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ts-figure-optimize

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The suite's SOLE figure vectorizer: turn ONE raster scientific figure (a PNG/JPG, e.g. a gpt-image-2 schematic) into an editable, publication-ready figure via DrawAI's **key-free HYBRID** — local perception (SAM3 region detection + PaddleOCR + Box-IR layout, no account) then a deterministic hybrid build that keeps the approved render **pixel-exact** (a whole-canvas raster) and lays an **editable <text> overlay** on top (~0.91 SSIM), exported as a self-contained SVG + vector PDF + an editable PPTX. The render's full richness is preserved EXACTLY (it IS the approved image) while every label becomes editable. The original raster is always kept. Hybrid is the ONLY mode used: there is no Codex full-vector redraw (legacy/off — it needs an account, and a redraw of a dense figure loses fidelity) and no Claude-redraw fallback. If this runtime cannot be provisioned, the caller keeps the approved PNG as-is — never a lossy redraw. Independent of the paper pipeline — give it any figure image.

skills/ts-figure-optimize/SKILL.md

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# ts-figure-optimize — raster figure → editable HYBRID, the key-free DrawAI path

This is the suite's **only** figure vectorizer, and it runs DrawAI's **key-free HYBRID**: SAM3 segments
the layout and PaddleOCR reads the text **locally** (on a GPU box, no Claude/Codex account), then a
deterministic build keeps the approved render **pixel-exact** and lays an **editable `<text>` overlay**
on top — exported as a self-contained SVG + vector PDF + editable PPTX (~0.91 SSIM; ~63 editable text
boxes on the test figure). The figure's richness is preserved EXACTLY (the graphics ARE the approved
render) while every label becomes editable.

**Hybrid is the ONLY mode used.** The engine also contains a legacy Codex full-vector redraw
(`run_reconstruction.py`) — it is **off**: it needs an LLM account, and a full redraw of a dense figure
measurably loses fidelity vs the exact render (~0.67 vs 0.91 SSIM). So the suite rule is **HYBRID, or
else keep the approved PNG — never redraw.**
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