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.
Confirm successful installation by checking the skill directory location:
.cursor/skills/networkx
Restart Cursor to activate networkx. Access via /networkx in your agent's command palette.
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Security Notice
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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
MultiGraph: Undirected graphs allowing multiple edges between nodes
MultiDiGraph: Directed graphs with multiple edges
Create graphs by:
import networkx as nx
# Create empty graphG = nx.Graph()# Add nodes (can be any hashable type)G.add_node(1)G.add_nodes_from([2,3,4])G.add_node("protein_A",type='enzyme', weight=1.5)# Add edgesG.add_edge(1,2)G.add_edges_from([(1,3),(2,4)])G.add_edge(1,4, weight=0.8, relation='interacts')
Reference: See references/graph-basics.md for comprehensive guidance on creating, modifying, examining, and managing graph structures, including working with attributes and subgraphs.
2. Graph Algorithms
NetworkX provides extensive algorithms for network analysis:
Reference: See references/algorithms.md for detailed documentation on all available algorithms including shortest paths, centrality measures, clustering, community detection, flows, matching, tree algorithms, and graph traversal.
3. Graph Generators
Create synthetic networks for testing, simulation, or modeling:
Reference: See references/generators.md for comprehensive coverage of all graph generators including classic, random, lattice, bipartite, and specialized network models with detailed parameters and use cases.
4. Reading and Writing Graphs
NetworkX supports numerous file formats and data sources:
Reference: See references/io.md for complete documentation on all I/O formats including CSV, SQL databases, Cytoscape, DOT, and guidance on format selection for different use cases.
5. Visualization
Create clear and informative network visualizations:
Basic Visualization:
import matplotlib.pyplot as plt
# Simple drawnx.draw(G, with_labels=True)plt.show()# With layoutpos = nx.spring_layout(G, seed=42)nx.draw(G, pos=pos, with_labels=True, node_color='lightblue', node_size=500)plt.show()
Customization:
# Color by degreenode_colors
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Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
βΊAccess to product documentation and roadmap tools (Jira, Notion, etc.)
βΊUnderstanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
βΊStakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
β Not validating competitive researchβverify facts before sharing
β Accepting user stories without involving engineering team
β Over-relying on frameworks without qualitative judgment
β Not customizing outputs to company culture and communication style
β Skipping stakeholder validation of generated requirements
Best Practices
β Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
β Don't
βDon't publish competitive analysis without fact-checking
βDon't finalize user stories without engineering review
βDon't make prioritization decisions solely on AI scoring
βDon't skip customer validation of generated requirements
βDon't ignore company-specific context and culture
π‘ Pro Tips
β Provide context: company goals, constraints, customer feedback
β Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
β Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
β Use skill for 70% generation + 30% customization to company needs
When to Use This
β Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
β Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
