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networkx

Creates, analyzes and visualizes networks and graphs in Python with NetworkX: algorithms, centrality, community detection and graph file formats.

Kind
Skill
Works with
Claude Code, Codex, Cursor, Gemini / Antigravity
Languages
Python
Source
Scientific Agent Skills (K-Dense) @ 5f2bc0341354
Path
skills/networkx/SKILL.md
Jobs
Implementation

Credit

Made by
K-Dense-AI
Original
github.com/K-Dense-AI/scientific-agent-skills/blob/5f2bc03413…
License
MIT

Listed by Skill Manifest; not made by us.

InstallInstall it by hand, following the source.
gh skill install K-Dense-AI/scientific-agent-skills networkx
Install factsScanned 2 Nov 2026
Adds
  • .claude/skills/networkx/ (the skill folder)
  • 6 file(s) the skill refers to
Runs scripts
None found
Uses the network
Yes
  • downloads Python packages
Needs secrets
Yes
  • credentials
Source revision
5f2bc0341354
Licence
MIT
Last activity
26 Oct 2026
Review
Reviewed

Found by a static scan of the stored text; nothing is run. “None found” is not a guarantee, so read what you install before you run it.

  • Only the main file is read; other files in the folder are not scanned
# 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 tested NetworkX 3.7 (Python >=3.12, excluding 3.14.1). 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.

Review sources, executed examples, optional-dependency limits, and reproducibility notes are in [references/review.md](references/review.md). Reference snippets are API patterns: supply the stated graph type and input files; they are not one sequential script.

## 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
…