A multi-stage Python pipeline that builds a corpus of VCV Rack (modular synthesizer) patches and module knowledge, then generates new .vcv patch files from learned patterns.
pip install -r requirements.txt
apt install libzstd1Create .env:
GITHUB_TOKEN=<your_github_pat>
Run stages sequentially. Each stage reads files from data/ produced by previous stages.
python3 00_build_whitelist.py # ~30s, needs network + GITHUB_TOKEN
python3 01_fetch_metadata.py # ~2min, needs network
python3 02_download_patches.py # ~1hr, needs network, RESUMABLE
python3 03_parse_and_filter.py # seconds
python3 04_aggregate.py # seconds
python3 05_build_port_registry.py # ~5min, clones ~25 repos
python3 06_deep_analysis.py # seconds
python3 07_build_module_profiles.py # ~2min, needs network
python3 08_generate_reference_files.py # seconds
python3 09_classify_and_learn.py # seconds
python3 10_build_knowledge_base.py # secondspython3 generate_patches.py # 4 hand-designed patches
python3 generate_batch.py # 20 corpus-derived patches (batch3)
python3 validate_patch.py data/generated/ # verify signal flow- 3,530 downloaded community patches from PatchStorage
- 2,818 patches passing strict filtering (100% free modules, Rack 2.x)
- 269 module reference docs with YAML frontmatter
- 137 decoded patch analyses with archetype classification
- Signal-flow validator — traces audio from oscillator to output without opening VCV Rack
This project code is unlicensed / provided as-is. Generated .vcv files are independent works. Community patches remain property of their original authors.
