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.
8.7
Rating
0
Installs
Data & Analytics
Category
Excellent comprehensive skill for NetworkX graph analysis. The description clearly covers all major capabilities (graph creation, algorithms, generators, I/O, visualization) with sufficient detail for a CLI agent to invoke appropriately. Task knowledge is outstanding with concrete code examples, workflow patterns, and important considerations about performance and reproducibility. Structure is very clear with a well-organized SKILL.md that provides overview and quick reference while delegating detailed algorithm/generator/I/O specifics to referenced files. Novelty is strong as graph analysis involves complex algorithms, multiple data formats, and sophisticated visualizations that would consume many tokens for a CLI agent to implement from scratch. Minor room for improvement: could add more specific decision trees for algorithm selection (e.g., when to use which centrality measure), but overall this is a highly effective skill that meaningfully reduces cost and complexity.
davila7
Skill Author
Loading SKILL.md…
Skill Author
