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 statistical-overlap-proportion-reportinggit 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/statistical-overlap-proportion-reporting)<a href="https://agentmods.dev/skills/holobiomicslab/asb-skill-collections/statistical-overlap-proportion-reporting"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/statistical-overlap-proportion-reporting/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/statistical-overlap-proportion-reporting"><img src="https://agentmods.dev/badge/skills/holobiomicslab/asb-skill-collections/statistical-overlap-proportion-reporting.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 | $0.00075 | $0.01941 |
| Opus 5 | $0.00037 | $0.00971 |
| Sonnet 5 | $0.00015 | $0.00388 |
| Haiku 4.5 | $0.00007 | $0.00194 |
Grade A, and why
statistical-overlap-proportion-reporting 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Overlap Proportion Reporting
Summary
Quantify and report the percentage distribution of differentially methylated bases across genomic feature classes (gene annotation: promoter/exon/intron; CpG context: island/shore) using overlap-based annotation functions. This skill generates proportion tables that classify methylation changes by their genomic location and regulatory context, enabling interpretation of where methylation differences occur in the genome.
When to use
After identifying differentially methylated bases (q-value < 0.01, methylation difference > 25%) using calculateDiffMeth(), use this skill to determine what fraction of those bases overlap with specific gene features (promoters, exons, introns) and CpG contexts (islands vs. shores). Apply when your research question requires reporting the genomic distribution of methylation changes relative to annotated regulatory and sequence features.
When NOT to use
- Input is raw methylation call files that have not yet been filtered by q-value and methylation difference thresholds — apply filtering first using getMethylDiff().
- Input is already a regional or tiling-window level summary rather than base-pair resolution methylation data — overlap-based annotation is designed for base-pair granularity.
- Gene annotation or CpG island BED files are missing or in incompatible genome coordinates (e.g., hg19 vs. hg18 mismatch) — coordinate systems must match the methylation data.
Inputs
- methylDiff object (from calculateDiffMeth() with q-value < 0.01 and methylation difference > 25% filtering)
- refseq gene annotation BED file (e.g., refseq.hg18.bed.txt) loaded as GRanges
- CpG island annotation BED file (e.g., cpgi.hg18.bed.txt) loaded as GRanges
Outputs
- Percentage overlap table: proportion of differentially methylated bases in promoter/exon/intron features
- Percentage overlap table: proportion of differentially methylated bases in CpG island/shore contexts
- Compiled summary table with both counts and percentages matching vignette-reported format
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 · 106 lines · 75 tokens per session scan A 06b069b6c832
statistical-overlap-proportion-reporting is a skill published in the GitHub repository HolobiomicsLab/asb-skill-collections (15 stars, last pushed 2d ago), licensed Apache-2.0. It adds 75 tokens to every session and 1,941 once invoked, about $0.0004 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.
Other skills, from other repositories
external-model-validation
Use when validating an existing prognostic risk signature on an external bulk expression cohort with survival outcomes, producing risk scores, Kaplan-Meier curves, risk distribution plots, heatmap, and time-dependent ROC curves. NOT for: model training, feature selection, nomogram construction, calibration analysis…
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…