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GNN graph and matrix visualization generation. Use when creating network graph plots, matrix heatmaps, state space diagrams, or other visual representations of GNN models.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Generalized Notation Notation (GNN) processing pipeline for Active Inference generative models. Use when working with GNN files, running the 25-step pipeline, parsing model specifications, generating simulations, or producing visualizations and reports from GNN model definitions.
GNN command-line interface dispatch and health checks. Use when invoking the `gnn` CLI, validating subcommand availability, or checking command-line access to pipeline modules.
GNN LLM-enhanced analysis and model interpretation. Use when generating natural language descriptions of GNN models, getting AI-assisted model explanations, or performing LLM-powered analysis of Active Inference specifications.
GNN Language Server Protocol integration. Use when starting editor diagnostics, checking hover behavior, or validating LSP support for GNN files.
GNN Model Context Protocol processing and tool registration. Use when registering GNN operations as MCP tools, building MCP server configurations, or integrating GNN capabilities with LLM tool-use workflows.
GNN pipeline orchestration and configuration management. Use when configuring pipeline execution, setting step inclusion/exclusion, managing pipeline state, or customizing the 25-step execution flow.
| name | gnn-visualization |
| description | GNN graph and matrix visualization generation. Use when creating network graph plots, matrix heatmaps, state space diagrams, or other visual representations of GNN models. |
Generates static visualizations of GNN models including network graphs, matrix heatmaps, state space diagrams, and connection maps using matplotlib and networkx.
# Run visualization
python src/8_visualization.py --target-dir input/gnn_files --output-dir output --verbose
# As part of pipeline
python src/main.py --only-steps 8 --verbose
from pathlib import Path
from visualization import (
GNNVisualizer, MatrixVisualizer,
generate_graph_visualization, generate_matrix_visualization,
generate_visualizations, process_visualization,
)
# Use the GNNVisualizer class
viz = GNNVisualizer(output_dir="output/")
viz.generate_graph_visualization(graph_data)
viz.generate_matrix_visualization(matrix_data)
# Standalone functions
generate_graph_visualization(graph_data, output_dir="output/")
generate_matrix_visualization(matrix_data, output_dir="output/")
# Run full visualization step (used by pipeline)
process_visualization(Path("input/gnn_files"), Path("output/8_visualization_output"), verbose=True)
GNNVisualizer — main visualization classMatrixVisualizer — matrix-specific visualizationgenerate_graph_visualization — network graph plotsgenerate_matrix_visualization — matrix heatmapsprocess_visualization — pipeline processing (JSON-first when step-3 *_parsed.json exists)Visualization uses a four-tier recovery system:
# matplotlib + networkx (included in core)
uv sync
# For additional viz backends
uv sync
output/8_visualization_output/This module registers tools with the GNN MCP server (see mcp.py):
get_visualization_module_infoget_visualization_optionslist_visualization_artifactsprocess_visualization