# Jupyter Notebook

> Use this tool when you need to manage and execute multiple Jupyter notebooks in a single interface, streamlining data science workflows and collaborative projects. It solves problems of notebook organization, version control, and reproducibility, allowing users to input code, data, and visualizations and output interactive, shareable results. Ideal for data scientists, researchers, and developers working on complex, iterative projects.

Canonical page: https://skillsregistry.net/skills/chengjiale150-jupyter  
JSON: https://api.skillsregistry.net/v1/skills/chengjiale150-jupyter

## Description

Enables AI-driven management and execution of Jupyter notebooks with multi-notebook support

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-04-25

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/chengjiale150-jupyter)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "chengjiale150-jupyter"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/chengjiale150-jupyter` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/chengjiale150-jupyter/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt