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* 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 --------- Co-authored-by: Xiangzhe <bakryun0718@proton.me>