视觉画布设计
canvas-design
基于设计原则创作静态视觉作品,用于海报与品牌图形。
image-svg-pptx-pro【文档与办公】Convert PPT screenshots, academic images, UI mockups, and report pages into a high-fidelity SVG intermediate, then reconstruct a mixed editable PowerPoint file with SVG/PNG fallbacks and optional VBA helpers.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this skill when the user wants to turn a raster image, PPT screenshot, academic figure, UI screen, poster, report page, or AI-generated PPT image into a PowerPoint file that is as editable as possible while preserving visual fidelity.
The core route is:
source image → normalized image → semantic layout plan → high-fidelity SVG → editable PPTX reconstruction → QA comparison → correction pass
This skill is intentionally not a naive OCR-to-PPT workflow. It uses SVG as the stable intermediate representation because SVG can encode precise geometry, typography, image crops, vector primitives, and fallbacks before converting to Office shapes.
Always produce, when possible:
reconstructed.pptx — main deliverable; editable PowerPoint.reconstruction.svg — high-fidelity intermediate source.layout_plan.json — editable semantic layout plan.assets/ — cropped image/logo/icon/screenshot regions.qa_report.md — visual and editability assessment.macros/insert_svg_fallback.bas — VBA helper for inserting SVG or fallback objects.Prioritize final usefulness, not theoretical editability.
When the user does not specify a mode, use balanced.
Best default. Core text and structure editable; complex visual regions preserved as cropped assets.
Use when the user explicitly wants more editable elements. Vectorize more lines, charts, and simple icons. Still keep complex logos/photos as assets.
Use when fidelity is more important than editing. Put a full-slide image/SVG fallback underneath or as grouped locked visual, then overlay editable text and key elements.
Run:
python scripts/preprocess_image.py input.png --out work/normalized.png --meta work/source_meta.json
If the input is a phone photo of a screen, deskew/crop it first if needed. Keep the original file unchanged.
layout_plan.jsonUse visual reasoning to decompose the normalized image into semantic layers. Follow references/layout_plan_schema.md.
The plan must include:
editable, asset, or fallback.Do not invent unreadable text. Mark uncertain text as needs_review: true.
Run:
python scripts/crop_assets.py work/normalized.png work/layout_plan.json --out work/assets
Complex image regions referenced in the plan must point to these cropped files.
Run:
python scripts/plan_to_svg.py work/layout_plan.json --assets work/assets --out work/reconstruction.svg
The SVG is the canonical visual intermediate. Inspect or render it before generating PPTX.
Prefer plan-based PPTX generation, because it preserves semantics better than arbitrary SVG parsing.
python scripts/plan_to_pptx.py work/layout_plan.json --assets work/assets --out work/reconstructed.pptx
If the plan is unavailable but an SVG exists, use the SVG parser fallback:
python scripts/svg_to_pptx_editable.py work/reconstruction.svg --out work/reconstructed.pptx
Create qa_report.md using references/quality_checklist.md.
Check:
If the visual mismatch is obvious, update layout_plan.json and regenerate. Do not present the first broken result as final.
When the user gives an image and asks for conversion, proceed without asking follow-up unless required files are missing. Say briefly what files were produced and link the PPTX/ZIP.
If exact text is unreadable, do not hallucinate it. Use cropped image fallback for the region and flag it in qa_report.md.
If SVG-to-PPTX direct conversion loses filters, masks, gradients, or paths, keep those regions as cropped assets and overlay editable text/shapes.
If the user needs a fully editable corporate deck, tell them which elements remain image-based and why.