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-hyper-hypo-classificationgit 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-hyper-hypo-classification)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/methylation-hyper-hypo-classification"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/methylation-hyper-hypo-classification/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-hyper-hypo-classification"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/methylation-hyper-hypo-classification.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 | $0.00067 | $0.01668 |
| Opus 5 | $0.00034 | $0.00834 |
| Sonnet 5 | $0.00013 | $0.00334 |
| Haiku 4.5 | $0.00007 | $0.00167 |
Grade A, and why
methylation-hyper-hypo-classification 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.
methylation-hyper-hypo-classification
Summary
Classify differentially methylated bases into hyper-methylated and hypo-methylated subsets using getMethylDiff() with statistical and effect-size cutoffs. This skill extracts and counts methylation changes between sample groups to identify loci where methylation is significantly elevated or reduced.
When to use
After calculating differential methylation across samples using calculateDiffMeth(), when you need to separately enumerate and extract hyper-methylated (increased methylation) versus hypo-methylated (decreased methylation) bases that meet both statistical significance (q-value < 0.01) and biological relevance (≥25% methylation difference) thresholds.
When NOT to use
- Methylation data has not yet been merged across all samples using unite() or does not contain coverage in all replicates.
- You are working with non-CpG cytosine methylation (e.g., CHG or CHH contexts in plants) without verifying that your q-value and effect-size thresholds remain appropriate.
- Input methylation percentages are binary (fully methylated or unmethylated) rather than continuous; the q-value calculation and effect-size filtering assume heterogeneous methylation percentages.
Inputs
- methylDiff object (output from calculateDiffMeth())
Outputs
- methylDiff object filtered for hyper-methylated bases (type='hyper')
- methylDiff object filtered for hypo-methylated bases (type='hypo')
- Integer counts of hyper-methylated and hypo-methylated bases
How to apply
Apply getMethylDiff() to a methylDiff object produced by calculateDiffMeth(), specifying q-value = 0.01 and percent methylation difference = 25% as joint filtering thresholds. The function automatically selects the appropriate statistical test (Fisher's exact test for small sample sizes, logistic regression for larger cohorts) based on sample count. Extract hyper-methylated bases by setting type='hyper' and hypo-methylated bases with type='hypo', producing separate methylDiff objects. Validate results by confirming that returned base counts align with expected methylation directionality from your experimental design (e.g., treatment vs. control groups).
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 · 67 tokens per session scan A 6c7f63ef8d09
methylation-hyper-hypo-classification is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 67 tokens to every session and 1,668 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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