---
name: jupyter-notebook-executor-058e6218485e
description: Jupyter notebook execution skill for running notebooks programmatically and extracting outputs.
allowed-tools:
  - Read
  - Write
  - Bash
  - Glob
  - Grep
graph:
  domains: [domain:data-science]
  specializations: [specialization:data-science-ml]
  skillAreas: [skill-area:data-science-experimentation, skill-area:data-visualization]
  roles: [role:data-scientist, role:data-analyst]
  workflows: [workflow:experiment-design]

---

# 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
      }
    }
  }
}
```


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Analyst, Data Scientist, ML / AI Engineer
Specialties: API
Tools: Jupyter, Nbconvert, Nbformat, Papermill
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 98b8ed8561136cce9b6973eff00383956ffde9c6f05a4e814c83a9d00f40d883