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/edgernpx skills add LeoLin990405/r-analytics-skill --skill edgergit 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/edger)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/edger"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/edger.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 | $0.00023 | $0.00442 |
| Opus 5 | $0.00012 | $0.00221 |
| Sonnet 5 | $0.00005 | $0.00088 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
edgeR scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
edgeR Package
Differential expression analysis for RNA-seq.
Basic Workflow
library(edgeR)
# Create DGEList
dge <- DGEList(counts = count_matrix, group = group)
# Filter low counts
keep <- filterByExpr(dge)
dge <- dge[keep, , keep.lib.sizes = FALSE]
# Normalize
dge <- calcNormFactors(dge)
# Estimate dispersion
dge <- estimateDisp(dge)
# Exact test (two groups)
et <- exactTest(dge)
topTags(et)
GLM Approach
# Design matrix
design <- model.matrix(~0 + group)
colnames(design) <- levels(group)
# Estimate dispersion
dge <- estimateDisp(dge, design)
# Fit GLM
fit <- glmQLFit(dge, design)
# Contrast
contrast <- makeContrasts(TreatmentA - Control, levels = design)
qlf <- glmQLFTest(fit, contrast = contrast)
topTags(qlf)
Results
# Top genes
results <- topTags(qlf, n = Inf)$table
# Significant genes
sig <- results[results$FDR < 0.05, ]
# Volcano plot
plotMD(qlf)
# MA plot
plotSmear(qlf)
Normalization
# TMM (default)
dge <- calcNormFactors(dge, method = "TMM")
# Other methods
dge <- calcNormFactors(dge, method = "RLE")
dge <- calcNormFactors(dge, method = "upperquartile")
# Get normalized counts
cpm(dge)
rpkm(dge, gene.length = gene_lengths)
MDS Plot
plotMDS(dge, col = as.numeric(group))
Export
write.csv(topTags(qlf, n = Inf)$table, "de_results.csv")
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.
- 5d ago First seen · 94 lines · 23 tokens per session scan A 8c84707a3dc9
edgeR is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 23 tokens to every session and 442 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.
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