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.

1.26x

Quality

75%

Does it follow best practices?

Impact

81%

1.26x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./scientific-skills/networkx/SKILL.md

Evaluation results

100%

30%

Network Visualization for Research Paper

Reproducible publication visualization

Criteria

Without context

With context

Seed in layout

100%

Seed in graph generation

100%

PNG export at dpi=300

100%

bbox_inches='tight'

100%

PDF vector export

100%

axis off

100%

tight_layout

100%

figsize set

100%

Community detection

100%

Community color mapping

100%

Node size by centrality

100%

67%

Identifying Key Influencers in a Large Social Network

Large-graph approximate centrality analysis

Criteria

Without context

With context

Seed in graph generation

100%

Approximate betweenness with k

100%

Sparse matrix used

Simple layout for large graph

Seed in layout

100%

Centrality results saved

100%

Multiple centrality metrics

100%

Top-N nodes reported

100%

Efficient file format

25%

76%

16%

Graph Enrichment and Serialization Pipeline

Attribute-preserving graph I/O and Pandas pipeline

Criteria

Without context

With context

Pandas edgelist import

100%

edge_attr parameter

100%

GraphML for attribute-rich output

100%

Node attributes set

100%

Community detection run

60%

100%

Self-loop removal after config model

100%

Pandas output

Graph reloaded correctly

Format selection comment

100%

Seed in any stochastic operation

50%

Repository: K-Dense-AI/claude-scientific-skills
Commit: b58ad7e

Evaluated: 27 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Network Visualization for Research Paper Identifying Key Influencers in a Large Social Network Graph Enrichment and Serialization Pipeline

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