What makes jupyter-notebook-manager notable?

1K downloads. Trust evidence available

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jupyter-notebook-manager

Complete Jupyter notebook management system with creation, execution, debugging, and analysis capabilities Skill: jupyter-notebook-manager Owner: sheng7564 Summary: Complete Jupyter notebook management system with creation, execution, debugging, and analysis capabilities Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-16T04:00:55.218Z | user first commit Archive index: Archive v1.0.0: 10 files, 28798 bytes Files: README.md (10618b), requirements.txt (391b), scripts/notebook_creator.py (24401b), scripts/notebook_execu

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.0release · observed Apr 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s179v11yrph12syd1q1a2qzttn84yqka:jupyter-notebook-manager

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-sheng7564-jupyter-notebook-manager/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

33,237 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: jupyter-notebook-manager
version: 1.0.0
author: AI Skills Community
description: Complete Jupyter notebook management system with creation, execution, debugging, and analysis capabilities
tags:
  - jupyter
  - data-science
  - python
  - notebook
  - analysis
category: data-science
requires:
  - jupyter
  - nbformat
  - nbconvert
  - pandas
trigger_keywords:
  - jupyter
  - notebook
  - ipynb
  - data analysis
  - create notebook
  - run notebook
  - debug notebook
  - execute notebook
  - analyze data
---

# Jupyter Notebook Manager

Complete Jupyter notebook management system that enables Claude to create, execute, debug, analyze, and optimize Jupyter notebooks with deep integration of data science workflows.

## 🎯 When to Use This Skill

### Trigger Conditions

Use this skill when you encounter:

1. **User mentions Jupyter-related keywords**:
   - "create a Jupyter notebook"
   - "run this notebook"
   - "debug my .ipynb file"
   - "analyze notebook results"
   - "optimize my notebook"

2. **User requests data analysis workflows**:
   - "set up data analysis pipeline"
   - "perform data cleaning"
   - "visualize analysis results"
   - "generate analysis report"

3. **User provides .ipynb files**:
   - Detecting .ipynb file references
   - User uploads notebook files
   - Working directory contains notebooks

4. **User needs notebook operations**:
   - "convert notebook to Python script"
   - "extract code from notebook"
   - "merge multiple notebooks"
   - "generate notebook template"

## 🚀 Core Capabilities

### 1. Notebook Creation & Templates

**When**: User needs to create new notebooks for specific analysis tasks

**Capabilities**:
- Generate notebooks from scratch with proper structure
- Provide domain-specific templates (EDA, ML, visualization)
- Add markdown documentation and code cells
- Configure kernel and metadata
- Support custom templates

**Example**:
```python
# User: "Create a data analysis notebook for sales data"
# → Generates structured notebook with:
#   - Import cells (pandas, numpy, matplotlib)
#   - Data loading section
#   - EDA section with common analyses
#   - Visualization section
#   - Summary section
```

### 2. Notebook Execution & Monitoring

**When**: User needs to run notebooks and track execution

**Capabilities**:
- Execute notebooks programmatically
- Monitor execution progress
- Capture outputs and errors
- Handle long-running cells
- Support parameterized execution

**Example**:
```python
# User: "Run analysis.ipynb with dataset=sales_2024.csv"
# → Executes notebook with parameters
# → Shows real-time progress
# → Captures all outputs
# → Reports execution time and status
```

### 3. Debugging & Error Analysis

**When**: Notebook execution fails or produces unexpected results

**Capabilities**:
- Identify error cells and stack traces
- Analyze variable states at error points
- Suggest fixes for common issues
- Detect dependency problems
- Check data quality issues

**Example**:
```python
# Us

README.md

# 🪐 Jupyter Notebook Manager

**Complete Jupyter notebook management system for Claude AI**

A comprehensive skill that enables Claude to create, execute, debug, analyze, and optimize Jupyter notebooks with deep integration of data science workflows.

[![Version](https://img.shields.io/badge/version-1.0.0-blue.svg)](https://github.com/ai-skills/jupyter-notebook-manager)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Python](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org)

---

## ✨ Features

###  **8 Core Capabilities**

1. 📝 **Notebook Creation** - Generate notebooks from templates (EDA, ML, Cleaning, etc.)
2. ▶️ **Notebook Execution** - Run notebooks with monitoring and parameter injection
3. 🐛 **Debugging & Analysis** - Identify errors, analyze state, suggest fixes
4. 🔍 **Variable Inspection** - Explore variables, dataframes, and memory usage
5. ⚡ **Code Optimization** - Detect bottlenecks, suggest improvements
6. 🔄 **Format Conversion** - Convert between .ipynb, .py, HTML, PDF
7. 📊 **Results Reporting** - Extract insights and generate summaries
8. 👥 **Collaboration** - Compare versions, merge changes, resolve conflicts

---

## 🚀 Quick Start

### Installation

```bash
# Install dependencies
pip install -r requirements.txt

# Verify installation
python scripts/notebook_creator.py --list-templates
```

### Basic Usage

#### 1. Create a Notebook

```bash
# Create from template
python scripts/notebook_creator.py \
  --template exploratory-data-analysis \
  --output my_analysis.ipynb \
  --data-file sales_data.csv \
  --title "Q4 Sales Analysis"
```

#### 2. Execute a Notebook

```bash
# Run notebook
python scripts/notebook_executor.py \
  my_analysis.ipynb \
  --output results.ipynb

# With parameters
python scripts/notebook_executor.py \
  my_analysis.ipynb \
  --param dataset=Q4_data.csv \
  --param year=2024
```

#### 3. Use with Claude

Just tell Claude what you need:

```
User: "Create a data analysis notebook for my sales data"

Claude: I'll create an EDA notebook for you...
[Creates structured notebook with all analysis sections]

User: "Run it with my Q4_sales.csv file"

Claude: Executing notebook with your data...
[Shows real-time progress and results]
```

---

## 📖 Documentation

### Available Templates

| Template | Purpose | Use Case |
|----------|---------|----------|
| `exploratory-data-analysis` | Comprehensive EDA | Understand new datasets |
| `machine-learning-training` | ML model pipeline | Train and evaluate models |
| `data-cleaning` | Data quality | Clean messy data |
| `time-series-analysis` | Time series forecasting | Temporal data |
| `statistical-testing` | Hypothesis testing | Statistical analysis |
| `visualization-dashboard` | Interactive viz | Present results |
| `blank` | Minimal structure | Start from scratch |

### Script Reference

#### notebook_creator.py

```bash
python scripts/notebook_creator.py [OPTIONS]

Options:
  --template TEXT          T

tests/README.md

# Jupyter Notebook Manager - Test Suite

This directory contains comprehensive test cases for the jupyter-notebook-manager skill.

## Test Structure

```
tests/
├── README.md (this file)
├── test_creator.py          # Unit tests for notebook_creator.py
├── test_executor.py          # Unit tests for notebook_executor.py
├── test_integration.py       # Integration tests
├── test_scenarios/           # Real-world scenario tests
│   ├── scenario_01_eda.md
│   ├── scenario_02_ml.md
│   ├── scenario_03_debug.md
│   └── scenario_04_optimize.md
└── fixtures/                 # Test data and notebooks
    ├── sample_data.csv
    ├── simple_notebook.ipynb
    ├── error_notebook.ipynb
    └── large_notebook.ipynb
```

## Test Categories

### 1. Unit Tests

**Purpose**: Test individual functions and methods

- `test_creator.py`: Notebook creation logic
  - Template loading
  - Cell generation
  - Metadata handling
  - Custom cell injection

- `test_executor.py`: Notebook execution logic
  - Execution flow
  - Parameter injection
  - Error capture
  - Output extraction

### 2. Integration Tests

**Purpose**: Test end-to-end workflows

- `test_integration.py`:
  - Create → Execute workflow
  - Execute → Analyze workflow
  - Multi-step operations

### 3. Scenario Tests

**Purpose**: Test real-world use cases

- **Scenario 1: Exploratory Data Analysis**
  - User provides CSV file
  - Create EDA notebook
  - Execute and analyze results
  - Validate output quality

- **Scenario 2: Machine Learning Pipeline**
  - User requests ML model training
  - Create ML notebook
  - Execute with different parameters
  - Compare model performances

- **Scenario 3: Debugging Failed Notebook**
  - User reports notebook error
  - Identify error cell
  - Analyze error cause
  - Suggest fix

- **Scenario 4: Notebook Optimization**
  - User complains about slow execution
  - Profile notebook performance
  - Identify bottlenecks
  - Provide optimized version

## Test Data

### Sample Datasets

1. **sample_data.csv** - Clean dataset for testing
   - 1000 rows, 10 columns
   - Mix of numeric and categorical
   - No missing values
   - Known patterns for validation

2. **messy_data.csv** - Dirty dataset for cleaning tests
   - Missing values (20%)
   - Duplicates (5%)
   - Outliers
   - Type inconsistencies

3. **large_data.csv** - Performance testing
   - 100,000 rows
   - Memory stress test
   - Optimization opportunities

### Test Notebooks

1. **simple_notebook.ipynb** - Basic operations
   - Simple calculations
   - Quick execution (<5s)
   - All cells succeed

2. **error_notebook.ipynb** - Error scenarios
   - Cell 5 has KeyError
   - Cell 8 has TypeError
   - Tests error handling

3. **long_running_notebook.ipynb** - Timeout testing
   - Has sleep(60) call
   - Tests timeout handling

4. **large_notebook.ipynb** - Scale testing
   - 100+ cells
   - Large outputs
   - Tests memory handling

## Running Tests

### Quick Test

```bash
# Run all tests
python -m pytest tests/ -v

# Run s

_meta.json

{
  "ownerId": "kn740584p7nqabpxt7wtpn9c2184y18n",
  "slug": "jupyter-notebook-manager",
  "version": "1.0.0",
  "publishedAt": 1776312055218
}

skill-card.md

## Description:

Complete Jupyter notebook management system with creation, execution, debugging, and analysis capabilities.

This skill is ready for commercial/non-commercial use.

## Publisher:

[sheng7564](https://clawhub.ai/user/sheng7564)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and data practitioners use this skill to create, execute, debug, analyze, and optimize Jupyter notebooks for data science workflows. It can generate notebook templates, run notebooks with parameters, capture execution results, and provide guidance on notebook errors or performance issues.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Notebook execution can run code from notebooks and injected parameters.

Mitigation: Use the skill only with trusted notebooks and trusted parameter values, and run it in an isolated environment with restricted filesystem and network access.

Risk: Generated notebooks may contain incorrect, unsafe, or unsuitable code before review.

Mitigation: Review generated notebooks before running them, especially cells that load data, write files, install packages, or execute shell commands.

Risk: Notebook execution may overwrite the input notebook when no output path is provided.

Mitigation: Provide an explicit output path for executed notebooks to preserve the original file.

## Reference(s):

- [ClawHub skill listing](https://clawhub.ai/sheng7564/skills/jupyter-notebook-manager)
- [Jupyter Documentation](https://jupyter.org/documentation)
- [nbformat Specification](https://nbformat.readthedocs.io/)
- [nbconvert Guide](https://nbconvert.readthedocs.io/)
- [Papermill Documentation](https://papermill.readthedocs.io/)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]

**Output Format:** [Markdown guidance with Python and shell command examples; generated or executed Jupyter notebook files and execution status summaries.]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May create or overwrite .ipynb files and may include captured notebook outputs or error summaries.]

## Skill Version(s):

1.0.0 (source: SKILL.md frontmatter and server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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