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.
WHAT YOU BECOME
Perfect for these scenarios
Navigate complex decisions with structured frameworks for clarity
Diagnose and resolve technical issues with step-by-step guidance
Break through creative stagnation with targeted brainstorming techniques
Identify bottlenecks and actionable steps to keep projects moving
MEASURED GAIN
Proven benefits and measurable impact
Cut time spent stuck by applying the right technique instantly
Minimize mental overload with guided problem-solving approaches
Achieve better results by matching methods to problem types
WHAT YOU GET
Files, tags and the three-step install
Tip: Read the documentation and the code before first use, so you know what it does and which permissions it needs.
NEXT SCRIPTS
Recommended based on tags and category
Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Claude Code agent generation system that creates custom agents and sub-agents with enhanced YAML frontmatter, tool access patterns, and MCP integration support following proven production patterns
Generate and maintain AGENTS.md files following the public agents.md convention. Use when creating documentation for AI agent workflows, onboarding guides, or when standardizing agent interaction patterns across projects.
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.