activeinferenceinstitute/gnn-statistical-analysis
src/analysis/SKILL.md
GNN advanced statistical analysis and result aggregation. Use when performing statistical analysis on simulation results, cross-simulation aggregation, computing information-theoretic metrics, or creating analytical visualizations of pipeline outputs.
$ install --global
skillsauthnpx skillsauth add activeinferenceinstitute/generalizednotationnotation gnn-statistical-analysisInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- gnn-statistical-analysis
- description:
- GNN advanced statistical analysis and result aggregation. Use when performing statistical analysis on simulation results, cross-simulation aggregation, computing information-theoretic metrics, or creating analytical visualizations of pipeline outputs.
GNN Statistical Analysis (Step 16)
Purpose
Performs advanced statistical analysis on pipeline outputs including simulation results, cross-framework comparison, Active Inference metrics, and comprehensive statistical reporting.
Key Commands
# Run analysis
python src/16_analysis.py --target-dir input/gnn_files --output-dir output --verbose
# As part of pipeline
python src/main.py --only-steps 16 --verbose
API
from analysis import (
process_analysis, perform_statistical_analysis,
calculate_variable_statistics, calculate_connection_statistics,
calculate_complexity_metrics, calculate_maintainability_index,
analyze_framework_outputs, generate_framework_comparison_report,
analyze_simulation_traces, analyze_free_energy,
compute_shannon_entropy, compute_kl_divergence,
compute_variational_free_energy, compute_expected_free_energy,
generate_analysis_summary
)
# Process analysis step (used by pipeline)
process_analysis(target_dir, output_dir, verbose=True)
# Statistical analysis
stats = perform_statistical_analysis(parsed_data)
# Complexity metrics
metrics = calculate_complexity_metrics(model_data)
# Framework comparison
report = generate_framework_comparison_report(results)
# Active Inference metrics
entropy = compute_shannon_entropy(distribution)
kl_div = compute_kl_divergence(p, q)
vfe = compute_variational_free_energy(beliefs, observations)
efe = compute_expected_free_energy(policy, beliefs)
Key Exports
process_analysis— main pipeline processing functionperform_statistical_analysis— core statistical analysiscalculate_complexity_metrics,calculate_maintainability_index,calculate_technical_debtanalyze_framework_outputs,generate_framework_comparison_reportcompute_shannon_entropy,compute_kl_divergence,compute_variational_free_energy,compute_expected_free_energyanalyze_simulation_traces,analyze_free_energy,analyze_policy_convergencegenerate_analysis_summary— summary report generation
Output
- Analysis reports in
output/16_analysis_output/ - Statistical summaries and aggregations
- Comparative visualizations
MCP Tools
This module registers tools with the GNN MCP server (see mcp.py):
process_analysisget_analysis_resultscompute_complexity_metrics
References
- AGENTS.md — Module documentation
- README.md — Usage guide
- SPEC.md — Module specification
Documentation
- README: Module Overview
- AGENTS: Agentic Workflows
- SPEC: Architectural Specification
- SKILL: Capability API
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