「notebook」の検索結果

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.

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

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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.

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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.

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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.

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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"

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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.

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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).

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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.

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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

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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.

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Migrates Synapse Data Factory pipelines to Fabric Data Factory, turning linked services into Fabric connections, inlining datasets into activities, converting global parameters to Variable Libraries, and mapping SynapseNotebook to TridentNotebook. Use for pipeline JSON. For Synapse Spark notebook code use synapse-migration.

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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.

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Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for programmatic ELN workflows.

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Use this when creating, editing, executing, or reviewing Jupyter notebooks — including cell structure, kernel management, extracting reusable logic to src/, and ensuring notebooks are restartable.

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Sync high-value dev knowledge (skills, decisions, lessons) into Google NotebookLM for cloud recall, podcasts, and flashcards. Pairs with Dockit/deep-search. Use for NotebookLM, nlm, knowledge base, skill sync, or cross-machine AI memory.

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Use NotebookLM through MCP or a local REST API to run cited Q&A, generate Studio artifacts, and manage high-volume research batches.

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Execute Jupyter notebooks using local scripts or MCP Jupyter. Arguments: <notebook_path> [--mode] [--cells] [--timeout] [--batch] [--save]

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Validate the GenAI stack (services, authentication, models, notebooks, GPU). Arguments: [all|services|auth|models|notebooks|vram] [--full] [--auth-only] [--nodes-only] [--notebooks]

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Skill de pesquisa profunda alimentado por NotebookLM MCP. Conduz pesquisa estruturada em múltiplas fontes (análise de mercado, inteligência competitiva, análise de tendências, pesquisa de prospects) usando Google NotebookLM como mecanismo de pesquisa, depois entrega briefs formatados e artefatos opcionais de estúdio (slides, podcasts de áudio, vídeos, infográficos, relatórios, mapas mentais).

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Set up notebook-connector configuration for Exasol AI workflows through the Secrets Python API. Covers secure config store (`scs`) creation, the `AILabConfig` and `StorageBackend` key sources, backend-specific config values such as `db_host_name`, `db_schema`, `storage_backend`, and `huggingface_token`, Python validation, and handoff to downstream notebook-connector skills.

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Учебные аудиоподкасты для Антона в NotebookLM: выбирает, чему учить (труба собеседований, дыры сверки), пишет пакет и промпт ведущим, генерирует аудио RU+EN через CLI notebooklm, кладёт в волт, ведёт учебный план и YouTube-очередь. Триггеры: /teach-podcast, /teach, сделай подкаст, обучи меня, подкаст в машину, что мне послушать. НЕ /notebooklm (общий слой артефактов).

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Drive NotebookLM over a programmatic CLI with no browser: turn a vault slice into an audio overview, study guide or mind map, and fold the artifacts back into the vault. Browser automation is the fallback if CLI auth breaks. Triggers: "/notebooklm", "make an audio digest of <topic>", "podcast from the vault".

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Review Jupyter notebooks for side effects, dependencies, and safe execution strategy before running or editing them.

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。