Agent skill · Data & Analytics

networkx

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

LeonChaoXgithub.com/LeonChaoXGitHub ↗
claude-codeMIT
Install
npx skills add LeonChaoX/qinyan-academic-skills --skill networkx --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 6
SKILL.md size: 12 KB
Bundled scripts: none
Path: skills/11-数据分析与统计建模/networkx/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 759
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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. ## 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 (edge lists, GraphML, JSON, CSV, adjacency matrices) - **Visualization**: Drawing and customizing network visualizations with matplotlib or interactive libraries - **Network comparison**: Checking isomorphism, computing graph metrics, analyzing structural proper

What's inside
Steps it walks through
  1. Overview
  2. When to Use This Skill
  3. Core Capabilities
  4. 1. Graph Creation and Manipulation
  5. 2. Graph Algorithms
  6. 3. Graph Generators
  7. 4. Reading and Writing Graphs
  8. 5. Visualization
  9. Working with NetworkX
  10. Installation
  11. Common Workflow Pattern
  12. Important Considerations
  13. Quick Reference
  14. Basic Operations
Ships with 5 files
  • references/algorithms.md
  • references/generators.md
  • references/graph-basics.md
  • references/io.md
  • references/visualization.md
More from qinyan-academic-skills
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About this skill
What does the networkx skill do?

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

How do I install it?

Run `npx skills add LeonChaoX/qinyan-academic-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 LeonChaoX/qinyan-academic-skills, a repository with 759 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.

Keep going