20 boosters for "notebook" — open source, verified from GitHub, ready to install
Helps developers quickly create structured, reproducible Jupyter notebooks for experiments and tutorials using built-in templates and helper scripts. Ideal for data scientists, researchers, and educators who need consistent notebook scaffolding.
You are a Dropbox specialist for the user's connected Dropbox account. Surface the tool's , , , and the you passed inside when the tool returned them. Never invent a field the tool did not return. Return only one JSON object (no markdown or prose outside it):
Try these methods in order. Use the first one available: If is wired as a SessionStart hook in , is injected into context automatically at session start. Skip step 1 below. To wire it: or run .
"name": "claude-blog", "description": "AI-powered blog skill suite with 30 sub-skills and 5 agents. FLOW framework integration (Find/Optimize/Win, 30 evidence-led prompts), semantic topic-cluster planning + execution, multilingual publishing (translate/localize/locale-audit), Google API integration
你是一名技术负责人（Tech Lead），具有以下专业能力： 记住：技术负责人不仅要有扎实的技术功底，还要有良好的沟通能力和团队领导力。始终以业务价值为导向，在技术实现和业务需求之间找到最佳平衡点。
A shorthand alias for the /thread command in Claude Code, providing quick access to thread management functionality for developers working with the IDE.
"version": "0.4.33", "description": "Scientific research agent extension - turns research goals into reproducible Jupyter notebooks with Python REPL, data analysis, and ML workflows", "name": "Yeachan Heo",
This skill gives you full access to a running marimo notebook. You can read cell code, create and edit cells, install packages, run cells, and inspect the reactive graph — all programmatically. The user sees results live in their
Automates downloading books from Anna's Archive and uploading them to Google NotebookLM for AI-powered document analysis. Useful for researchers and students who want to create searchable knowledge bases from books.
This skill automates downloading books from Anna's Archive and uploading them to Google NotebookLM for interactive AI-powered reading. It benefits researchers, students, and knowledge workers who want to quickly build searchable knowledge bases from downloaded books.
A fully autonomous AI research agent that ingests sources into Google NotebookLM, runs deep web research, synthesizes knowledge through cited Q&A and 9 downloadable artifact types, creates polished content drafts, and optionally publishes to social platforms.
Treat every competition as a validation problem first and a modeling problem second. The default target platform is Kaggle, so prefer Kaggle-native notebooks/scripts, datasets, model artifacts, competition submissions, and score receipts. For code competitions, assume the final notebook/kernel will
ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Jupyter (.ipynb) to marimo, or when user mentions "marimo", "reactive notebook", or asks for an interactive Python notebook. Covers marimo CLI (edit, run, convert, export), UI components (mo.ui.*), layout functions, SQL integration, caching, state management, and wigglystuff widgets. If a task involves notebooks and Python, invoke this skill first.
"description": "Independent, unofficial Kaggle.com workflow plugin: competition reports, dataset/model downloads, notebook execution, forums, writeups, submissions, and badge collection. Not affiliated with, endorsed by, or sponsored by Kaggle or Google.", "homepage": "https://github.com/shepsci/kag
"name": "@kastalien-research/thoughtbox", "packageManager": "pnpm@9.15.4", "description": "MCP server providing cognitive enhancement tools for LLM agents: structured reasoning, mental models, and literate programming notebooks",
This skill automates downloading books from Anna's Archive and uploading them to Google NotebookLM for AI-powered document analysis. It's useful for researchers and students who want to create knowledge bases from books without manual uploads.
Subagents enable deep agents to delegate work while maintaining clean context. They're useful for context quarantine and providing specialized instructions. Subagents solve the context bloat problem. When agents use tools producing large outputs (web searches, file reads, database queries), the cont
Agents merge language models with tools to build systems capable of reasoning about tasks, deciding which tools to suit the situation, and iteratively working toward solutions. The function provides a production-ready implementation. Agents follow a loop pattern: input flows to the model, which dec
"name": "@pan-sec/notebooklm-mcp", "version": "2026.2.11", "mcpName": "io.github.Pantheon-Security/notebooklm-mcp-secure",
Jupy-Juice is a Cursor IDE rules booster that provides structured guidance for building an AI-powered Jupyter notebook assistant with Pydantic AI, helping developers maintain consistent code style and project organization.
