Agent Skills › Prismer-AI/Prismer › jupyter

jupyter

GitHub

用于创建和执行Jupyter笔记本，支持交互式数据分析、代码执行、可视化展示及文档编写。适用于运行Python代码、处理.ipynb文件及数据探索场景。

docker/templates/data-scientist/skills/jupyter/SKILL.md Prismer-AI/Prismer

Trigger Scenarios

用户要求创建或编辑Jupyter笔记本需要交互式运行Python代码进行数据分析提及.ipynb文件或生成可视化图表

Install

npx skills add Prismer-AI/Prismer --skill jupyter -g -y

More Options

Non-standard path

npx skills add https://github.com/Prismer-AI/Prismer/tree/main/docker/templates/data-scientist/skills/jupyter -g -y

Use without installing

npx skills use Prismer-AI/Prismer@jupyter

指定 Agent (Claude Code)

npx skills add Prismer-AI/Prismer --skill jupyter -a claude-code -g -y

安装 repo 全部 skill

npx skills add Prismer-AI/Prismer --all -g -y

预览 repo 内 skill

npx skills add Prismer-AI/Prismer --list

SKILL.md

Frontmatter

{
    "name": "jupyter",
    "description": "Create and execute Jupyter notebooks for interactive data analysis using jupyter_execute and jupyter_notebook tools. Use when the user asks to run Python code interactively, create notebooks, analyze data in cells, or mentions .ipynb files."
}

Jupyter Notebook Skill

Description

Create and execute Jupyter notebooks for interactive data analysis and visualization.

Tools Used

jupyter_execute - Execute Python code in Jupyter kernel (auto-switches to Jupyter)
jupyter_notebook - Create, read, update, delete, and list notebooks
update_notebook - Add or update cells in the notebook without executing
update_gallery - Display generated plots and visualizations in gallery view
update_data_grid - Display structured tabular data (DataFrames, query results) in AG Grid
update_code - Show code examples and scripts in the Code Playground
save_artifact - Save generated artifacts (plots, data files) to workspace collection

Capabilities

Create new notebooks with proper structure
Add and execute code cells
Add markdown documentation cells
Display inline visualizations
Display tabular data in interactive grid view
Show code examples with syntax highlighting
Export to various formats (HTML, PDF)

Usage Patterns

Create Analysis Notebook

When user says: "Create a notebook for [analysis]"

Create notebook with title and imports
Add data loading cell
Add exploration cells
Structure with markdown headers
Execute cells sequentially

Execute and Debug

When user says: "Run this code"

Execute cell
Capture output and errors
If error, diagnose and fix
Show results or visualizations

Document Workflow

When user says: "Add explanation for this step"

Add markdown cell before code
Explain methodology
Note assumptions and limitations

Tool Examples

Execute Python code

jupyter_execute code="import pandas as pd\ndf = pd.read_csv('/workspace/data/results.csv')\nprint(df.describe())"

Create a notebook with cells

update_notebook cells=[{"type": "markdown", "source": "# Analysis"}, {"type": "code", "source": "import pandas as pd\nimport matplotlib.pyplot as plt"}] execute=false

Display generated plots

update_gallery images=[{"url": "/workspace/data/plot.png", "title": "Analysis Results"}]

Best Practices

Cell Independence: Each cell should run independently when possible
Import First: All imports at notebook start
Check output before proceeding: Verify execution output is correct before running dependent cells
Markdown Structure: Use headers for navigation
Save Often: Checkpoint regularly