repl

Name: repl
Availability: InStock
Author: nteract

Run persistent Python REPL sessions backed by notebook cells.

158|12|Updated Feb 11, 2026

Search Tags:#python #interactive #repl #kernel #stateful #notebook #nteract

Ready to run

npx skills add https://github.com/nteract/nteract --skill repl

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: repl
Source: https://github.com/nteract/nteract/tree/main/plugins/nightly/skills/repl
Command: npx skills add https://github.com/nteract/nteract --skill repl

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Use a notebook-backed Python REPL to perform interactive, stateful exploration where multiple cells share the same runtime and outputs are rich and visible to users.

Core Features & Use Cases

Persistent notebook session: run Python code in a single, evolving notebook that preserves variables and outputs across cells.
Dual tooling support: integrates direct pi tools (python, python_add_dependencies) and MCP notebook tools (create_notebook, create_cell, execute_cell, set_cell, get_all_cells) for flexible workflows.
Real-world scenarios: data exploration, rapid prototyping, debugging, visualization, and iterative analysis across sessions.

Quick Start

Start a notebook-backed Python session and run subsequent commands in the same persistent notebook.

Frequently Asked Questions about repl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run stateful Python code across multiple cells in an interactive notebook?▼

A notebook-backed Python REPL provides a persistent runtime environment where multiple cells share the same kernel, preserving variables and outputs for iterative data exploration and debugging.

What is the best way to keep Python variables persistent across multiple execution sessions?▼

A notebook-backed Python REPL maintains a persistent session that stores variables and outputs across cells, enabling stateful experimentation and iterative debugging without losing runtime context.

Can I use direct Python tools and MCP notebook APIs together for data exploration?▼

Yes, the Skill integrates direct Python tools like python and python_add_dependencies alongside MCP notebook APIs including create_notebook, create_cell, execute_cell, set_cell, and get_all_cells for flexible interactive workflows.

Do I need any external dependencies to use a notebook-backed Python REPL?▼

No external dependencies are required. The Skill operates using built-in direct Python tools and MCP notebook APIs to manage notebook creation, cell execution, and stateful interactions.

When should I use a stateful Python REPL instead of running standalone scripts?▼

Use a stateful Python REPL for iterative data exploration, rapid prototyping, debugging, and visualization where variables and outputs must persist across multiple cells rather than running isolated standalone scripts.

Why does my Python REPL lose variable state between execution cells?▼

Variable state is lost without a notebook-backed persistent session. A stateful Python REPL uses a shared kernel to preserve variables and outputs across multiple cells, preventing context loss during iterative exploration.