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 networkgit 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/network)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/network"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/network/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/network"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/network.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 | $0.00017 | $0.01030 |
| Opus 5 | $0.00009 | $0.00515 |
| Sonnet 5 | $0.00003 | $0.00206 |
| Haiku 4.5 | $0.00002 | $0.00103 |
Grade A, and why
network 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
network
Classes for relational data.
Creating Networks
library(network)
# From edge list
edges <- matrix(c(1, 2, 1, 3, 2, 3, 3, 4), ncol = 2, byrow = TRUE)
net <- network(edges, directed = TRUE)
# From adjacency matrix
adj <- matrix(c(0, 1, 1, 0,
0, 0, 1, 0,
0, 0, 0, 1,
0, 0, 0, 0), nrow = 4, byrow = TRUE)
net <- network(adj, directed = TRUE)
# Empty network
net <- network.initialize(10, directed = FALSE)
Network Properties
# Number of vertices
network.size(net)
# Number of edges
network.edgecount(net)
# Is directed?
is.directed(net)
# Is bipartite?
is.bipartite(net)
# Density
network.density(net)
Vertex Attributes
# Set vertex attribute
set.vertex.attribute(net, "name", c("A", "B", "C", "D"))
net %v% "name" <- c("A", "B", "C", "D")
# Get vertex attribute
get.vertex.attribute(net, "name")
net %v% "name"
# List all vertex attributes
list.vertex.attributes(net)
Edge Attributes
# Set edge attribute
set.edge.attribute(net, "weight", c(1, 2, 3, 4))
net %e% "weight" <- c(1, 2, 3, 4)
# Get edge attribute
get.edge.attribute(net, "weight")
net %e% "weight"
# List all edge attributes
list.edge.attributes(net)
Network Attributes
# Set network attribute
set.network.attribute(net, "title", "My Network")
net %n% "title" <- "My Network"
# Get network attribute
get.network.attribute(net, "title")
net %n% "title"
Adding/Removing Edges
# Add edges
add.edges(net, tail = c(1, 2), head = c(4, 4))
# Add edge with attributes
add.edges(net, tail = 1, head = 5, names.eval = "weight", vals.eval = 3)
# Delete edges
delete.edges(net, eid = 1)
# Add vertices
add.vertices(net, nv = 2)
# Delete vertices
delete.vertices(net, vid = c(1, 2))
Subsetting
# Get neighbors
get.neighborhood(net, v = 1, type = "out")
get.neighborhood(net, v = 1, type = "in")
get.neighborhood(net, v = 1, type = "combined")
# Get edge IDs
get.edgeIDs(net, v = 1, alter = 2)
# Subnetwork
subnet <- get.inducedSubgraph(net, v = c(1, 2, 3))
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 · 201 lines · 17 tokens per session scan A 0e87be78e34b
network is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 1,030 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 2026-09-03.
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