# Visualize

Visualize the Semantica knowledge graph, topology, centrality, communities, paths...

- Repository: https://github.com/semantica-agi/semantica (14373 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add semantica-agi/semantica --skill visualize -g -a claude-code -y`
Codex: `npx skills add semantica-agi/semantica --skill visualize -g -a codex -y`
Cursor: `npx skills add semantica-agi/semantica --skill visualize -g -a cursor -y`
Gemini CLI: `npx skills add semantica-agi/semantica --skill visualize -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# /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.

```python
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.

```python
from semantica.kg.centrality_calculator import CentralityCalculator
from semantica.context import ContextGraph

graph = ContextGraph()
calc = CentralityCalculator()

The rest: https://github.com/semantica-agi/semantica/blob/HEAD/plugins/skills/visualize/SKILL.md