Kg

Knowledge graph commands:

kg extract <path> -- Extract entities and relations from source files at the given path.

1. Scan files at <path> recursively for classes, functions, modules, types, and config references
2. For each entity, record its type, name, file location, and description
3. Map relations between entities: imports, extends, implements, depends-on, calls, references
4. Store entities via mcp__claude-flow__agentdb_hierarchical-store in the knowledge-graph namespace
5. Create causal edges via mcp__claude-flow__agentdb_causal-edge for each relation
6. Report: total entities found, total relations mapped, entity type breakdown

kg traverse <entity> -- Pathfinder traversal starting from the named entity.

1. Look up the seed entity via mcp__claude-flow__agentdb_hierarchical-recall
2. Expand outward by following causal edges (default depth: 3)
3. Score each path: relevance = edge_weight * semantic_similarity(query, node)
4. Prune paths with cumulative score below 0.3
5. Return the top 10 paths with entities, relations, and scores

kg relations <entity> -- List all direct relations for the named entity.

1. Query causal edges where source or target matches <entity>
2. Group by relation type (imports, extends, implements, depends-on, calls)
3. Display as a table with: relation, direction (incoming/outgoing), target entity, weight

kg visualize -- Generate an ASCII visualization of the knowledge graph.

1. Recall all entities and edges from the knowledge-graph namespace
2. Identify the most-connected nodes (top 10 by degree)
3. Render a simplified graph showing key nodes and their connections
4. Include legend with entity types and relation types

kg search <query> -- Semantic search across the knowledge graph.

1. Route the query via mcp__claude-flow__agentdb_semantic-route to find relevant entities
2. Expand results with causal edges to show related context
3. Rank by semantic similarity score
4. Display matches with entity name, type, file location, and relevance score