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GNN AI-powered pipeline analysis and executive reports. Use when generating AI-driven executive summaries, performing intelligent pipeline health assessments, or creating comprehensive AI-enhanced analysis of GNN processing results.
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-intelligent-analysis |
| description | GNN AI-powered pipeline analysis and executive reports. Use when generating AI-driven executive summaries, performing intelligent pipeline health assessments, or creating comprehensive AI-enhanced analysis of GNN processing results. |
Provides AI-powered analysis of the entire pipeline execution, generating executive summaries, health assessments, intelligent recommendations, and comprehensive AI-enhanced reports.
# Run intelligent analysis
python src/24_intelligent_analysis.py --target-dir input/gnn_files --output-dir output --verbose
# As part of pipeline (final step)
python src/main.py --only-steps 24 --verbose
from intelligent_analysis import (
process_intelligent_analysis, IntelligentAnalyzer,
analyze_pipeline_summary, analyze_individual_steps,
generate_executive_report, identify_bottlenecks,
generate_recommendations, calculate_pipeline_health_score,
classify_failure_severity, detect_performance_patterns,
generate_optimization_suggestions
)
# Process intelligent analysis step (used by pipeline)
process_intelligent_analysis(target_dir, output_dir, verbose=True)
# Use the IntelligentAnalyzer class
analyzer = IntelligentAnalyzer()
# Analyze pipeline summary
summary = analyze_pipeline_summary(pipeline_data)
# Generate executive report
report = generate_executive_report(analysis_results)
# Health scoring
score = calculate_pipeline_health_score(pipeline_data)
# Identify bottlenecks and generate recommendations
bottlenecks = identify_bottlenecks(step_data)
recs = generate_recommendations(analysis_results)
# Performance pattern detection
patterns = detect_performance_patterns(metrics)
suggestions = generate_optimization_suggestions(patterns)
process_intelligent_analysis — main pipeline processing functionIntelligentAnalyzer / AnalysisContext / StepAnalysis — analysis classesanalyze_pipeline_summary, analyze_individual_steps — analysis functionsgenerate_executive_report — executive summary generationcalculate_pipeline_health_score, classify_failure_severitydetect_performance_patterns, generate_optimization_suggestionsidentify_bottlenecks, generate_recommendationsThis is the final step (Step 24). It has access to all outputs from Steps 0–23 and provides the capstone analysis.
output/24_intelligent_analysis_output/This module registers tools with the GNN MCP server (see mcp.py):
get_analysis_capabilitiesget_intelligent_analysis_module_infoprocess_intelligent_analysis