jupyter-notebook-executor
Jupyter notebook execution skill for running notebooks programmatically and extracting outputs.
Best use case
jupyter-notebook-executor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Jupyter notebook execution skill for running notebooks programmatically and extracting outputs.
Teams using jupyter-notebook-executor should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/jupyter-notebook-executor/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How jupyter-notebook-executor Compares
| Feature / Agent | jupyter-notebook-executor | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Jupyter notebook execution skill for running notebooks programmatically and extracting outputs.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# jupyter-notebook-executor
## Overview
Jupyter notebook execution skill for running notebooks programmatically, parameterizing inputs, and extracting outputs for ML workflows.
## Capabilities
- Parameterized notebook execution
- Output extraction and validation
- Notebook conversion (to HTML/PDF)
- Cell execution control
- Error handling and reporting
- Environment management
- Kernel specification
- Timeout management
## Target Processes
- Exploratory Data Analysis (EDA) Pipeline
- Model Interpretability and Explainability Analysis
- Experiment Planning and Hypothesis Testing
## Tools and Libraries
- papermill
- nbconvert
- jupyter
- nbformat
## Input Schema
```json
{
"type": "object",
"required": ["action", "notebookPath"],
"properties": {
"action": {
"type": "string",
"enum": ["execute", "convert", "extract", "validate"],
"description": "Action to perform on the notebook"
},
"notebookPath": {
"type": "string",
"description": "Path to the Jupyter notebook"
},
"executeConfig": {
"type": "object",
"properties": {
"parameters": { "type": "object" },
"outputPath": { "type": "string" },
"kernel": { "type": "string" },
"timeout": { "type": "integer" },
"cwd": { "type": "string" }
}
},
"convertConfig": {
"type": "object",
"properties": {
"format": { "type": "string", "enum": ["html", "pdf", "markdown", "script"] },
"outputPath": { "type": "string" },
"template": { "type": "string" },
"excludeInput": { "type": "boolean" },
"excludeOutput": { "type": "boolean" }
}
},
"extractConfig": {
"type": "object",
"properties": {
"cellTags": { "type": "array", "items": { "type": "string" } },
"outputTypes": { "type": "array", "items": { "type": "string" } },
"variableNames": { "type": "array", "items": { "type": "string" } }
}
}
}
}
```
## Output Schema
```json
{
"type": "object",
"required": ["status", "action"],
"properties": {
"status": {
"type": "string",
"enum": ["success", "error", "timeout"]
},
"action": {
"type": "string"
},
"executionResult": {
"type": "object",
"properties": {
"outputPath": { "type": "string" },
"executionTime": { "type": "number" },
"cellsExecuted": { "type": "integer" },
"errors": { "type": "array" }
}
},
"conversionResult": {
"type": "object",
"properties": {
"outputPath": { "type": "string" },
"format": { "type": "string" }
}
},
"extractedData": {
"type": "object",
"properties": {
"variables": { "type": "object" },
"outputs": { "type": "array" },
"figures": { "type": "array", "items": { "type": "string" } }
}
}
}
}
```
## Usage Example
```javascript
{
kind: 'skill',
title: 'Execute EDA notebook with parameters',
skill: {
name: 'jupyter-notebook-executor',
context: {
action: 'execute',
notebookPath: 'notebooks/eda_template.ipynb',
executeConfig: {
parameters: {
data_path: 'data/train.csv',
output_dir: 'results/eda/',
sample_size: 10000
},
outputPath: 'notebooks/eda_results.ipynb',
kernel: 'python3',
timeout: 3600
}
}
}
}
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