networkx
Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.
npx skills add K-Dense-AI/scientific-agent-skills --skill networkx --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# NetworkX ## Overview NetworkX is a Python package for creating, manipulating, and analyzing complex networks and graphs. Use this skill when working with network or graph data structures, including social networks, biological networks, transportation systems, citation networks, knowledge graphs, or any system involving relationships between entities. This skill targets NetworkX 3.x (current stable: 3.6, which requires Python >= 3.11). Several pre-3.0 APIs (`nx.info`, `nx.write_gpickle`, `nx.read_shp`) and the 3.4-era `nx.random_tree` no longer exist — current replacements are used throughout this skill. ## When to Use This Skill Invoke this skill when tasks involve: - **Creating graphs**: Building network structures from data, adding nodes and edges with attributes - **Graph analysis**: Computing centrality measures, finding shortest paths, detecting communities, measuring clustering - **Graph algorithms**: Running standard algorithms like Dijkstra's, PageRank, minimum spanning trees, maximum flow - **Network generation**: Creating synthetic networks (random, scale-free, small-world models) for testing or simulation - **Graph I/O**: Reading from or writing to various formats (edg
- Overview
- When to Use This Skill
- Core Capabilities
- 1. Graph Creation and Manipulation
- 2. Graph Algorithms
- 3. Graph Generators
- 4. Reading and Writing Graphs
- 5. Visualization
- Working with NetworkX
- Installation
- Common Workflow Pattern
- Important Considerations
- Quick Reference
- Basic Operations
What does the networkx skill do?
Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill networkx --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.