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.
exploring-knowledge-graph
Guidance for deep knowledge graph traversal across memories, entities, and relationships. Use when needing comprehensive context before planning, investigating connections between concepts, or answering "what do you know about X" questions.
graph-algorithms
Essential graph algorithms including DFS, BFS, Dijkstra shortest path, and Union-Find with production-ready implementations.
recursive-knowledge
Process large document corpora (1000+ docs, millions of tokens) through knowledge graph construction and stateful multi-hop reasoning. Use when (1) User provides a large corpus exceeding context limits, (2) Questions require connections across multiple documents, (3) Multi-hop reasoning needed for complex queries, (4) User wants persistent queryable knowledge from documents. Replaces brute-force document stuffing with intelligent graph traversal.
graph-database-expert
Expert in graph database design and development with deep knowledge of graph modeling, traversals, query optimization, and relationship patterns. Specializes in SurrealDB but applies generic graph database concepts. Use when designing graph schemas, optimizing graph queries, implementing complex relationships, or building graph-based applications.
bv
Beads Viewer - Graph-aware triage engine for Beads projects. Computes PageRank, betweenness, critical path, and cycles. Use --robot-* flags for AI agents.
Network Analysis
Analyze network structures, identify communities, measure centrality, and visualize relationships for social networks and organizational structures
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.
