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-quality-controlgit 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-quality-control)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/dna-methylation-quality-control"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/dna-methylation-quality-control/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-quality-control"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/dna-methylation-quality-control.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 | $0.01459 |
| Opus 5 | $0.00021 | $0.00730 |
| Sonnet 5 | $0.00008 | $0.00292 |
| Haiku 4.5 | $0.00004 | $0.00146 |
Grade A, and why
dna-methylation-quality-control 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-quality-control
Summary
Apply detection p-value and bead count thresholds to remove low-quality probes from Illumina methylation array data (450K or EPIC). This filtering step is essential for downstream analysis, removing probes with insufficient signal reliability before normalization and differential methylation analysis.
When to use
Apply this skill immediately after loading raw .idat files or beta-value matrices from HumanMethylation450 or EPIC arrays when you need to exclude probes that fail quality control. Specifically, use it when your input dataset contains detection p-values and bead count information and you have not yet performed downstream analyses (normalization, batch correction, or DMR detection).
When NOT to use
- Input data has already been filtered by another pipeline or tool (detection p-values and bead counts no longer available)
- You are working with single-cell methylation data or non-array-based methods (WGBS, bisulfite sequencing)
- Your analysis explicitly requires retaining low-signal probes for specific methodological reasons
Inputs
- Raw .idat files from Illumina methylation array (450K or EPIC)
- Beta-value matrix with detection p-values and bead count data
- Sample metadata (phenotype information, batch labels)
Outputs
- Filtered probe count matrix (probes × samples)
- Quality control report documenting probe removal statistics
- Pre- and post-filter probe count comparison
- Bead count distribution plots
How to apply
Load the methylation array data using ChAMP data import functions (from .idat files or beta-valued matrix), then apply champ.filter() with default parameters. This function performs two successive filtering steps: (1) removal of probes with detection p-value > 0.01, and (2) removal of probes with fewer than 3 beads in at least 5% of samples per probe. Compare pre- and post-filter probe counts and examine bead count distributions to verify filtering efficacy. Document the number of probes retained and removed in a quality control report.
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 · 42 tokens per session scan A b23e3c01f95c
dna-methylation-quality-control 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 1,459 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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