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 overdispersion-correction-applicationgit 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/overdispersion-correction-application)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/overdispersion-correction-application"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/overdispersion-correction-application/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/overdispersion-correction-application"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/overdispersion-correction-application.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 | $0.00055 | $0.01787 |
| Opus 5 | $0.00028 | $0.00894 |
| Sonnet 5 | $0.00011 | $0.00357 |
| Haiku 4.5 | $0.00006 | $0.00179 |
Grade A, and why
overdispersion-correction-application 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
overdispersion-correction-application
Summary
Apply overdispersion correction in methylKit's calculateDiffMeth() function using the 'MN' (Methylation-specific Negative Binomial) parameter to adjust for variance in excess of binomial expectations, producing more stringent p-value and q-value distributions in differential methylation analysis. This skill is essential when methylation count data exhibit greater variance than expected under a binomial model, which is common in bisulfite sequencing due to biological and technical sources of variation.
When to use
Apply this skill when analyzing differential methylation from bisulfite sequencing data where you suspect overdispersion (variance exceeds binomial expectations), or when comparing uncorrected and corrected statistical tests to determine whether more stringent thresholds are justified by the data. Use it specifically when working with methylBase objects from methylKit and seeking to adjust variance estimates as φ·n_i·π̂_i·(1-π̂_i), where φ = X²/(N-P) is the scaling parameter computed from Chi-square test residuals.
When NOT to use
- When input data are already aggregated at regional or tiling-window level rather than single-base resolution; overdispersion correction is designed for individual base-pair methylation counts.
- When sample size is very small (< 3 replicates per group); the overdispersion parameter estimation becomes unstable with insufficient degrees of freedom.
- When the research question explicitly requires nominal p-values or when multiple-testing correction has already been applied at an earlier filtering stage (e.g., pre-filtering to high-variance sites).
Inputs
- methylBase object (merged methylation data across samples at consistent genomic positions)
- Sample grouping/treatment assignment for differential methylation comparison
Outputs
- Differential methylation results with variance-corrected test statistics and q-values
- Overdispersion scaling parameter φ (X²/(N-P))
- Q-value distribution (higher in corrected vs. uncorrected analyses)
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 · 97 lines · 55 tokens per session scan A 7d290fddf1b2
overdispersion-correction-application is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 55 tokens to every session and 1,787 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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