206 skills · sorted by relevance
Operate Gemini Notebook through notebooklm-py commands or its Python API. Set up authentication, organize notebooks and sources, ask questions grounded in those sources, and generate or download outputs such as audio, reports, quizzes, and slide decks.
Use the nlm command-line tool or MCP tools to manage Gemini Notebook notebooks and sources, ask questions, discover research material, and generate content. Check account usage and reset times before work that depends on available quota.
Work with NotebookLM through an installed command-line tool to list notebooks, add URL sources, ask questions, generate artifacts, and download results. It also provides commands for checking authentication, conversation history, and sharing status.
Ask questions about documents in Google NotebookLM from Claude Code and receive source-grounded answers with citations. Manage a local notebook library, find notebooks by topic, and combine follow-up answers into a response.
NotebookLM でスライドデッキを作成する。「NotebookLM スライド」「NotebookLM スライドデッキ」「NotebookLM プレゼン」「NotebookLM デッキ」などで起動。notebooklm-mcp を使用。
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NotebookLM でインフォグラフィックを作成する。「NotebookLM インフォグラフィック」「NotebookLM で図解」「NotebookLM 視覚化」「NotebookLM でインフォグラフィック」などで起動。notebooklm-mcp を使用。
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NotebookLM でディープリサーチ（Web 調査）を実行する。「NotebookLM リサーチ」「NotebookLM で調査」「NotebookLM ディープリサーチ」「NotebookLM で調べて」などで起動。notebooklm-mcp を使用。
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NotebookLM でレポート・ブリーフィングを作成する。「NotebookLM レポート」「NotebookLM ブリーフィング」「NotebookLM 学習ガイド」「NotebookLM ブログポスト」などで起動。notebooklm-mcp を使用。
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Create and refine Jupyter notebooks for experiments, exploratory analysis, or tutorials. Bundled templates help organize runnable code and short explanations into clear steps, while validation guidance supports notebooks that others can read and rerun.
Query Google NotebookLM notebooks for source-grounded, citation-backed answers from user-uploaded documents. Manages notebook library, handles Google authentication, and supports smart discovery. Works standalone via /blog notebooklm or internally from blog-write and blog-researcher for source-grounded research context. Falls back gracefully when not configured. Use when user says "notebooklm", "notebook", "query notebook", "ask notebook", "notebook research", "source grounded research", "document query", "notebook library".
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Query Google NotebookLM notebooks for source-grounded, citation-backed answers from user-uploaded documents. Manages notebook library, handles Google authentication, and supports smart discovery. Works standalone via /blog notebooklm or internally from blog-write and blog-researcher for Tier 1 research data. Falls back gracefully when not configured. Use when user says "notebooklm", "notebook", "query notebook", "ask notebook", "notebook research", "source grounded research", "document query", "notebook library".
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Enables interaction with Google NotebookLM for advanced RAG (Retrieval-Augmented Generation) capabilities via the notebooklm-mcp-cli tool. Use when querying project documentation stored in NotebookLM, managing research notebooks and sources, retrieving AI-synthesized information, generating audio podcasts or reports from notebooks, or performing contextual queries against curated knowledge bases. Triggers on "notebooklm", "nlm", "notebook query", "research notebook", "query documentation in notebooklm".
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Research markets, competitors, companies, and technical topics using multiple sources in Google NotebookLM. Produce a structured brief with key findings and recommended actions, with optional slides, podcasts, videos, infographics, reports, or mind maps.
Bring articles, YouTube videos, documents, and other sources into NotebookLM to create audio, slides, reports, and study materials. It also supports deeper analysis through a series of questions, with optional export to 飞书 documents.
Query a user's Gemini Notebook (formerly NotebookLM) for source-grounded answers through a logged-in browser session on their Google account. Uses the account's usage limits and writes browser state. Runs only on an explicit request: /blog notebooklm, "ask my Gemini Notebook", "ask my NotebookLM notebook", or "query my notebook library". Also used by blog-write when the user named a notebook for research. Not for generic research or note-taking.
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Query a user's Gemini Notebook (formerly NotebookLM) for source-grounded answers through a logged-in browser session on their Google account. Uses the account's usage limits and writes browser state. Runs only on an explicit request: /blog notebooklm, "ask my Gemini Notebook", "ask my NotebookLM notebook", or "query my notebook library". Also used by blog-write when the user named a notebook for research. Not for generic research or note-taking.
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Turn a single document into a NotebookLM slide deck with cute characters and a clear narrative. The skill starts generation, provides a notebook link, and downloads the finished deck as a PDF when explicitly requested.
Organize research sources in self-hosted Open Notebook, then search them, ask questions with citations, create notes, transform text, or generate podcasts with multiple speakers. Supports REST API automation and configuration of local or cloud AI providers.
Query and manage Google NotebookLM notebooks with persistent profile auth, source sync, batch/multi queries, and structured exports. Use when user asks to query NotebookLM, 'ask my notebook', shares NotebookLM notebook URLs, wants to list/create notebooks, manage sources, do bulk folder sync, dedupe, or audit exports.
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This skill should be used when the user wants to query their Google NotebookLM notebooks for citation-backed, source-grounded answers, or manage notebooks, sources, and Studio content (audio, report, video, infographic, presentation, data table, flashcards, quiz, mind map). It drives the @roomi-fields/notebooklm-mcp engine — via the notebooklm MCP tools when they are available in the session, otherwise via its HTTP REST API — and covers Google login, citation formats, the daily-quota-aware batch/ingestion pattern, and source discovery.
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Create, edit, or validate Jupyter notebooks for SQL and Python analysis. Organize data sources, assumptions, calculations, and findings into a readable document that others can review and rerun, with execution gaps clearly identified.
Reference for notebook manipulation scripts (notebook_helpers.py, notebook_tools.py). Use when working with Jupyter notebooks, analyzing structure, executing cells, or manipulating notebook content.
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Explore data and develop Python code in a live Jupyter session that keeps variables between runs. Inspect objects, edit notebook cells, and optionally restart and run the whole notebook to check execution from top to bottom.
Review OpenAI Cookbook documentation and notebook prose for technical accuracy, readable wording, consistent terminology, and publication metadata. Make focused edits, preserve notebook code and outputs, and report unresolved issues with validation results.