메뉴
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.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/BrainStOrmics/pdf2md-skill --skill pdf2md명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
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.
SOC 직업 분류 기준
SKILL.md 표시 중
| 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