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-differential-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-differential-analysis)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/dna-methylation-differential-analysis"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/dna-methylation-differential-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-differential-analysis"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/dna-methylation-differential-analysis.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 | $0.00054 | $0.02177 |
| Opus 5 | $0.00027 | $0.01089 |
| Sonnet 5 | $0.00011 | $0.00435 |
| Haiku 4.5 | $0.00005 | $0.00218 |
Grade A, and why
dna-methylation-differential-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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dna-methylation-differential-analysis
Summary
Identify and quantify differentially methylated bases or regions between sample groups using statistical tests that account for methylation heterogeneity and overdispersion. This skill applies Fisher's exact test or logistic regression to bisulfite sequencing methylation calls, filtered by q-value and percent methylation difference thresholds, to distinguish hyper-methylated from hypo-methylated loci.
When to use
Apply this skill when you have merged methylation call data across multiple biological replicates (samples per group ≥2) with base-pair-level coverage information, and you need to identify loci where methylation levels differ significantly between treatment groups. This is the core comparative analysis step in bisulfite sequencing workflows after quality filtering and sample merging.
When NOT to use
- Input data lacks technical replicates or biological replication structure—statistical tests require ≥2 samples per group to estimate variance.
- Bases have not been pre-filtered for minimum coverage (typically 10X default in methRead())—low-coverage bases produce unreliable methylation percentages.
- You seek to analyze regional methylation (e.g., DMRs across promoters) rather than base-resolution differential calls—use regional or tiling window methods instead.
Inputs
- methylBase object (merged sample data from unite() with base-level coverage and methylation percentages)
- methylRawList objects (optional, for overdispersion estimation; generated by methRead() from bisulfite alignment outputs)
Outputs
- methylDiff object containing all bases with calculated test statistics and q-values
- hyper-methylated bases subset (type='hyper' from getMethylDiff())
- hypo-methylated bases subset (type='hypo' from getMethylDiff())
- numerical counts and percentages of significant bases by direction
How to apply
Execute the calculateDiffMeth() function on a methylBase object (created by merging samples with unite()) to compute differential methylation statistics. The function automatically selects Fisher's exact test for small sample sizes or logistic regression for larger cohorts. For studies showing overdispersion (variance exceeding binomial expectations typical in methylation data), apply the overdispersion='MN' parameter, which calculates a scaling factor φ = X²/(N-P) to adjust variance as φ·n_i·π̂_i·(1-π̂_i) and switches to an F-test, producing more stringent (higher) q-values. Extract differentially methylated bases using getMethylDiff() with dual filtering: q-value threshold (typically < 0.01) and percent methylation difference cutoff (typically > 25%). Separate hyper-methylated (high methylation in treatment) from hypo-methylated (low methylation in treatment) bases using the type parameter to assess directional changes.
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 · 107 lines · 54 tokens per session scan A 53968382f9c0
dna-methylation-differential-analysis is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 54 tokens to every session and 2,177 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.
Other skills, from other repositories
gsva-analysis-and-visualization
Use this skill to run GSVA or ssGSEA pathway-level differential analysis from a bulk expression matrix and a sample group file, then generate a heatmap from the saved GSVA result object. Trigger keywords: GSVA, ssGSEA, pathway enrichment, KEGG pathway analysis, MSigDB. NOT for: gene-level differential expression…
medical-research-literature-reader-pro
A medical-research-native literature reading skill for users with clinical, bioinformatics, translational, and basic experimental backgrounds. Use this skill whenever a user wants to read, analyze, critique, or interpret a medical or scientific paper — whether they provide a PDF, abstract, DOI, PMID, or just a title.…
adverse-event-narrative
Generates CIOMS I-compliant ICSR narratives from adverse event case data for FDA and EMA regulatory submission. Includes temporal analysis, MedDRA coding, causality assessment using WHO-UMC or Naranjo criteria, and multi-format output.
anatomy-quiz-master
Generate interactive anatomy quizzes for medical education with multiple.
decision-curve-analysis
Use when evaluating the clinical utility of a binary prediction model from a single clinical CSV file by fitting a logistic decision-curve model, plotting decision and clinical-impact curves, and exporting summary outputs. NOT for: survival calibration, ROC-only discrimination analysis, nomogram construction, or…
elastic-net-feature-selection
Use when selecting predictive genes or other molecular features from bulk expression matrices for binary case-vs-control classification with elastic net logistic regression, including coefficient path and cross-validation plots. Trigger keywords: elastic net, glmnet, feature selection, binary classification…