activeinferenceinstitute/gnn-intelligent-analysis
src/intelligent_analysis/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add activeinferenceinstitute/generalizednotationnotation gnn-intelligent-analysisInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- 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.
GNN Intelligent Analysis (Step 24)
Purpose
Provides AI-powered analysis of the entire pipeline execution, generating executive summaries, health assessments, intelligent recommendations, and comprehensive AI-enhanced reports.
Key Commands
# 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
API
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)
Key Exports
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_recommendations
Pipeline Position
This is the final step (Step 24). It has access to all outputs from Steps 0–23 and provides the capstone analysis.
Output
- AI analysis reports in
output/24_intelligent_analysis_output/ - Executive summary documents
- Pipeline health scorecards
MCP Tools
This module registers tools with the GNN MCP server (see mcp.py):
process_intelligent_analysisget_analysis_capabilitiesget_intelligent_analysis_module_info
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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