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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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 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.
GNN code generation for simulation frameworks. Use when generating PyMDP, RxInfer.jl, ActiveInference.jl, JAX, DisCoPy, PyTorch, NumPyro, Stan, or bnlearn code from GNN model specifications.
| name | gnn-llm-analysis |
| description | 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. |
Provides LLM-enhanced analysis of GNN models including natural language interpretation, model summarization, structural analysis, and AI-assisted insights using multiple provider backends.
# Run LLM analysis
python src/13_llm.py --target-dir input/gnn_files --output-dir output --verbose
# As part of pipeline
python src/main.py --only-steps 13 --verbose
The LLM module supports multiple providers with automatic recovery:
from llm import (
process_llm, LLMProcessor, LLMAnalyzer,
analyze_gnn_file_with_llm, extract_variables, extract_connections,
perform_semantic_analysis, generate_model_insights,
generate_documentation, generate_llm_summary,
get_available_providers, get_module_info
)
# Process LLM step (used by pipeline)
process_llm(target_dir, output_dir, verbose=True)
# Use the LLMProcessor class
processor = LLMProcessor()
result = processor.analyze(gnn_content)
description = processor.generate_description(gnn_content)
# Use the LLMAnalyzer class
analyzer = LLMAnalyzer()
insights = analyzer.analyze_content(gnn_content)
# Analyze a file with LLM
result = await analyze_gnn_file_with_llm(content)
# Extract model components
variables = extract_variables(content)
connections = extract_connections(content)
# Check available providers
providers = get_available_providers() # e.g., ['ollama', 'openai']
LLMProcessor — class with analyze(), analyze_model(), generate_description()LLMAnalyzer — class with analyze_content(), extract_insights()analyze_gnn_file_with_llm — async full analysisextract_variables, extract_connections, extract_sections — component extractiongenerate_model_insights, generate_documentation, generate_llm_summaryUnifiedLLMProcessor, AnalysisType, ProviderType — advanced provider API# LLM PyPI packages are core dependencies (uv sync)
uv sync
# LLM client packages are core dependencies; use uv sync.
# Install Ollama CLI separately for local inference: https://ollama.com
output/13_llm_output/This module registers tools with the GNN MCP server (see mcp.py):
analyze_gnn_with_llmgenerate_llm_documentationget_llm_module_infoget_llm_providersprocess_llm