Imported from yunniko/image-object-splitter (AGENTS.md). Install upstream with npx skills add yunniko/image-object-splitter. Copyright stays with the author.

This is NOT the Next.js you know

This version has breaking changes — APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in node_modules/next/dist/docs/ (resolved from this file's directory; in monorepos the next package may not be visible from the repo root) before writing any code. Heed deprecation notices.

This block is written and re-added by next dev — verify at node_modules/next/dist/server/lib/generate-agent-files.js. Removing it from a diff only re-creates the uncommitted change; committing it with your work keeps the tree clean.

image-object-splitter — project conventions

Read HANDOVER.md first: current state, decision record (especially D1's test-fixture licensing rationale and D2's three real @imgly/ background-removal integration bugs), next steps. Goal in GOALS.md (G-001). Parent initiative in E:\CLAUDE\projects\svc-lab\; company-wide standards in E:\CLAUDE\COMPANY\.

• Stack: Next.js App Router, TypeScript, Tailwind. No database, no auth, no accounts. Detection (TensorFlow.js + coco-ssd), cropping (canvas), and background removal (@imgly/background-removal) all run client-side — no API routes, nothing is ever sent to a server. Keep it that way; this project's entire privacy pitch depends on it being literally true.
• lib/object-detector.ts and lib/background-remover.ts both dynamically import() their heavy ML dependencies rather than importing them at the top of the file — this keeps a multi-megabyte model/runtime out of every page's JS bundle except the one page that actually uses it. Don't change these to static imports even if it looks simpler.
• lib/background-remover.ts's removeImageBackground requires an explicit MIME type argument — do not "simplify" this back to a bare Uint8Array/ArrayBuffer call. See HANDOVER.md D2 point 3: the underlying library silently fails to decode an untyped Blob, and this isn't caught by TypeScript (the library's own types claim raw bytes are fine) — only a real e2e run against a real photo caught it.
• lib/geometry.ts's padAndClampBox is the only place crop coordinates get computed — always clamps to the source image's actual bounds. Don't add a second box-math implementation elsewhere; route any new export format through this function.
• npm install/npm ci need --legacy-peer-deps (a live npm/arborist bug, not specific to this project — see svc-lab/HANDOVER.md).
• Two test layers: npx vitest run (unit — pure crop/zip logic only, no ML) and npx playwright test (e2e — real on-device model inference against a real photo fixture; run npm run fetch-test-fixtures first, see D1). The e2e suite is slower than a typical svc-lab service's because it downloads and runs real AI models inside the browser — this is expected, not a hang. Also run npm run build before calling any change to the ML wrapper modules done — it's the only layer that catches a wrong third-party export name or config shape (see D2).
• See E:\CLAUDE\COMPANY\INFRASTRUCTURE_DEPLOY.md for the redeploy command once live.