activeinferenceinstitute/gnn-ml-integration
src/ml_integration/SKILL.md
GNN machine learning integration and model training. Use when training ML models on GNN data, checking ML framework availability, or integrating GNN pipeline outputs with machine learning workflows.
$ install --global
skillsauthnpx skillsauth add activeinferenceinstitute/generalizednotationnotation gnn-ml-integrationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- gnn-ml-integration
- description:
- GNN machine learning integration and model training. Use when training ML models on GNN data, checking ML framework availability, or integrating GNN pipeline outputs with machine learning workflows.
GNN Machine Learning Integration (Step 14)
Purpose
Integrates machine learning capabilities with the GNN pipeline, enabling feature extraction from parsed models, ML model training on GNN data, and framework availability checking.
Key Commands
# Run ML integration
python src/14_ml_integration.py --target-dir input/gnn_files --output-dir output --verbose
# As part of pipeline
python src/main.py --only-steps 14 --verbose
API
from ml_integration import process_ml_integration, check_ml_frameworks
# Process ML integration step (used by pipeline)
result = process_ml_integration(target_dir, output_dir, verbose=True)
# Check available ML frameworks
frameworks = check_ml_frameworks()
# Returns: {'pytorch': {'available': True, 'version': '2.x'},
# 'tensorflow': {'available': False},
# 'jax': {'available': True, 'version': '0.4.x'},
# 'sklearn': {'available': True, 'version': '1.x'}}
Key Exports
process_ml_integration— main pipeline processing functioncheck_ml_frameworks— checks availability of PyTorch, TensorFlow, JAX, scikit-learn
Supported ML Frameworks
| Framework | Check Key | Purpose |
| ----------- | ----------- | --------- |
| PyTorch | pytorch | Deep learning, CUDA support detection |
| TensorFlow | tensorflow | Neural network training |
| JAX | jax | Differentiable computing |
| scikit-learn | sklearn | Classical ML algorithms |
Dependencies
# ML framework deps
uv sync --extra ml-ai
# Includes: torch, transformers, datasets
Output
- ML results in
output/14_ml_integration_output/ - Framework availability reports
MCP Tools
This module registers tools with the GNN MCP server (see mcp.py):
process_ml_integrationcheck_ml_frameworkslist_ml_integration_targets
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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