opencoder

🥈Silver

OpenCoder is an open-source alternative to Claude Code, offering similar UI and UX. It's built on the Vercel AI SDK and supports Claude agents. Use it for coding, debugging, and automation tasks in operations.

374390Updated Today

Intermediate30min to implementautomation

Saves ~45 min per use

Quick InstallView Source

git clone https://github.com/ducan-ne/opencoder.git

Works with:

Claude

Overview

About This Skill

opencoder is an innovative automation skill designed for Claude Code users seeking an alternative solution for their automation needs. This skill focuses on simplifying workflow automation processes, making it easier for developers and AI practitioners to implement and manage their automated tasks. With an intermediate level of complexity, opencoder can be set up in just 30 minutes, allowing users to quickly integrate it into their existing workflows. The key benefits of using opencoder include enhanced efficiency in automating repetitive tasks and the ability to streamline processes that would otherwise consume valuable time. While specific time savings are currently unknown, the skill's design inherently promotes faster execution of automation tasks, which can lead to significant productivity gains over time. By reducing the manual effort required for various automation processes, users can focus on more strategic initiatives that drive value for their teams. opencoder is particularly suited for developers, product managers, and AI practitioners who are looking to optimize their workflow automation capabilities. Its medium go-to-market relevance suggests that it is well-positioned to address common challenges faced by professionals in tech-driven environments. Practical use cases for opencoder include automating data entry tasks, managing API integrations, and facilitating continuous deployment processes, all of which can significantly enhance operational efficiency. With an implementation difficulty rated as intermediate, users should have a foundational understanding of automation principles and coding practices to fully leverage opencoder's capabilities. This skill fits seamlessly into AI-first workflows, enabling teams to harness the power of AI automation to achieve their goals. By integrating opencoder into their operations, organizations can take a significant step towards modernizing their automation strategies and improving overall productivity.

Use Cases

Automate file reading and writing tasks in your development environment.

Integrate web scraping capabilities using Playwright for data extraction.

Utilize AI models for planning and generating code snippets.

Implement memory management features to enhance application performance.

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/ducan-ne/opencoder

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

I need to automate a repetitive task in my workflow. Here's the task: [TASK]. Can you generate a Python script using OpenCoder to automate this? The script should be efficient and handle [SPECIFIC_CONDITIONS] if applicable.

Example Output

# OpenCoder Automation Script

```python
import os
import pandas as pd

# Function to process and categorize data
def process_data(input_file, output_dir):
    # Read input data
    data = pd.read_csv(input_file)
    
    # Create output directory if it doesn't exist
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
    
    # Process data and save to categorized files
    for category in data['Category'].unique():
        category_data = data[data['Category'] == category]
        output_file = os.path.join(output_dir, f'{category}_data.csv')
        category_data.to_csv(output_file, index=False)
        print(f'Saved {len(category_data)} records to {output_file}')

# Main execution
if __name__ == '__main__':
    input_file = 'input_data.csv'
    output_dir = 'processed_data'
    process_data(input_file, output_dir)
```

## How to Use This Script
1. Save this script as `data_processor.py`
2. Prepare your input CSV file named `input_data.csv`
3. Run the script using Python 3.x
4. Check the `processed_data` directory for categorized output files