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 LeoLin990405/r-analytics-skill --skill r-biogit clone --depth 1 https://github.com/LeoLin990405/r-analytics-skillWrote 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/leolin990405/r-analytics-skill/r-bio)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/r-bio"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/r-bio/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/leolin990405/r-analytics-skill/r-bio"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/r-bio.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00029 | $0.01138 |
| Opus 5 | $0.00015 | $0.00569 |
| Sonnet 5 | $0.00006 | $0.00228 |
| Haiku 4.5 | $0.00003 | $0.00114 |
Grade A, and why
r-bio 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
R Bioinformatics Skill
Sub-skills
| Sub-skill | Description |
|---|---|
| r-bio-genomics | GenomicRanges, Biostrings, annotation |
| r-bio-rnaseq | DESeq2, edgeR, differential expression |
| r-bio-phylo | ape, ggtree, phylogenetics |
Bioinformatics and biostatistics in R.
Core Packages
| Package | Description |
|---|---|
| Bioconductor ★ | Genomic data analysis platform |
| GenomicRanges | Genomic intervals |
| Biostrings | DNA/RNA/protein sequences |
| SummarizedExperiment | Assay data container |
Genetics & Phylogenetics
| Package | Description |
|---|---|
| genetics | Genetic data handling |
| gap | Genetic data analysis |
| ape | Phylogenetics and evolution |
| ggtree | Phylogenetic tree visualization |
Mixed Effects (Biostatistics)
| Package | Description |
|---|---|
| lme4 ★ | Mixed-effects models |
| nlme | Mixed-effects with custom covariance |
| glmmTMB | Generalized mixed-effects |
Visualization
| Package | Description |
|---|---|
| pheatmap | Pretty heatmaps |
| ComplexHeatmap | Advanced heatmaps |
| EnhancedVolcano | Volcano plots |
Quick Examples
# Install Bioconductor
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install()
# Install packages
BiocManager::install(c("DESeq2", "edgeR", "GenomicRanges"))
# GenomicRanges
library(GenomicRanges)
gr <- GRanges(
seqnames = c("chr1", "chr1", "chr2"),
ranges = IRanges(start = c(100, 200, 150), width = 50),
strand = c("+", "-", "+"),
score = c(1.5, 2.0, 3.0)
)
findOverlaps(gr1, gr2)
subsetByOverlaps(gr1, gr2)
# RNA-seq with DESeq2
library(DESeq2)
dds <- DESeqDataSetFromMatrix(
countData = counts,
colData = sample_info,
design = ~ condition
)
dds <- DESeq(dds)
res <- results(dds, contrast = c("condition", "treated", "control"))
sig <- res[which(res$padj < 0.05), ]
# Volcano plot
library(EnhancedVolcano)
EnhancedVolcano(res,
lab = rownames(res),
x = 'log2FoldChange',
y = 'pvalue',
pCutoff = 0.05,
FCcutoff = 1
)
# Heatmap
library(pheatmap)
pheatmap(mat,
scale = "row",
clustering_distance_rows = "correlation",
annotation_col = annotation
)
# Phylogenetic analysis
library(ape)
tree <- read.tree("tree.nwk")
plot(tree)
# Gene annotation
library(org.Hs.eg.db)
mapIds(org.Hs.eg.db,
keys = gene_ids,
column = "SYMBOL",
keytype = "ENSEMBL"
)
What ships with it
17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- r-bio-genomics/Bioconductor/SKILL.md 1.8 KB
- r-bio-genomics/Biostrings/SKILL.md 1.7 KB
- r-bio-genomics/GenomicRanges/SKILL.md 1.5 KB
- r-bio-genomics/pheatmap/SKILL.md 3.5 KB
- r-bio-genomics/seqinr/SKILL.md 1.7 KB
- r-bio-genomics/SKILL.md 1.5 KB
- r-bio-phylo/ape/SKILL.md 3.0 KB
- r-bio-phylo/phangorn/SKILL.md 1.8 KB
- r-bio-phylo/SKILL.md 1.3 KB
- r-bio-rnaseq/DESeq2/SKILL.md 2.7 KB
- r-bio-rnaseq/edgeR/SKILL.md 1.5 KB
- r-bio-rnaseq/limma/SKILL.md 1.8 KB
- r-bio-rnaseq/SKILL.md 1.5 KB
- sub-skills/r-bio-genomics/sub-skills/Biostrings/SKILL.md 1.7 KB
- sub-skills/r-bio-genomics/sub-skills/GenomicRanges/SKILL.md 1.5 KB
- sub-skills/r-bio-rnaseq/sub-skills/edgeR/SKILL.md 1.5 KB
- sub-skills/r-bio-rnaseq/sub-skills/limma/SKILL.md 1.8 KB
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 · 171 lines · 29 tokens per session scan A b0c8d79a5e06
r-bio is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 29 tokens to every session and 1,138 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
bio-applied-molecular-evolution
Test Hardy-Weinberg equilibrium, simulate Wright-Fisher drift/selection, and compute dN/dS, Tajima's D, and Fst with NumPy/SciPy. Use for neutral theory, molecular clock divergence time, selection scans, or effective population size (Ne) questions.
advanced-string-structures
Build tries, Aho-Corasick, and suffix arrays with Kasai LCP to index DNA/text and match many patterns in one pass. Use for genome motif scanning, k-mer indexing, longest-repeat search, or BWA/FM-index groundwork.
ai-science-esm2-embeddings
Generate ESM2 protein embeddings (fair-esm/transformers) and predict structure with ESMFold. Use when embedding sequences, scoring mutations zero-shot, annotating protein function, or doing fast MSA-free structure prediction.
ai-science-geneformer-scgpt
Tokenize scRNA-seq via Geneformer gene-rank or scGPT expression-bin encoding; annotate cell types, simulate in-silico knockouts. Use for foundation-model cell annotation, Geneformer/scGPT tokenization, or perturbation prediction.
ai-science-zero-shot-mutation
Score protein point mutations zero-shot with ESM-1v/ESM-2 masked-LM log-odds, ensembled, benchmarked on ProteinGym DMS. Use when predicting mutation effects, ranking missense variants, scoring VUS fitness with no labels.
bio-applied-advanced-ngs
Assemble genomes de novo: greedy OLC, de Bruijn graph/Eulerian path, N50/L50/NG50 stats, SPAdes/Flye/hifiasm CLI usage. Use when choosing k-mer size, picking an assembler for Illumina/ONT/HiFi reads, or scoring contiguity.