菜单
Convert research paper PDFs to Markdown with structured figure data extraction. Extracts panel-level CSV tables or descriptions from scientific figures (CNS-level papers). Use when the user wants to convert a PDF paper to MD with figure data.
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/BrainStOrmics/pdf2md-skill --skill pdf2md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。
运行 PDF 转 Markdown pipeline — 执行 7 步转换流程（PDF 解析、图片过滤、面板检测、描述生成、表格提取、CSV 提取、MD 组装）、质量检查、故障处理。Use when the user wants to convert a PDF paper to Markdown, run the pipeline, or check output quality.
VLM API 与工作流配置 — 配置 VLM 提供商（API key、模型、endpoint）、修改 pipeline 工作流参数、理解输入输出格式。Use when the user needs to configure API keys, change VLM models, adjust pipeline parameters, or understand the I/O contract.
环境配置 — 搭建 pdf2md 运行所需的 conda/uv 环境、安装依赖、配置 HF 中国镜像、预下载模型。Use when the user needs to set up the environment, install dependencies, fix import errors, or download models.
基于 SOC 职业分类
正在显示 SKILL.md
| name | pdf2md |
| description | Convert research paper PDFs to Markdown with structured figure data extraction. Extracts panel-level CSV tables or descriptions from scientific figures (CNS-level papers). Use when the user wants to convert a PDF paper to MD with figure data. |
Convert CNS-level research paper PDFs into structured Markdown, with scientific figures decomposed into individual panels and their numerical data extracted as CSV tables.
This project is organized into four sub-skills:
| Skill | Path | Purpose | Trigger When |
|---|---|---|---|
| pdf2md-setup | skills/setup/SKILL.md | Environment configuration | Set up conda/uv env, install deps, configure HF mirror, download models |
| pdf2md-config | skills/config/SKILL.md | VLM API & workflow configuration | Configure API keys, change VLM models, adjust CLI parameters |
| pdf2md-run | skills/run/SKILL.md | Pipeline execution | Convert a PDF, run the pipeline, check output quality |
| pdf2md-retry | skills/retry/SKILL.md | Retry failed panels | Fix network errors, retry failed panel extractions, recover from API timeouts |
# 1. Set up environment
conda activate pdf2md # See skills/setup/SKILL.md
# 2. Configure VLM API
# Edit config.yaml with your API key # See skills/config/SKILL.md
# 3. Run the pipeline
python examples/pipeline.py /path/to/paper.pdf # See skills/run/SKILL.md
pdf2md-skill/
├── SKILL.md # This file — project index
├── config.example.yaml # VLM config template (tracked by git)
├── config.yaml # Your VLM config (gitignored)
├── skills/
│ ├── setup/SKILL.md # Environment setup sub-skill
│ ├── config/SKILL.md # VLM & workflow config sub-skill
│ ├── run/SKILL.md # Pipeline execution sub-skill
│ └── retry/SKILL.md # Retry failed panels sub-skill
├── references/
│ ├── architecture.md # Implementation details
│ ├── figure_rules.md # 9-rule VLM classification prompt
│ └── troubleshooting.md # Problem-solution lookup
└── examples/
├── pipeline.py # Reference implementation (~620 lines)
├── check_failures.py # Check for failed panel extractions
└── retry_failures.py # Retry failed panels with VLM API
skills/setup/SKILL.md — Environment installation and hardware requirementsskills/config/SKILL.md — VLM API configuration and CLI parametersskills/run/SKILL.md — 7-step pipeline execution, quality checks, failure strategiesskills/retry/SKILL.md — Retry failed panel extractions, fix network errorsreferences/figure_rules.md — 9-rule panel classification promptreferences/architecture.md — Implementation details (text truncation fix, VLM response handling, memory management)references/troubleshooting.md — Problem-solution tableexamples/pipeline.py — Complete reference implementationexamples/check_failures.py — Check for failed panel extractionsexamples/retry_failures.py — Retry failed panels with parallel processing