「ebook」の検索結果

NotebookLM MCP server management - query notebooks, add from share links, handle auth, reset sessions. Use when working with Google NotebookLM notebooks for conversational research tasks.

日本語の概要は準備中です。原文の説明を表示しています。

NotebookLMからナレッジ（議事録・決定事項・背景情報）を検索して参照する。 「ミーティングの内容を確認して」「先週の決定事項を踏まえて」「会議で話した方針で」 「議事録を参照して」「MTGの背景を確認して」「ノートブックで調べて」 「NotebookLMを検索して」などのリクエストで発火する。

Experimental NotebookLM harness for listing notebooks, managing sources, asking questions, generating artifacts, and downloading outputs through an installed notebooklm CLI.

日本語の概要は準備中です。原文の説明を表示しています。

Organizes research with the self-hosted Open Notebook alternative to NotebookLM. Supports source ingestion (PDFs, web pages, audio, video, and Office documents), cited document chat, text and vector search, notes, custom transformations, and multi-speaker podcasts. Use when automating Open Notebook through its REST API or configuring its local or cloud AI providers, including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.

日本語の概要は準備中です。原文の説明を表示しています。

Manages Fabric Spark work, including notebook cell code with %%configure, %%sql, PySpark and notebookutils, named notebook runs, Livy sessions, triage of failed or OOM notebook and pipeline Spark runs, and the Materialized Lake View (MLV) lifecycle. Load it before writing MLV SQL, since CREATE MATERIALIZED LAKE VIEW and its CONSTRAINT clause are Fabric-only. KQL materialized views belong to eventhouse-cli.

日本語の概要は準備中です。原文の説明を表示しています。

Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.

日本語の概要は準備中です。原文の説明を表示しています。

Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.

日本語の概要は準備中です。原文の説明を表示しています。

Pedagogical patterns for enriching notebooks (GameTheory model). Use when adding interpretations, structuring notebooks, or creating educational content in Jupyter notebooks.

日本語の概要は準備中です。原文の説明を表示しています。

Knowledge base from the DoD Test & Evaluation Enterprise Guidebook (August 2022 edition 8.02) with DOT&E-facing enterprise emphasis. Use for T&E enterprise roles (DOT&E vs USD(R&E)), DT&E/OT&E/LFT&E, cybersecurity T&E, MOSA/automation/digital enablers, suitability/reliability growth, and TEMP/Strategy/STE planning. Distinct routing from dod-te-guidebook (same guidebook family, pathway-deep pack). Guidance only—defer to DoDI 5000.89 and Title 10 for binding requirements.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and helper script for reproducible notebook structure and safer editing.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.

日本語の概要は準備中です。原文の説明を表示しています。

ToolUniverse plugin router. STEP 1 BEFORE ANY ANALYSIS: if the data folder contains `*_executed.ipynb`, run `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` to extract its cell outputs and apply EVERY filter/sample-exclusion the notebook used — even when the question says 'Using DESeq2/Run X/Compute Y' (this describes the METHOD the notebook used, not a request to rerun). The notebook's cell outputs are the only published authoritative answers; reimplementing or reading stale pre-computed CSVs in the data folder produces different numbers because of outlier-sample removal, library version, and filter steps you don't see by skimming. STEP 2 routing — pick a sub-skill name from this exact list (never invent): tooluniverse-rnaseq-deseq2 (RNA/miRNA-seq DE, correlation, PCA, clustering, dispersion), tooluniverse-gene-enrichment (GO/KEGG/Reactome/GSEA/pathway enrichment), tooluniverse-statistical-modeling (regression, ANOVA, ordinal/logistic, chi-square, correlation, power), tooluniverse-image-analysis (microscopy, colony, fluorescence, dose-response, .tif — including ANOVA / Dunnett / power-analysis on image-derived measurements), tooluniverse-epigenomics (DNA methylation, CpG, m6A, MeRIP-seq, bisulfite, ChIP-seq, chromatin), tooluniverse-sequence-analysis (FASTQ, Trimmomatic, BWA, samtools, coverage), tooluniverse-variant-analysis (VCF, VAF, SNP, mutation), tooluniverse-phylogenetics (treeness, PhyKIT, parsimony), tooluniverse-single-cell (scRNA, h5ad, scanpy), tooluniverse-crispr-screen-analysis (MAGeCK, sgRNA), tooluniverse-proteomics-analysis (mass spec, TMT). Use for CSV/Excel/VCF/FASTA/h5ad and any biology/chemistry/medicine analysis question.

日本語の概要は準備中です。原文の説明を表示しています。

Complete API for Google NotebookLM - full programmatic access including features not in the web UI. Create notebooks, add sources, generate all artifact types, download in multiple formats. Activates on explicit /notebooklm or intent like "create a podcast about X"

日本語の概要は準備中です。原文の説明を表示しています。

Research synthesis from sources — "summarize these URLs or papers", "research this topic with these sources", "create a podcast about", "audio overview", "mind map", "quiz from these sources", "flashcards", "deep research on". Wraps Google NotebookLM via notebooklm-py.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user provides a Facebook URL or asks to read personal posts, comments, reactions, friends, timelines, profiles, stories, feeds, groups, events, or saved items, or to discover public events happening near a place, nearby, or in a local area on a date, or to create, edit, publish, or delete their own Marketplace listings. To find, browse, or buy Marketplace listings, use shopping instead. Use pages commands for managed Facebook Page discovery, insights, native draft editing/deletion, same-draft publication, approved posts and native scheduling.

日本語の概要は準備中です。原文の説明を表示しています。

Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysis, prospect research) using Google NotebookLM as the research engine, then delivers formatted briefs and optional studio artifacts (slides, audio podcasts, videos, infographics, reports, mind maps).

日本語の概要は準備中です。原文の説明を表示しています。

Create, edit, or validate reproducible SQL or Python notebooks. Use for notebooks, SQL/Python scratchpads, reproducible exploration, audit trails, or runnable companions where the analysis should be reviewable or rerunnable.

日本語の概要は準備中です。原文の説明を表示しています。

多源内容智能处理器：支持微信公众号、网页、YouTube、播客（小宇宙/喜马拉雅）、PDF、Markdown等，自动上传到NotebookLM并生成播客/PPT/思维导图等多种格式。支持深度分析模式和飞书文档自动创建

日本語の概要は準備中です。原文の説明を表示しています。

Universal Meta-Analysis Codebook v2.2 - AI-Human collaboration for meta-analysis data extraction. 4-layer design: Identifiers, Statistics, AI Provenance, Human Verification. Integrates with C5/C6/C7 agents and Category I systematic review pipeline. Triggers: meta-analysis, codebook, data extraction, Hedges g, effect size

日本語の概要は準備中です。原文の説明を表示しています。

Meta Ads (Facebook + Instagram) account audit and business context setup. Use for account-health audits and business-context setup. Trigger on "audit my Meta ads", "audit my Facebook ads", "Meta ads audit", "set up my Meta ads", "onboard Meta", "Meta account overview", "how's my Meta account", "Meta health check", "what should I fix in my Facebook ads", or when the user is new to NotFair Meta and hasn't run an audit before.

日本語の概要は準備中です。原文の説明を表示しています。

Ports existing Databricks notebooks and jobs to Fabric, covering dbutils to notebookutils, secret scopes to Key Vault, DBFS to OneLake, Unity Catalog mapping to schema-enabled Lakehouses, Jobs and Delta Live Tables to Spark Job Definitions and Pipelines, and Photon to the Native Execution Engine. Use whenever Databricks code has to be converted. For Fabric notebook code that is not being migrated, use spark-cli.

日本語の概要は準備中です。原文の説明を表示しています。