* feat(bridge): optional-sharp image normalization util (long-edge 2048)
* feat(bridge): normalize fetched images before vision describe self-call
Route the bridge's own fetchRemoteImageAsDataUri() output through
normalizeDataUri() (long-edge cap 2048) before handing it to the vision
model — matches the resize cap OpenAI/Anthropic already apply, cutting
upload bytes/latency. Scoped to the bridge's self-fetched images only,
never the user's raw passthrough payload (HR#20 opt-in principle).
* test(bridge): height-dominant long-edge coverage
Add a 100x4096 PNG case to image-normalize.test.ts alongside the existing
width-dominant one, so normalizeImageBuffer's long-edge cap is proven on
both axes.
* fix(bridge): type sharp's callable default export (TS2349)
* chore(quality): rebaseline deadExports for the OCR/image-to-text series
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Co-authored-by: Xiangzhe <bakryun0718@proton.me>
