Visualize
Visualize the Semantica knowledge graph, topology, centrality, communities, paths...
Install
npx skills add semantica-agi/semantica --skill visualize -g -a claude-code -y- Repository
- semantica-agi/semantica14.4k
- Category
- AI and agents
- Updated
- 3d ago
- License
- MIT
- Context cost
- Mediumabout 1,670 tokens
- Runs scripts
- No, instructions only
What it does
Visualize the Semantica knowledge graph, topology, centrality, communities, paths, embeddings, decision insights, and temporal evolution. Uses GraphAnalyzer, CentralityCalculator, CommunityDetector, PathFinder, and ContextGraph analytics. Sub-commands: topology, centrality, community, path, decision-graph, insights, temporal, embedding.
/semantica:visualize
Render graph visualizations as Mermaid, ASCII, or structured Markdown. Usage: /semantica:visualize <sub-command> [args]
$ARGUMENTS = sub-command + optional node label or filter.
topology [--filter <node_type>]
Full graph structure analysis, node types, edge distribution, connectivity metrics.
from semantica.kg.graph_analyzer import GraphAnalyzer
from semantica.context import ContextGraph
graph = ContextGraph(advanced_analytics=True)
analyzer = GraphAnalyzer()
# Comprehensive analysis
analysis = analyzer.analyze_graph(graph=graph.to_dict())
metrics = analyzer.compute_metrics(graph=graph)
connectivity = analyzer.analyze_connectivity(graph=graph)
Output:
Graph Topology:
Nodes: N (M types)
Edges: P
Density: 0.23
Avg degree: 4.7
Connected: YES / NO (K components)
Node type distribution:
[Mermaid pie chart]
| Type | Count | % | Avg Degree |
Top-10 connected nodes:
| Node | Type | Degree | Betweenness |
centrality [--type degree|betweenness|closeness|eigenvector|pagerank|all] [--top N]
Calculate and rank nodes by centrality.
from semantica.kg.centrality_calculator import CentralityCalculator
from semantica.context import ContextGraph
graph = ContextGraph()
calc = CentralityCalculator()
