
- You need single-base-resolution methylation calls without overlap-based aggregation — use raw methylRawList or methylBase objects directly instead.
Inputs
- methylDiff object (output from calculateDiffMeth() filtered by getMethylDiff())
- RefSeq gene annotation BED file (e.g., refseq.hg18.bed.txt) containing promoter, exon, intron coordinates
- CpG island annotation BED file (e.g., cpgi.hg18.bed.txt) with island and shore boundaries
Outputs
- Percentage overlap table: differentially methylated bases classified by gene annotation feature (promoter/exon/intron/intergenic)
- Percentage overlap table: differentially methylated bases classified by CpG context (island/shore)
- Summary statistics table with row counts and proportions matching vignette reference format
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 · 106 lines · 54 tokens per session scan A 59857799b9a5
methylation-region-genomic-context-assignment is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 54 tokens to every session and 1,786 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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