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 cpg-base-filtering-by-statistical-thresholdgit 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/cpg-base-filtering-by-statistical-threshold)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/cpg-base-filtering-by-statistical-threshold"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/cpg-base-filtering-by-statistical-threshold/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/cpg-base-filtering-by-statistical-threshold"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/cpg-base-filtering-by-statistical-threshold.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 | $0.00042 | $0.01450 |
| Opus 5 | $0.00021 | $0.00725 |
| Sonnet 5 | $0.00008 | $0.00290 |
| Haiku 4.5 | $0.00004 | $0.00145 |
Grade A, and why
cpg-base-filtering-by-statistical-threshold 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CpG base filtering by statistical threshold
Summary
Filter differentially methylated CpG bases from a methylDiff object using combined q-value and percent methylation difference thresholds to isolate statistically significant and biologically meaningful changes. This skill extracts hyper- and hypo-methylated base sets for downstream annotation and validation.
When to use
After calculateDiffMeth() has been run on a methylBase object and you have a methylDiff object with q-values and methylation difference estimates. Apply this skill when you need to reduce the set of all tested CpG bases to a high-confidence subset meeting both statistical significance (q-value < 0.01) and minimum effect size (≥25% methylation difference) criteria for validation or annotation.
When NOT to use
- Input is a raw methylation call file or methylRawList object that has not yet been merged and tested for differential methylation — use unite() and calculateDiffMeth() first.
- You have already applied sample-level filtering (coverage, PCR bias) but have not yet merged samples — call unite() before differential methylation testing.
- The analysis goal requires region-level rather than base-pair-level differential methylation — use regional or tiling window analysis functions instead.
Inputs
- methylDiff object (output from calculateDiffMeth with q-values and percent methylation differences already computed)
Outputs
- methylDiff object with hyper-methylated bases (type='hyper')
- methylDiff object with hypo-methylated bases (type='hypo')
How to apply
Call getMethylDiff() on a methylDiff object with q-value threshold of 0.01 and percent methylation difference cutoff of 25%, specifying type='hyper' or type='hypo' to extract separate base objects for each direction of change. The q-value threshold controls false discovery rate from Fisher's exact test or logistic regression (automatically selected based on sample size in calculateDiffMeth), while the 25% methylation difference threshold ensures only bases with substantial changes in methylation percentage are retained. Extract hyper-methylated bases (increased methylation) and hypo-methylated bases (decreased methylation) as separate methylDiff objects for parallel validation. Validate output by confirming the resulting hyper and hypo base counts match expected values from prior runs or published vignettes.
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 · 95 lines · 42 tokens per session scan A 267435655c38
cpg-base-filtering-by-statistical-threshold is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 42 tokens to every session and 1,450 once invoked, about $0.0002 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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