igraph

igraph is a skill for Claude Code, Codex from LeoLin990405/r-analytics-skill. It costs 26 tokens per session (1,084 once invoked), scanned A, original, MIT.

An R package for creating, analysing, and visualising graphs, where graphs are collections of items joined by relationships. It supports paths, centrality, communities, and many graph types.

In plain words
What is it for?
Use it to build graphs from edge lists or matrices, find important nodes and groups, measure paths and connectivity, and draw networks.
Why use it?
It handles both basic graph operations and advanced network analysis in one package, so you can study structure without implementing the algorithms yourself.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to build graphs from edge lists or matrices, find important nodes and groups, measure paths and connectivity, and draw networks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/leolin990405/r-analytics-skill/igraph
Install

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.

Any agent
npx skills add LeoLin990405/r-analytics-skill --skill igraph
Clone the repo
git clone --depth 1 https://github.com/LeoLin990405/r-analytics-skill

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for igraph

README.md
[![agentmods](https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/igraph.svg)](https://agentmods.dev/skills/leolin990405/r-analytics-skill/igraph)
Your own site
<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/igraph"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/igraph.svg" alt="Measured on agentmods" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,084 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00026 $0.01084
Opus 5 $0.00013 $0.00542
Sonnet 5 $0.00005 $0.00217
Haiku 4.5 $0.00003 $0.00108

Measured 3d ago against content hash 4c48965af47e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

igraph 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 3d 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.

sub-skills/r-network/r-network-analysis/igraph/SKILL.md · 194 lines

How it starts

The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.

igraph

Network analysis and visualization.

Create Graphs

library(igraph)

# From edge list
g <- graph_from_edgelist(matrix(c(1,2, 2,3, 3,1), ncol = 2, byrow = TRUE))

# From data frame
g <- graph_from_data_frame(edges, directed = TRUE, vertices = nodes)

# From adjacency matrix
g <- graph_from_adjacency_matrix(adj_matrix)
g <- graph_from_adjacency_matrix(adj_matrix, mode = "undirected", weighted = TRUE)

# Special graphs
g <- make_empty_graph(n = 10)
g <- make_full_graph(n = 10)
g <- make_ring(n = 10)
g <- make_star(n = 10)
g <- make_tree(n = 15, children = 2)
g <- make_lattice(dimvector = c(5, 5))

# Random graphs
g <- sample_gnp(n = 100, p = 0.1)
g <- sample_gnm(n = 100, m = 200)
g <- sample_pa(n = 100, power = 1)  # Preferential attachment
g <- sample_smallworld(dim = 1, size = 100, nei = 2, p = 0.1)

Graph Properties

# Basic
vcount(g)  # Number of vertices
ecount(g)  # Number of edges
is_directed(g)
is_weighted(g)
is_connected(g)

# Vertices and edges
V(g)
E(g)
V(g)$name
E(g)$weight

# Neighbors
neighbors(g, v = 1)
neighbors(g, v = 1, mode = "out")
neighbors(g, v = 1, mode = "in")
neighbors(g, v = 1, mode = "all")

# Adjacency
adjacent_vertices(g, v = 1)
incident_edges(g, v = 1)
are_adjacent(g, v1 = 1, v2 = 2)

Centrality

# Degree
degree(g)
degree(g, mode = "in")
degree(g, mode = "out")

# Betweenness
betweenness(g)
edge_betweenness(g)

# Closeness
closeness(g)

# Eigenvector
eigen_centrality(g)$vector

# PageRank
page_rank(g)$vector

# Hub and authority
hub_score(g)$vector
authority_score(g)$vector

Community Detection

# Louvain
comm <- cluster_louvain(g)
membership(comm)
modularity(comm)

# Other algorithms
cluster_fast_greedy(g)
cluster_walktrap(g)
cluster_edge_betweenness(g)
cluster_label_prop(g)
cluster_infomap(g)
cluster_leiden(g)

# Compare communities
compare(comm1, comm2, method = "nmi")

Paths and Distances

# Shortest paths
shortest_paths(g, from = 1, to = 10)
all_shortest_paths(g, from = 1, to = 10)

# Distances
distances(g)
distances(g, v = 1)
mean_distance(g)

# Diameter
diameter(g)
get_diameter(g)

Read the full file on GitHub · 194 lines

Changes

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.

  1. 3d ago First seen · 194 lines · 26 tokens per session scan A 4c48965af47e

Subscribe to this mod's changes

igraph 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 1,084 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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