python-repl

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Interactive Python REPL automation with common helpers and best practices

⭐4.1kstars🔀344forks📁View on GitHub🕐Updated Jan 4, 2026
[Developer Tools]

When & Why to Use This Skill

The Python REPL skill enhances interactive coding workflows by providing automated helpers for data analysis, debugging, and performance profiling. It streamlines Python development within Claude by bundling common imports and utility functions, allowing for faster iteration, deeper code introspection, and more efficient execution patterns.

Use Cases

  • 1. Rapid Data Exploration: Quickly inspect DataFrames and generate visualizations using bundled helpers like inspect_df and quick_plot without writing repetitive boilerplate code.
  • 2. Code Debugging and Introspection: Use the describe_object utility to perform deep introspection of Python objects, making it easier to understand complex data structures during development.
  • 3. Performance Optimization: Identify bottlenecks in your code by using the time_function decorator to profile execution speed directly within the interactive environment.
  • 4. Automated Workflow Setup: Save time with pre-configured common imports (pandas, numpy) and optimized display settings, ensuring a ready-to-use environment for every session.
namepython-repl
descriptionInteractive Python REPL automation with common helpers and best practices

Python REPL Skill

Enhances Python REPL workflows with bundled utility functions for data analysis, debugging, and performance profiling.

Overview

This skill bundles Python REPL helpers, common imports, and execution patterns for efficient Python development in gptme.

Bundled Scripts

Helper Functions (python_helpers.py)

This skill includes bundled utility functions for common Python tasks:

  • Data inspection (inspect_df, describe_object)
  • Quick plotting (quick_plot)
  • Performance profiling (time_function)
  • Common imports setup (setup_common_imports)

Usage Patterns

Data Analysis

When working with data, automatically import common libraries and set up display options:

import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 100)

Debugging

Use bundled helpers for debugging:

from python_helpers import inspect_df, describe_object
inspect_df(df)  # Quick dataframe overview
describe_object(obj)  # Object introspection

Dependencies

Required packages are listed in requirements.txt:

  • ipython: Interactive Python shell
  • numpy: Numerical computing
  • pandas: Data manipulation

Best Practices

  1. Use helpers: Leverage bundled helper functions instead of reimplementing
  2. Import once: Common imports are handled by pre-execute hook
  3. Profile performance: Use time_function for performance-sensitive code

Examples

Quick Data Analysis

# Helpers auto-import pandas, numpy
df = pd.read_csv('data.csv')
inspect_df(df)  # Show overview

Performance Profiling

from python_helpers import time_function

@time_function
def slow_operation():
    # Your code here
    pass
  • Tool: ipython