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 r-package-champ-usagegit 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/r-package-champ-usage)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/r-package-champ-usage"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/r-package-champ-usage/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/r-package-champ-usage"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/r-package-champ-usage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.01982 |
| Opus 5 | $0.00034 | $0.00991 |
| Sonnet 5 | $0.00013 | $0.00396 |
| Haiku 4.5 | $0.00007 | $0.00198 |
Grade A, and why
r-package-champ-usage 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 6d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
R package ChAMP usage
Summary
ChAMP is an R package for comprehensive DNA methylation array analysis, supporting HumanMethylation450 (450K) and EPIC array types from data loading through gene set enrichment analysis. Use this skill when you have .idat files or beta-valued methylation matrices and need to perform quality control, normalization, batch correction, and downstream statistical analysis.
When to use
When you have raw methylation array data (450K or EPIC format) in .idat files or as a beta-valued matrix and need to conduct a complete analysis pipeline including data import, quality filtering, normalization, batch effect correction, DMR detection, or gene set enrichment. Specifically use champ.load() when you need to verify pre-filter probe counts (485,512 for 450K arrays, 867,531 for EPIC arrays) before applying quality-based filtering.
When NOT to use
- Input data is already a fully normalized and filtered feature table from another pipeline (e.g., already processed by minfi, RnBeads, or IMA); ChAMP assumes raw or minimally processed .idat or beta matrix input.
- You are analyzing non-array methylation data (e.g., WGBS, targeted bisulfite sequencing) — ChAMP is designed specifically for Illumina beadarray platforms (450K and EPIC).
- Your data contains fewer probes than expected or is from an unsupported array type (e.g., mouse arrays require ChAMPdata to be installed separately).
Inputs
- .idat files (raw methylation array data for 450K or EPIC arrays)
- beta-valued matrix (methylation β-values)
- sample metadata and phenotype information
Outputs
- Filtered and normalized methylation matrix (probe × sample)
- Quality control plots and reports
- Batch-corrected methylation data
- DMR detection results
- Gene set enrichment analysis results
How to apply
Install ChAMP (version ≥2.29.1) along with ChAMPdata (≥2.23.1) from GitHub or Bioconductor. Load your methylation data using champ.load() or champ.import() functions, specifying the array type (450K or EPIC). Verify pre-filter probe counts match expected values for your array type. Apply quality control using ChAMP's QC functions, then proceed through the analysis pipeline: choose a Type-2 probe correction method (SWAN, PBC, or default BMIQ), apply Functional Normalization if needed, correct batch effects using SVD inspection and ComBat correction, adjust for cell-type heterogeneity via RefbaseEWAS if applicable, and finally perform DMR detection (Probe Lasso, Bumphunter, or DMRcate) or gene set enrichment analysis. The modular design allows selection of methods appropriate to your study design.
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.
- 6d ago First seen · 108 lines · 67 tokens per session scan A e71b4b9e3343
r-package-champ-usage 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 1,982 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 2026-09-06.
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