207 skills · sorted by relevance
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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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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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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Review Jupyter notebooks for side effects, dependencies, and safe execution strategy before running or editing them.
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Review homework notebooks for completeness, reproducibility, safe execution, and academic-integrity boundaries.
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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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Plan and review machine learning systems for production use, covering data requirements, repeatable training, quality checks, deployment, monitoring, and rollback. Produce concrete plans and review findings for moving model experiments into operational systems.
Limit file edits to a chosen directory and block Edit, Write, and NotebookEdit operations outside it. Keep debugging or module changes within scope, with the restriction remaining active until explicitly removed.
Automate Joplin note management through its terminal application. Create and organize notes and notebooks, update to-dos, manage tags and attachments, and run synchronization or import and export workflows with structured results.
Helps build LabArchives connections for electronic lab notebooks and laboratory inventory. Guides endpoint selection, request signing for authentication, and user authorization, with local tools to inspect container files and prepare redacted request plans before remote operations.
Automate lab data tasks in Benchling using its Python SDK and REST API. Import and export sequences, inspect sample inventory, and work with electronic lab notebooks and workflow tasks while checking schemas, permissions, and operation results.
Save research findings, fixes, and decisions in a shared Markdown notebook on disk. Search and organize notes across sessions, record when they inform actual work, and let unused notes expire automatically.
Search an agent's long-term memory for people, projects, and other facts. Save important corrections immediately, update outdated entries, and remove errors or duplicates while respecting which notebooks the current conversation can read or change.
Choose, build, and review charts that answer a clear analytical question. The skill checks chart types, labels, units, colors, and data sufficiency, then refines the visual in its intended report, dashboard, notebook, or other output.
Prepare scientific figures for papers, reports, and saved deliverables by checking data fidelity, chart choice, labels, axes, and color consistency. Verify exported figures for overlapping text and readability while keeping fonts, palettes, and framing configurable.
Save decisions, research findings, and work updates to Obsidian, then update related notes, daily entries, and task boards. Maintain existing pages, preserve changing facts with their history, and check for duplicates and broken links.
Search Kaggle datasets, competitions, notebooks, and models through its official command-line tool. Download public data into a chosen directory and preserve audit records and file hashes, identifiers used to check downloaded files.
Converts LabArchives notebook data, entry metadata, and authorized ELN exports into manuscript-ready academic writing outputs such as Methods sections, data-availability statements, reproducibility appendices, experiment timelines, and submission support notes. Optional bundled scripts can be used to collect or validate source notebook data before writing.
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Authoring conventions for exploratory data analysis notebooks and analytical dashboards, covering section sequence, visualization selection, scale thresholds, caching and state, and dashboard validation budgets. Use when composing or reviewing an EDA notebook, an analytical dashboard, or a dashboard test pass.
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