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feat(training): letter forge + labeled dataset + Modal prompt-A/B eval
Auenchanters +1: Claude Fable 5 guardian/letterforge.py: 8 parameterized letter factories with gold
labels derived through triage itself; scripts/make_dataset.py renders
images + labels.jsonl (+SFT-ready targets); modal_eval.py scores level/
doc-type accuracy, scam recall, false alarms, amount/deadline hits,
latency and output size for the production vs compact prompt on A10G.
Also: deep-link/easter-egg retry budget 7.5s -> 30s (Space mounts
slower than local; observed live).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>