- label_canary_theorems.py: infer ground-truth multi-labels from Lean
receipts (proof_method, domain, RRCShape) via pattern matching — pure I/O
- pist_enrich_and_train.py: run pist-decompose → extract features → train
centroid/KNN classifiers — float only at external boundary (acceptable)
- pist_train_ground_truth.py: LOOCV evaluation on ground-truth labels —
statistical training, no decision logic
- shared-data/pist_flexure_library_report.json: updated flexure library report
All three shims: pure I/O, no admissibility/gating decisions, float only in
normalization (external boundary). Complies with AGENTS.md §Programming Choice
Flow. Outputs are regenerable from source receipts.
Build: Compiler 3311 jobs, 0 errors (no Lean changes).
Generated with Devin (https://cli.devin.ai/docs)
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
