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NetworkX Graph Analysis

A reference skill that guides Claude to create, analyze, and visualize networks with Python's NetworkX.

Data & AnalyticsIntermediate★ 33,030⑂ 3,248AI score 8/10Last updated: Aug 9, 2026

What it does

A reference skill that helps Claude use the NetworkX library correctly and idiomatically.

  • Graph construction: Graph, DiGraph, MultiGraph, MultiDiGraph plus node/edge attribute handling
  • Algorithms: shortest paths (Dijkstra), centrality (degree/betweenness/closeness/PageRank), clustering, community detection, max flow, minimum spanning trees
  • Generators: Erdős–Rényi random, Barabási–Albert scale-free, Watts–Strogatz small-world, lattices, random trees
  • I/O: edgelist, GraphML, GML, JSON node-link, Pandas DataFrames, NumPy/SciPy matrices
  • Visualization: spring/circular/Kamada-Kawai/spectral layouts, centrality-driven node sizes and colors, publication-quality export

It targets NetworkX 3.x, explicitly avoiding removed APIs (nx.info, nx.write_gpickle, nx.random_tree) and covering accelerated backends such as nx-cugraph (GPU) and nx-parallel (multicore). Five bundled reference files cover basics, algorithms, generators, I/O, and visualization in depth.

Who it's for

  • Researchers and analysts working on social, citation, or knowledge graphs
  • Bioinformaticians handling protein-interaction or metabolic networks
  • Engineers analyzing routing and bottlenecks in transportation or logistics networks
  • Anyone needing clean network figures for papers and reports

Example uses

  1. Community detection: "Load this follow-relationship CSV into a DiGraph, export the top 20 nodes by PageRank and greedy-modularity communities to CSV."
  2. Path analysis: "Build a weighted graph from station adjacency data, find the fastest route from A to B and list transfer hubs with high betweenness."
  3. Visualization: "Read this protein-interaction GraphML, draw a spring layout with node size by degree and color by community, and save 300dpi PNG plus PDF."

· · · Install guide · · ·

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/networkx folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/networkx/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/networkx ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Make sure Git, Python 3.11+, and Claude Code are installed.
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Create the skills folder if needed: mkdir -p ~/.claude/skills
  4. Copy the skill in: cp -r scientific-agent-skills/skills/networkx ~/.claude/skills/
  5. Install dependencies: pip install "networkx[default]" matplotlib pandas
  6. Restart Claude Code and try: "Use NetworkX to compute centrality for this edgelist file."
  7. If the skill doesn't trigger automatically, say "use the networkx skill" in your prompt.
  8. For very large graphs, optionally install the nx-parallel or nx-cugraph backend.
View source on GitHub ↗License: MIT