菜单
Convert PDF to clean Markdown with image content described as text. Use when user wants to convert a PDF to markdown, extract content from PDF, or prepare PDF content for AI tools.
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。
基于 SOC 职业分类
| name | pdf-to-markdown |
| description | Convert PDF to clean Markdown with image content described as text. Use when user wants to convert a PDF to markdown, extract content from PDF, or prepare PDF content for AI tools. |
| allowed-tools | Read, Bash |
| version | 1.0.0 |
Convert PDF files to clean, well-structured Markdown. Tables become markdown tables. Images and graphics are described as text (no image files generated).
uv run skills/pdf-to-markdown/scripts/convert_to_markdown.py input.pdf
Output: ~/Desktop/{filename}.md
| Flag | Description |
|---|---|
--no-llm | Skip LLM processing (faster, images become [Image] placeholders) |
--force-ocr | Force OCR on all pages (for scanned PDFs) |
--page-range "0,5-10" | Process specific pages only |
uv run skills/pdf-to-markdown/scripts/convert_to_markdown.py ~/Documents/report.pdf
uv run skills/pdf-to-markdown/scripts/convert_to_markdown.py report.pdf ~/Documents/report.md
uv run skills/pdf-to-markdown/scripts/convert_to_markdown.py --no-llm report.pdf
uv run skills/pdf-to-markdown/scripts/convert_to_markdown.py --force-ocr scanned_doc.pdf
uv run skills/pdf-to-markdown/scripts/convert_to_markdown.py --page-range "0-5" large_report.pdf
--no-llm flag if you don't have Gemini API accessThe first run downloads ML models (~1-2GB) which are cached at ~/.cache/marker/. Subsequent runs are faster.
Uses Marker library:
Automate the version bump ritual — three independent tracks (marketplace, psd-coding-system, psd-productivity)
Auto-evolve the plugin — compounds learnings into CLAUDE.md/patterns/agents then prunes them, checks releases, compares competition, contributes patterns
Autonomous build-to-done — implement, verify the full Definition of Done (build/lint/typecheck/full suite/Playwright + screenshots), open a PR with visual evidence, then watch CI and the AI reviewers and fix every round until 100% clean. Does not stop until done.
Clarify intent, research in parallel, design the approach, and emit a task breakdown + a machine-checkable Definition of Done — optionally filing contract-compliant GitHub issues that /lfg can pick up and drive to done.
Sign, notarize, and package a macOS .app into a .pkg for PSD Jamf Self Service deployment — full Apple Developer ID pipeline (sign → notarize → staple → pkgbuild → notarize pkg → verify). Triggers on "sign app", "notarize app", "package for jamf", "build pkg".
Configure this project's verification gate and review agents — writes .psd/verify.json (build/lint/test/typecheck/e2e commands, E2E flows, strictness, AI-reviewer logins, learnings, active review agents)