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Use multiple LLMs and agents in JupyterLab for coding, analysis, and automation.
The material indicates an open-source, prompt-only skill with no declared secrets, remote endpoints, or local execution requirements, suggesting low overall risk. However, the missing README and unknown maintenance status leave functional details and real data boundaries to be verified before use.
The material explicitly states that no keys or environment variables are required, and there is no indication that users must provide API keys, tokens, or other sensitive credentials; based on the available facts, credential exposure or misuse risk appears low.
The system marks it as prompt-only and lists no remote endpoint hosts. Although the description mentions support for Claude Code, Copilot, Ollama, and OpenAI-compatible LLMs, no actual egress endpoints or data transfer behavior are documented, so no explicit network egress is evidenced in the provided material.
The objective classification is prompt-only, and the material does not claim that it starts local processes, runs scripts, or invokes system commands; therefore, no local code execution capability is evidenced.
No permissions are declared for reading or writing local files, notebook contents, system directories, or other resources. Although the name and description relate to JupyterLab/notebook usage, the provided material does not define any concrete data access scope, and no overbroad access is apparent.
The source is an open-source GitHub repository under GPL-3.0 with 311 stars, providing meaningful auditability and some community trust, which are clear mitigating factors. However, the missing README and unknown maintenance status make it harder to confirm actual functionality and dependency boundaries, so some supply-chain caution is still warranted.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "notebook-intelligence" yet — see the docs or source repo.
In a JupyterLab notebook, write Python code based on the current DataFrame to handle missing values, aggregate sales by month, and plot a trend chart. Explain each step.
Runnable Python analysis code with step-by-step explanations and charting logic.
Review the code cells in the current notebook, identify performance bottlenecks and duplicated logic, refactor them into a clearer and more efficient version, and explain the reasons for each change.
Refactored code suggestions with explanations of performance and maintainability improvements.
Using the connected Ollama or OpenAI-compatible model, read the current experiment notes, summarize the key findings, suggest next experimental steps, and generate a todo list.
An experiment summary, next-step recommendations, and a structured todo list for continued research or collaboration.
Let Claude Code read, edit, and run Jupyter notebooks efficiently.
Access NotebookLM programmatically to automate research, analysis, and document workflows.
Connect Google NotebookLM for research, source analysis, and content generation workflows.
Control Google NotebookLM end-to-end for research, notes, chat, and exports.
Connect to Google NotebookLM to organize sources and generate learning outputs.
Manage notes and sources, search content, and use AI models in Open Notebook.