--- name: pdf2md version: 1.0.0 category: 开发编程 trigger_words: tags: - api - ai platform: coze source: DeepseekModel source_url: https://deepseekmodel.com/skill?id=zhihaoairobotic-clawphd-clawphd-skills-pdf2md-skill-md --- name pdf2md description Convert a local paper PDF to structured Markdown and export all figures as PNG + SVG + drawio. Attempts editable figure reconstruction via the built-in autofigure pipeline (SAM3 → RMBG-2.0 → VLM → SVG), falling back to a layered-SVG wrapper when API keys are unavailable. Use when the user wants to parse a paper PDF, extract its text as Markdown, or get editable/exportable figure assets. metadata {"clawphd":{"emoji":"📄"}} PDF → Markdown & Editable Figures One tool. Uses pdf_to_markdown with your local PDF. Core Markdown conversion needs no cloud API. Editable figure reconstruction uses the built-in autofigure pipeline when vlm_provider + fal_api_key are configured; otherwise degrades gracefully to a layered-SVG fallback. When to use Trigger words / phrases (Chinese or English): 论文 PDF 转 Markdown / PDF 转 md 把这篇论文转成 Markdown 提取论文图片 / 导出论文的图 导出可编辑图 / 可编辑 SVG / 可编辑 drawio pdf to markdown / parse paper PDF / extract paper figures 把 PDF 解析成结构化文本 Tool pdf_to_markdown( pdf_path = "", ...options... ) Parameters Parameter Type Default Notes pdf_path string required Absolute or workspace-relative path to the PDF out_root string outputs/pdf2md Root output dir; paper lands in // backend "docling" | "mineru" "docling" Markdown engine; mineru requires separate CLI install export_figures bool true Extract labelled figures to assets/figures/ figure_box_source "auto" | "docling" | "fitz" "auto" How figure boxes are located export_svg bool true Attempt SVG per figure (mutool → pdf2svg → fitz → PNG wrapper) export_drawio bool true Attempt drawio per figure (built-in editable conversion from SVG) enable_rebuild bool true Run editable reconstruction (see below). DEFAULT IS TRUE. enable_rebuild defaults to true . When the autofigure pipeline is fully configured (needs fal_api_key in config + a VLM provider), it runs SAM3 segmentation → RMBG-2.0 background removal → VLM SVG template → icon replacement. When not configured it falls back to a two-layer SVG that embeds the raster PNG with an empty vector overlay. The fallback never crashes the pipeline. Editable rebuild pipeline The autofigure pipeline runs entirely in-process (no external CLI needed): Step Module What it does 1 SegmentFigureTool SAM3 via fal.ai — detects icons/elements 2 CropRemoveBgTool RMBG-2.0 (local torch) — removes backgrounds 3 GenerateSVGTemplateTool VLM reconstructs figure layout as SVG 4 ReplaceIconsSVGTool Embeds transparent icon PNGs into SVG Requirements for full autofigure rebuild: fal_api_key set in ~/.clawphd/config.json under tools.autofigure A multimodal VLM provider configured (openrouter / gemini recommended) pip install clawphd-ai[autofigure] (torch / torchvision / transformers) Output directory layout outputs/pdf2md// .pdf ← copy of source PDF .md ← full Markdown of the paper meta/ doc.json ← docling structured document model run.json ← run metadata: timing, tool detection, warnings figures.json ← array of per-figure metadata records assets/ figures/ fig_001/ fig_001.png ← cropped raster (always present if PyMuPDF available) fig_001.svg ← vector SVG (only when enable_rebuild=false) fig_001.drawio ← drawio XML (only when enable_rebuild=false) meta.json ← figure-level metadata rebuild/ ← present when enable_rebuild=true autofigure/ ← autofigure intermediate files (SAM3, crops, icons) rebuilt.svg ← primary SVG output (autofigure or layered-SVG fallback) rebuilt.drawio ← primary drawio output fig_002/ ... paper_id ( sha1(pdf_bytes)[:12] ) is returned in metadata for traceability. SVG export priority mutool draw — highest-quality vector SVG, full-page then viewBox-cropped pdf2svg — alternative CLI, full-page then viewBox-cropped fitz (PyMuPDF built-in) — per-figure cropbox SVG PNG-embedding SVG — final fallback, always works run.json fields { "paper_id" : "..." , "figures_total" : 5 , "svg_exported" : 5 , "drawio_exported" : 5 , "rebuilt_exported" : 5 , "elapsed_sec" : 12.4 , "tools_detected" : { "mutool" : true , "pdf2svg" : false , "svgtodrawio" : false , "autofigure_enabled" : true } , "warnings" : [ ] } Typical workflow Simplest call (all defaults) pdf_to_markdown(pdf_path="path/to/paper.pdf") Produces .md , copied PDF, all figures as PNG + SVG, and a rebuilt.svg per figure (autofigure if configured, else layered-SVG fallback). With drawio export pdf_to_markdown( pdf_path = "papers/attention_is_all_you_need.pdf", export_drawio = true ) Disable rebuild (faster, skips reconstruction step) pdf_to_markdown( pdf_path = "paper.pdf", enable_rebuild = false ) Use MinerU backend pdf_to_markdown( pdf_path = "paper.pdf", backend = "mineru" ) Requires the MinerU CLI ( mineru or magic-pdf ) on PATH. Falls back to docling automatically if MinerU is absent or fails. Return value (JSON string) { "paper_id" : "<12-char sha1>" , "out_dir" : "outputs/pdf2md/" , "md_path" : "outputs/pdf2md//.md" , "source_pdf_copy" : "outputs/pdf2md//.pdf" , "figures_total" : 5 , "svg_exported" : 5 , "drawio_exported" : 5 , "rebuilt_exported" : 5 , "backend_used" : "docling" , "elapsed_sec" : 12.4 , "warnings" : [ ] } Report the out_dir and md_path to the user so they know where to find the outputs. If warnings is non-empty, summarise them briefly. Example conversation User: 帮我把 /home/me/papers/resnet.pdf 转成 Markdown，并导出所有图的可编辑 SVG Step 1: pdf_to_markdown(pdf_path="/home/me/papers/resnet.pdf", export_svg=true) Step 2: 回复用户（中文）： - Markdown 已保存到 outputs/pdf2md//.md - 共检测到 N 张图，已导出为 PNG + SVG - 每张图的可编辑重建结果在 rebuild/rebuilt.svg - 若需要 drawio 格式，可再次调用并加上 export_drawio=true