Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add azuma520/youtube-to-notebooklm --skill anything-to-notebooklmgit clone --depth 1 https://github.com/azuma520/youtube-to-notebooklmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/azuma520/youtube-to-notebooklm/anything-to-notebooklm)<a href="https://agentmods.dev/skills/azuma520/youtube-to-notebooklm/anything-to-notebooklm"><img src="https://agentmods.dev/badge/skills/azuma520/youtube-to-notebooklm/anything-to-notebooklm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/azuma520/youtube-to-notebooklm/anything-to-notebooklm"><img src="https://agentmods.dev/badge/skills/azuma520/youtube-to-notebooklm/anything-to-notebooklm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00271 | $0.02062 |
| Opus 5 | $0.00135 | $0.01031 |
| Sonnet 5 | $0.00054 | $0.00412 |
| Haiku 4.5 | $0.00027 | $0.00206 |
Grade A, and why
anything-to-notebooklm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
多源內容 → NotebookLM 處理器
從多種來源取得內容，上傳到 NotebookLM 生成各類成品；或瀏覽、查詢、提問既有 notebook。
依賴：notebooklm-py（CLI v0.3.3+）、markitdown（僅限 PPTX/XLSX/EPUB 轉換）。首次使用須 notebooklm login。
Decision Tree
用戶請求
├── 想找/瀏覽/查詢/下載既有 notebook 或成品？
│ └── Workflow B：瀏覽 → 閱讀 → 提問
├── 有內容要上傳 / 要生成新東西？
│ └── Workflow A：上傳 → 生成 → 下載
└── 不確定 → 詢問用戶
Workflow A：上傳 → 生成 → 下載
Step 1：識別內容源
| 輸入特徵 | 處理方式 |
|---|---|
| YouTube URL | source add "URL" → source wait <id> |
| 網頁 URL | source add "URL" → source wait <id> |
| Google Drive 文件 | source add-drive <file_id> "title" |
本地 .pdf/.docx/.md/.csv/.txt |
source add "filepath" → source wait <id> |
本地 .pptx/.xlsx/.epub |
markitdown "file" -o "$TEMP/converted.md" → source add |
| 本地圖片/音訊 | source add "filepath" → source wait <id>（自動 OCR/轉錄） |
| 純關鍵詞 | source add-research "query"（見下方） |
Step 2：建立 notebook + 添加來源
notebooklm create "筆記本標題"
notebooklm source add "來源" # 上傳來源
notebooklm source wait <source_id> # 等待處理完成
- 一個 notebook 最多 50 個 source，全部
source wait完再生成 - 上傳前確認：告知用戶即將建立的 notebook 名稱，確認後再執行
Step 3：生成
預設行為是非阻塞（除 mind-map 外）。互動場景加 --wait 等待完成。
| 用戶意圖 | 指令 |
|---|---|
| 播客/音頻 | generate audio --wait |
| PPT/簡報 | generate slide-deck --wait |
| 思維導圖 | generate mind-map（同步，自動等待） |
| Quiz/出題 | generate quiz --wait |
| 報告/總結 | generate report --wait |
| 視頻 | generate video --wait |
| 信息圖 | generate infographic --wait |
| 閃卡 | generate flashcards --wait |
| 數據表 | generate data-table "description" --wait |
用戶沒指定生成什麼 → 只上傳不生成，等後續指令。
各類型的 format/style/length 參數 → 見 references/generate-options.md。
Step 4：下載
notebooklm download <type> ./output.<ext>
常用選項：--latest（最新成品）、--format <fmt>（輸出格式）、--all（全部下載）。
完整下載選項 → 見 references/generate-options.md。
AI 研究搜尋
用戶給的是純關鍵詞時，用 NotebookLM 內建研究功能：
notebooklm source add-research "query" --mode deep --from web --import-all
notebooklm research wait # 等待研究完成
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 182 lines · 271 tokens per session scan A 483d9c5f0f77
anything-to-notebooklm is a skill published in the GitHub repository azuma520/youtube-to-notebooklm (38 stars, last pushed 6mo ago), licensed MIT. It adds 271 tokens to every session and 2,062 once invoked, about $0.0014 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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