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-bio-genomicsgit 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-genomics)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/r-bio-genomics"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/r-bio-genomics/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-genomics"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/r-bio-genomics.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 | $0.00028 | $0.00486 |
| Opus 5 | $0.00014 | $0.00243 |
| Sonnet 5 | $0.00006 | $0.00097 |
| Haiku 4.5 | $0.00003 | $0.00049 |
Grade A, and why
r-bio-genomics 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 10d 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.
What it actually says
R Genomics
Genomic data structures and operations.
GenomicRanges
library(GenomicRanges)
# Create GRanges
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)
)
# Accessors
seqnames(gr); start(gr); end(gr); width(gr); strand(gr)
mcols(gr) # Metadata columns
# Operations
shift(gr, 10)
resize(gr, width = 100)
flank(gr, width = 50)
reduce(gr) # Merge overlapping
# Overlaps
findOverlaps(gr1, gr2)
subsetByOverlaps(gr1, gr2)
countOverlaps(gr1, gr2)
# Set operations
union(gr1, gr2)
intersect(gr1, gr2)
setdiff(gr1, gr2)
Biostrings
library(Biostrings)
# DNA sequences
dna <- DNAStringSet(c("ATCGATCG", "GCTAGCTA"))
reverseComplement(dna)
translate(dna)
# Pattern matching
matchPattern("ATG", dna[[1]])
vmatchPattern("ATG", dna)
countPattern("ATG", dna)
# Alignment
pairwiseAlignment(pattern, subject, type = "global")
Annotation
library(org.Hs.eg.db)
# Map IDs
mapIds(org.Hs.eg.db, keys = genes, column = "SYMBOL", keytype = "ENSEMBL")
# Select
select(org.Hs.eg.db, keys = genes, columns = c("SYMBOL", "GENENAME"), keytype = "ENSEMBL")
# TxDb for transcripts
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
genes(txdb)
transcripts(txdb)
exons(txdb)
What ships with it
5 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.
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.
- 10d ago First seen · 81 lines · 28 tokens per session scan A 96225926751b
r-bio-genomics is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 486 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-bio-data-formats
Parse/write FASTA, FASTQ, SAM/BAM, VCF, BED, GFF/GTF with pysam and pure Python; decode SAM FLAG/CIGAR; reconcile 0-based vs 1-based coordinates. Use for custom format parsers or off-by-one coordinate bugs.
bio-applied-mageck-gene-essentiality
Run MAGeCK count/test on pooled CRISPR sgRNA screens, scoring gene essentiality via RRA, FDR, and log2 fold-change. Use when analyzing CRISPR screen FASTQ/count data, calling essential or drug-resistance genes, or benchmarking vs DepMap.
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.
bio-applied-network-modules
Detect PPI/co-expression modules with NetworkX/python-louvain/leidenalg (Louvain, Leiden, modularity Q) and WGCNA eigengenes. Use when clustering a gene network, computing WGCNA modules, or testing DEG/pathway enrichment on network communities.
bio-applied-ribo-seq
Ribo-seq: cutadapt/bowtie2 adapter+rRNA removal, plastid P-site calibration, 3-nt periodicity QC, RiboCode/ribotricer ORF calling, translation efficiency. Use when user has ribosome profiling or footprint data.
bio-core-computational-genetics
Translate DNA per-frame, score codon usage bias (RSCU/CAI), simulate restriction digests/ORFs, and test three-point-cross mapping and Hardy-Weinberg equilibrium. Use for CAI, virtual digests, crossover mapping, or HWE tests.