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 igraphgit 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/igraph)<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>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 | $0.01084 |
| Opus 5 | $0.00013 | $0.00542 |
| Sonnet 5 | $0.00005 | $0.00217 |
| Haiku 4.5 | $0.00003 | $0.00108 |
Grade A, and why
igraph scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
igraph
Network analysis and visualization.
Create Graphs
library(igraph)
# From edge list
g <- graph_from_edgelist(matrix(c(1,2, 2,3, 3,1), ncol = 2, byrow = TRUE))
# From data frame
g <- graph_from_data_frame(edges, directed = TRUE, vertices = nodes)
# From adjacency matrix
g <- graph_from_adjacency_matrix(adj_matrix)
g <- graph_from_adjacency_matrix(adj_matrix, mode = "undirected", weighted = TRUE)
# Special graphs
g <- make_empty_graph(n = 10)
g <- make_full_graph(n = 10)
g <- make_ring(n = 10)
g <- make_star(n = 10)
g <- make_tree(n = 15, children = 2)
g <- make_lattice(dimvector = c(5, 5))
# Random graphs
g <- sample_gnp(n = 100, p = 0.1)
g <- sample_gnm(n = 100, m = 200)
g <- sample_pa(n = 100, power = 1) # Preferential attachment
g <- sample_smallworld(dim = 1, size = 100, nei = 2, p = 0.1)
Graph Properties
# Basic
vcount(g) # Number of vertices
ecount(g) # Number of edges
is_directed(g)
is_weighted(g)
is_connected(g)
# Vertices and edges
V(g)
E(g)
V(g)$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)
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.
- 3d ago First seen · 194 lines · 26 tokens per session scan A 4c48965af47e
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.
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-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.
bio-core-computational-genetics
Translate DNA per-frame, score codon usage bias (RSCU/CAI), simulate restriction digests/ORFs, and test three-point-cross mapping and Hardy-Weinberg equilibrium. Use for CAI, virtual digests, crossover mapping, or HWE tests.