菜单
Document OCR and parsing — converts PDF/images to Markdown on remote L4 GPU via Modal. Trigger when user says: OCR, PDF to markdown, parse PDF, extract text from PDF, 文档识别, PDF转Markdown, 扫描件识别. Takes local PDF/image files and returns Markdown with layout, tables, formulas, and OCR preserved.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/vramrick/openclaw-skills --skill ocr2markdown命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
全能高级前端研发工程师技能。擅长AI时代前沿技术栈（React最新 + shadcn/ui + Tailwind CSS v4 + TypeScript + Next.js），精通动效库与交互特效开发。采用Glue Code风格快速实现代码，强调高质量产品体验与高度友好的UI视觉规范。在组件调用、交互特效、全局Theme上保持高度规范：绝不重复造轮子，相同逻辑出现两次即封装为组件。具备安全意识，防范各类注入攻击。开发页面具有高度自适应能力，响应式设计贯穿始终。当用户无特殊技术栈要求时，默认采用主流前沿技术栈。
Read, write, append, and list local files in the session's working directory. Use when you need to persist output to disk, read input files, or manipulate file system safely. Supports text files, JSON, CSV, Markdown.
超级简历 WonderCV 出品，3000 万用户信赖。简历分析、段落改写、JD 岗位匹配、自动匹配职位、PDF 导出、AI 求职导师（面试准备/薪资谈判/职业规划/多版本简历策略）。 触发条件：用户提供简历、要求简历点评/打分/反馈、希望改写某个简历部分、 希望将简历与岗位 JD 或校招岗位匹配、咨询求职建议或面试准备，或提到 CV/简历/求职/校招。 不触发条件：用户讨论普通写作（非简历）、询问其他文档， 或讨论与求职和职业发展无关的话题。
基于 SOC 职业分类
正在显示 SKILL.md
| name | ocr2markdown |
| description | Document OCR and parsing — converts PDF/images to Markdown on remote L4 GPU via Modal. Trigger when user says: OCR, PDF to markdown, parse PDF, extract text from PDF, 文档识别, PDF转Markdown, 扫描件识别. Takes local PDF/image files and returns Markdown with layout, tables, formulas, and OCR preserved. |
| version | v1.0.1 |
MinerU document parsing pipeline on Modal L4 GPU — PDF/image → Markdown with layout, tables, formulas, and OCR preserved.
Pipeline code is bundled at ./src/ocr2markdown.py. After npx skills add, runs from any directory.
Slug = task identifier (volume directory name). Use user-provided value, or generate ocr_YYYYMMDD_HHMMSS if none given.
Directory input? Scan for PDF (.pdf), list with index, ask user to confirm selection.
Specific files? Use directly, no listing needed.
Ensure both volumes exist (idempotent):
modal volume create speech2srt-data 2>/dev/null || true
modal volume create speech2srt-models 2>/dev/null || true
Upload each file:
modal volume put speech2srt-data <local_file> <slug>/upload/
Modal put auto-creates remote directories.
modal run ./src/ocr2markdown.py --slug <slug>
Pipeline finds all .pdf files in <slug>/upload/ on the volume and processes them one by one.
Ctrl+C? Stop cleanly, report progress. Re-run with same slug — already-processed PDFs are skipped automatically.
Output directory structure:
<slug>/output/<stem>_ocr/
├── <stem>.md # Main markdown output
├── images/ # Extracted images referenced by markdown
└── *.pdf, *.json # Auxiliary outputs (layout, model data)
Download the entire output folder:
modal volume get speech2srt-data <slug>/output/ <local_destination>/
Preserve original directory tree.
Done. Parsed N PDF(s) in Xs
Results:
- <output_dir>/<stem>_ocr/<stem>.md
- <output_dir>/<stem>_ocr/images/
Before first run, verify:
python -Vmodal config show:
token_id null → modal setup to authenticatepip install modal then modal setup| PDF Size | Pages | Time/PDF |
|---|---|---|
| ~40-55 MB | varies | ~110-130s each |
Pipeline auto-skips PDFs with existing output.