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 dna-methylation-block-detection-analysisgit 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/dna-methylation-block-detection-analysis)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/dna-methylation-block-detection-analysis"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/dna-methylation-block-detection-analysis/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/dna-methylation-block-detection-analysis"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/dna-methylation-block-detection-analysis.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 | $0.00046 | $0.01486 |
| Opus 5 | $0.00023 | $0.00743 |
| Sonnet 5 | $0.00009 | $0.00297 |
| Haiku 4.5 | $0.00005 | $0.00149 |
Grade A, and why
dna-methylation-block-detection-analysis 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dna-methylation-block-detection-analysis
Summary
Detection and visualization of differentially methylated blocks (DMBs) in EPIC and 450k DNA methylation array data using ChAMP's champ.Block() function. This skill identifies contiguous genomic regions with coordinated differential methylation patterns that may represent functional regulatory units.
When to use
Apply this skill when you have loaded normalized methylation data from EPIC or 450k arrays and need to identify differentially methylated blocks rather than individual CpG sites or DMRs. Use it after quality control, normalization, and batch correction are complete, and when you have defined comparison groups (case vs. control) in your sample metadata.
When NOT to use
- Input data has not been normalized and batch-corrected; quality control and preprocessing must precede block detection
- You need single-CpG-level differential methylation analysis rather than regional/block-level patterns—use probe-level DMR detection (champ.DMR with Probe Lasso, Bumphunter, or DMRcate) instead
- Sample size is very small (< 4 samples per group) or phenotype groups are not clearly defined in metadata
Inputs
- Normalized beta-value matrix (numeric matrix with CpG probes as rows, samples as columns)
- ExpressionSet object containing methylation data and sample metadata
- Sample phenotype/group labels (e.g., case/control assignments)
Outputs
- Block detection results table (genomic coordinates, effect sizes, p-values)
- Block.GUI() interactive visualization interface
- Differentially methylated block annotations with probe ranges and statistical summaries
How to apply
Load the preprocessed methylation dataset (beta-value matrix or ExpressionSet object) into the R environment. Call champ.Block() with the appropriate arraytype parameter (either 'EPIC' or '450K') to detect contiguous blocks of differential methylation across your sample groups. The function will perform block-level statistical testing to identify regions where multiple adjacent probes show coordinated methylation differences. Launch the Block.GUI() interactive visualization interface to inspect, filter, and explore detected blocks by genomic location, effect size, and statistical significance. Verify output by checking that block boundaries align with genomic features and that the number and magnitude of detected blocks are consistent with the expected biology of your comparison (note: negative results—absence of blocks—may be valid for certain datasets, particularly simulation data).
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 · 99 lines · 46 tokens per session scan A c247ed607031
dna-methylation-block-detection-analysis is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 46 tokens to every session and 1,486 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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