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 statnetgit 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/statnet)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/statnet"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/statnet/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/statnet"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/statnet.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 | $0.00022 | $0.01076 |
| Opus 5.5 | $0.00009 | $0.00430 |
| Sonnet 5.5 | $0.00004 | $0.00215 |
| Haiku 4.5 | $0.00002 | $0.00108 |
Grade A, and why
statnet 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 1mo 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
statnet
Statistical analysis of network data.
Installation
# Install statnet suite
install.packages("statnet")
# Load
library(statnet)
# Loads: network, sna, ergm, tergm, networkDynamic, etc.
Network Descriptives (sna)
library(sna)
# Centrality measures
degree(net)
degree(net, cmode = "indegree")
degree(net, cmode = "outdegree")
betweenness(net)
closeness(net)
evcent(net) # Eigenvector centrality
# Network-level measures
gden(net) # Density
grecip(net) # Reciprocity
gtrans(net) # Transitivity
centralization(net, degree)
ERGM (Exponential Random Graph Models)
library(ergm)
# Fit basic ERGM
fit <- ergm(net ~ edges)
# With structural terms
fit <- ergm(net ~ edges + mutual + triangle)
# With node attributes
fit <- ergm(net ~ edges + nodematch("gender") + nodecov("age"))
# With edge attributes
fit <- ergm(net ~ edges + edgecov(covariate_matrix))
# Summary
summary(fit)
ERGM Terms
# Structural terms
edges # Number of edges
mutual # Mutual ties (reciprocity)
triangle # Triangles
gwesp(0.5) # Geometrically weighted ESP
gwdegree(0.5) # Geometrically weighted degree
kstar(2) # 2-stars
# Attribute terms
nodematch("attr") # Homophily
nodemix("attr") # Mixing patterns
nodecov("attr") # Node covariate (continuous)
nodefactor("attr") # Node factor (categorical)
absdiff("attr") # Absolute difference
# Dyadic terms
edgecov(matrix) # Edge covariate
ERGM Diagnostics
# MCMC diagnostics
mcmc.diagnostics(fit)
# Goodness of fit
gof_result <- gof(fit)
plot(gof_result)
# Simulate from model
sim_nets <- simulate(fit, nsim = 100)
TERGM (Temporal ERGM)
library(tergm)
# Create network list
net_list <- list(net_t1, net_t2, net_t3)
# Fit STERGM (separable temporal ERGM)
fit <- stergm(
net_list,
formation = ~ edges + mutual,
dissolution = ~ edges,
estimate = "CMLE"
)
# Summary
summary(fit)
Network Visualization (sna)
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.
- 1mo ago First seen · 207 lines · 22 tokens per session scan A 437c041127c4
statnet is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 7mo ago), licensed MIT. It adds 22 tokens to every session and 1,076 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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