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 agentmods add skills/leolin990405/r-analytics-skill/bioconductornpx skills add LeoLin990405/r-analytics-skill --skill bioconductorgit 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/bioconductor)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/bioconductor"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/bioconductor.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.00536 |
| Opus 5 | $0.00013 | $0.00268 |
| Sonnet 5 | $0.00005 | $0.00107 |
| Haiku 4.5 | $0.00003 | $0.00054 |
Grade A, and why
Bioconductor 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.
What it actually says
Bioconductor
Open source software for bioinformatics.
Installation
# Install BiocManager
install.packages("BiocManager")
# Install Bioconductor packages
BiocManager::install("GenomicRanges")
BiocManager::install(c("DESeq2", "edgeR"))
# Check version
BiocManager::version()
# Update packages
BiocManager::install()
Core Packages
# Genomic ranges
library(GenomicRanges)
library(IRanges)
# Sequences
library(Biostrings)
# Annotations
library(AnnotationDbi)
library(org.Hs.eg.db)
# RNA-seq
library(DESeq2)
library(edgeR)
GenomicRanges
library(GenomicRanges)
# Create GRanges
gr <- GRanges(
seqnames = c("chr1", "chr1", "chr2"),
ranges = IRanges(start = c(1, 100, 200), end = c(50, 150, 250)),
strand = c("+", "-", "+")
)
# Operations
findOverlaps(gr1, gr2)
subsetByOverlaps(gr1, gr2)
reduce(gr)
Biostrings
library(Biostrings)
# DNA sequences
dna <- DNAString("ATCGATCG")
reverseComplement(dna)
translate(dna)
# Pattern matching
matchPattern("ATG", dna)
vmatchPattern("ATG", dna_set)
Annotation
library(org.Hs.eg.db)
# Map gene IDs
mapIds(org.Hs.eg.db,
keys = gene_ids,
column = "SYMBOL",
keytype = "ENTREZID")
# Available columns
columns(org.Hs.eg.db)
keytypes(org.Hs.eg.db)
SummarizedExperiment
library(SummarizedExperiment)
# Create
se <- SummarizedExperiment(
assays = list(counts = count_matrix),
colData = sample_info,
rowData = gene_info
)
# Access
assay(se)
colData(se)
rowData(se)
Finding Packages
# Search for packages
BiocManager::available("RNA")
# Package info
BiocManager::install("BiocPkgTools")
library(BiocPkgTools)
biocPkgList()
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 · 124 lines · 26 tokens per session scan A 6afbafc69908
Bioconductor is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 26 tokens to every session and 536 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-cancer-transcriptomics
Classify tumor RNA-seq into subtypes (melanoma Tirosh/Harbst on TCGA-SKCM): log1p/z-score, PCA/t-SNE, hierarchical clustering, random forest, Kaplan-Meier survival. Use when subtyping cBioPortal expression data.
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.