coderunner

🥈Silver

Coderunner provides a secure local sandbox to run LLM-generated code using Apple containers. It benefits operations teams by enabling safe execution of code from LLMs like Claude. It connects to workflows where code generation and testing are required, such as software development and IT operations.

813300Updated Today

Intermediate30min to implementautomation

Saves ~90 min per use

Quick InstallView Source

git clone https://github.com/instavm/coderunner.git

Works with:

Claude

Overview

About This Skill

The coderunner skill provides a secure local sandbox environment specifically designed for executing code generated by large language models (LLMs) using Apple containers. This automation skill allows developers to safely test and run code snippets without the risk of exposing their systems to potential vulnerabilities. By leveraging Apple containers, coderunner ensures that the execution environment is isolated, which enhances security and stability during the development process. One of the key benefits of using coderunner is its ability to streamline the workflow for developers and AI practitioners. While the exact time savings are not quantified, the skill significantly reduces the overhead associated with setting up secure environments for testing LLM-generated code. By implementing coderunner, users can focus more on development and less on environment configuration, leading to increased productivity and efficiency. This skill is particularly beneficial for developers, product managers, and AI practitioners who are involved in projects that utilize LLMs. It is suitable for those working in tech-focused departments where code generation and testing are frequent tasks. For instance, a developer working on an AI-powered application can use coderunner to quickly test code suggestions made by an AI model, ensuring that the code is functional and secure before integrating it into larger projects. Implementing coderunner requires an intermediate level of expertise and typically takes about 30 minutes. Users should have a basic understanding of local development environments and familiarity with Apple containers. As businesses increasingly adopt AI-first workflows, skills like coderunner become essential tools in the developer's toolkit, enabling seamless integration of AI-generated solutions into everyday coding practices.

How to Use

[{"step":"Prepare your code snippet. Write or paste the Python code you want to test in the prompt template, ensuring it includes all necessary imports and dependencies.","tip":"Use simple, focused code snippets rather than large scripts. For complex tasks, break them into smaller testable components."},{"step":"Execute in Coderunner. Copy the filled prompt template into your AI tool (e.g., Claude) and run it. The code will execute in a secure sandboxed environment.","tip":"Check the execution time and resource usage reported by Coderunner. If it fails, review error messages carefully—they often indicate missing dependencies or syntax issues."},{"step":"Analyze the output. Review the results and identify areas for improvement based on your specific goal (performance, correctness, scalability, etc.).","tip":"Compare the output against expected results. For data processing tasks, verify sample records to ensure the logic is correct."},{"step":"Iterate and optimize. Use the analysis to refine your code, then re-run in Coderunner. Repeat until the code meets your requirements.","tip":"Save successful code snippets for reuse. For production use, consider adding unit tests or documentation."},{"step":"Deploy securely. Once validated, integrate the optimized code into your workflows, ensuring proper error handling and monitoring are in place.","tip":"For critical operations, run Coderunner in a CI/CD pipeline to automate testing of generated code before deployment."}]

Use Cases

Process local video files using AI-generated scripts without uploading them to the cloud.

Transform images with LLM-generated code while keeping the original files on your machine.

Analyze documents locally by executing code generated by AI models directly within the sandbox.

Run Python scripts automatically generated by LLMs to perform data analysis on your local datasets.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/instavm/coderunner

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use Coderunner to safely execute the following Python code in a secure sandbox: [INSERT_PYTHON_CODE]. After execution, analyze the output and suggest improvements for [SPECIFIC_GOAL, e.g., performance optimization, error handling, or feature addition]. Provide the final optimized code snippet.

Example Output

```python
# Sample code executed in Coderunner
import pandas as pd
from datetime import datetime

# Load a dataset of 50,000 customer records
df = pd.read_csv('customer_data.csv')

# Calculate average order value by region
avg_order = df.groupby('region')['order_value'].mean()

# Identify top 5 regions by AOV
print("Top 5 Regions by Average Order Value:")
print(avg_order.sort_values(ascending=False).head())
```

**Execution Results:**
Top 5 Regions by Average Order Value:
region
West Coast    1245.67
Northeast     1198.34
Midwest       987.23
Southeast     876.54
Pacific NW    765.43

**Analysis:**
1. The code successfully processed 50,000 records with no errors.
2. Performance: Execution took 1.2 seconds (acceptable for this dataset size).
3. Optimization Opportunities:
   - Add error handling for missing 'order_value' or 'region' columns
   - Consider parallel processing for datasets >100k records
   - Add logging for data quality checks (null values in critical columns)

**Optimized Code:**
```python
import pandas as pd
import logging
from concurrent.futures import ThreadPoolExecutor

logging.basicConfig(level=logging.INFO)

def safe_execute(df):
    try:
        if df['order_value'].isnull().any() or df['region'].isnull().any():
            logging.warning("Null values detected in critical columns")
        return df.groupby('region')['order_value'].mean().sort_values(ascending=False).head()
    except Exception as e:
        logging.error(f"Error processing data: {str(e)}")
        return None

# Load data with chunks for large datasets
chunks = pd.read_csv('customer_data.csv', chunksize=10000)
results = []

with ThreadPoolExecutor(max_workers=4) as executor:
    results = list(executor.map(safe_execute, chunks))

final_result = pd.concat(results).groupby(level=0).mean()
print("Optimized Results:")
print(final_result)
```

**Key Improvements:**
- Added error handling and logging
- Implemented parallel processing for scalability
- Maintained the same output format while being more robust