Ocw To Study — Development skill for Claude Code
Claude Code skill: lecture recordings (mp4) to Obsidian slide-by-slide study notes.
How to install Ocw To Study
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open royal0421/ocw-to-study and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What Ocw To Study does
Claude Code skill: lecture recordings (mp4) to Obsidian slide-by-slide study notes.
Alternatives in Development
- Obsidian MCP Server — Read, write, search, and surgically edit Obsidian vault notes, tags, and frontmatter via MCP 686 ★
- Obsidian Decide — Extract decisions from this conversation and log them to the right project notes 277 ★
- Morning Debrief Analyzer — This prompt transcribes morning debrief audio recordings using OpenAI Whisper and analyzes the transcript to e 201 ★
README
ocw-to-study
把**上課錄影 mp4** 轉成一份 Obsidian 相容的「逐頁講解」Markdown 筆記: 每一張投影片配上該段老師的逐字稿(右側固定高度捲動框)與重點拆解(下方全寬)。
這是一個 [Claude Code](https://claude.com/claude-code) skill。完整規格、每一步的判準與踩過的坑都在 [`SKILL.md`](SKILL.md)。
它做什麼
| 步驟 | 指令 | 產出 |
|---|---|---|
| 抽投影片 | vts.py extract |
從影片畫面原生解析度擷取,多幀中位數去除雷射筆光點 |
| 檢查漏頁 | vts.py coverage |
與講義 PDF 逐頁比對 |
| 轉逐字稿 | vts.py asr |
faster-whisper(有 GPU 用 large-v2),5 分鐘分段可續跑 |
| 校正專有名詞 | vts.py fix |
套用共用校正表 corrections_common.json + 各章專屬表 |
| 產出筆記 | vts.py build |
依人工審定的段落斷點組出 .md |
| 渲染預覽 | vts.py preview |
headless Chrome 截成 PNG |
段落斷點(`breaks.json`)與重點拆解(`content.json`)需要通讀逐字稿後人工產出,這一步刻意不自動化,原因見 `SKILL.md` 步驟 5。
安裝
把整個資料夾放到 Claude Code 的 skills 目錄:
~/.claude/skills/ocw-to-study/
├── SKILL.md
└── scripts/
├── vts.py 主程式(extract / coverage / asr / fix / build / preview)
├── note_utils.py 段落合併的機械規則
├── tex.py 句中符號轉 LaTeX(V_GS → $V_{GS}$)
├── formula.py 整行算式轉 LaTeX
└── corrections_common.json 跨課程共用的 ASR 誤聽校正表
之後在 Claude Code 說 `/ocw-to-study`,或提供上課錄影並要求做逐頁筆記。
依賴
- Python 3.10+
ffmpeg、ffprobe(需在 PATH)pip install faster-whisper zhconv Pillow numpy markdown PyMuPDF- Google Chrome 或 Edge(
preview用) - 選用:NVIDIA GPU+CUDA(轉錄快約 5 倍)
在 Windows 11 上開發與測試。
單獨使用腳本
python scripts/vts.py asr --root <課程資料夾> --ch 3 --video ""
python scripts/vts.py extract --root <課程資料夾> --ch 3 --video ""
python scripts/vts.py coverage --root <課程資料夾> --ch 3 --pdf "<講義.pdf>"
python scripts/vts.py fix --root <課程資料夾> --ch 3
python scripts/vts.py build --root <課程資料夾> --ch 3
python scripts/vts.py preview --root <課程資料夾> --ch 3 --batch 5
副產物寫進 `<課程資料夾>/AI/CH/`,筆記寫進 `<課程資料夾>/`。
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