Mysti

🥈Silver

Mysti is an AI coding team of agents for VS Code. Claude Code and OpenAI Codex collaborate to brainstorm, debate, and synthesize the best coding solutions. It benefits developers by accelerating coding tasks, improving code quality, and integrating with VS Code workflows.

878400Updated 1w ago

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/DeepMyst/Mysti.git

Works with:

Claude

Overview

About This Skill

Mysti is an innovative AI automation skill designed to transform your coding experience within Visual Studio Code. By leveraging the collaborative capabilities of Claude Code and OpenAI Codex, Mysti enables AI agents to work together in brainstorm mode, debating and synthesizing the best coding solutions. This unique approach allows developers to tackle complex programming tasks more effectively, ensuring that they have access to a variety of perspectives when solving coding challenges. The key benefits of using Mysti include enhanced collaboration among AI agents, which leads to more robust code solutions. By utilizing the brainstorm mode, developers can compare different AI perspectives on a coding problem, allowing for a more thorough exploration of potential solutions. Additionally, the rapid response capabilities of Codex, combined with Claude's deep reasoning, enable quick iterations on code, significantly reducing the time spent on debugging and refining solutions. Although the exact time savings are currently unknown, the streamlined process is designed to save developers valuable time in their workflow. Mysti is particularly suited for developers, product managers, and AI practitioners who are looking to optimize their coding processes. It is ideal for those working on complex projects that require collaboration and innovative problem-solving. The skill allows users to select specialized developer personas, tailoring the AI's approach to specific coding challenges, thus maximizing the effectiveness of the automation. With a medium GTM relevance, it is a great addition to any team's toolkit, especially for those already utilizing AI tools. Implementing Mysti is straightforward, taking approximately 30 minutes to set up within your existing workflow. The skill does not require additional subscriptions, making it a cost-effective solution for teams looking to enhance their coding capabilities. As organizations increasingly adopt AI-first workflows, Mysti stands out as a valuable asset that integrates seamlessly with existing AI tools, ensuring that developers can leverage the full potential of AI automation in their projects.

Use Cases

Collaborate with multiple AI agents to generate code solutions for complex programming tasks.

Utilize Brainstorm Mode to compare different AI perspectives on a coding problem.

Quickly iterate on code by leveraging the rapid response capabilities of Codex alongside Claude's deep reasoning.

Select specialized developer personas to tailor the AI's approach to specific coding challenges.

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/DeepMyst/Mysti

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

Collaborate with the AI coding agents for VS Code to solve a coding challenge. Describe the problem you're facing with [PROGRAMMING_LANGUAGE] and any relevant context. Ask the agents to brainstorm solutions and provide a synthesized approach. For example: 'I need to optimize a function in [PROGRAMMING_LANGUAGE] that processes large datasets. The current implementation is too slow. Can you brainstorm and debate the best optimization strategies?'

Example Output

After analyzing your request, the AI coding agents brainstormed several optimization strategies for your function in Python that processes large datasets. They debated the merits of using list comprehensions versus traditional loops, ultimately suggesting that employing NumPy for vectorized operations would yield the best performance. The agents synthesized their findings into a cohesive solution: 'To optimize your function, replace the current list processing with NumPy arrays to leverage vectorization. This change can reduce processing time significantly, especially with datasets exceeding 1 million entries. Here's a sample code snippet:

```python
import numpy as np

def optimized_function(data):
    array_data = np.array(data)
    # Perform operations using NumPy for efficiency
    return np.sum(array_data)
```
Additionally, they recommended testing the performance improvement using the `timeit` module to benchmark the new implementation against the original one.'