Emdash is an open-source agentic development environment that enables running multiple coding agents in parallel. It benefits developers and operations teams by automating coding tasks, integrating with providers like Claude, and connecting to workflows such as Git, Jira, and Linear. Emdash supports containerization with Docker and Git worktrees, allowing for efficient parallel execution of agents.
git clone https://github.com/generalaction/emdash.gitEmdash is an innovative Open-Source Agentic Development Environment designed to empower developers by allowing them to run multiple coding agents in parallel. This skill integrates seamlessly with various providers, making it versatile for different coding environments. With an intermediate implementation difficulty, users can set up Emdash in approximately 30 minutes, allowing teams to quickly leverage its capabilities for enhanced productivity. The key benefits of Emdash include the ability to automate the development of multiple features simultaneously, which significantly accelerates project timelines. By utilizing different coding agents, teams can manage dependencies and environment setups in isolated worktrees, streamlining the entire development process. Although specific time savings are not quantified, the efficiency gained from parallel execution and automated task assignments to coding agents can lead to substantial reductions in overall development time. Emdash is particularly beneficial for developers, product managers, and AI practitioners who are looking to optimize their workflow automation. It supports various use cases such as integrating with project management tools for automatic task assignments and reviewing code changes side-by-side with integrated diff tools for improved collaboration. Additionally, it enhances the testing process by enabling parallel test runs across multiple coding agents, which is crucial for maintaining high-quality code in fast-paced development environments. With its intermediate complexity, Emdash fits well into AI-first workflows, allowing teams to harness automation effectively. As organizations increasingly adopt AI automation, skills like Emdash become essential for maximizing productivity and ensuring that development processes are both efficient and scalable. By adopting this skill, teams can focus on innovation and problem-solving, rather than getting bogged down by manual coding tasks.
Automate the development of multiple features simultaneously using different coding agents.
Integrate with project management tools to automatically assign tasks to coding agents.
Review code changes side-by-side with integrated diff tools for better collaboration.
Manage dependencies and environment setups for various coding agents in isolated worktrees.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/generalaction/emdashCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Set up a coding environment using Emdash for [PROJECT_NAME]. Specify the programming languages to be used: [LANGUAGES]. Ensure that multiple coding agents are running in parallel to handle tasks such as [TASKS]. Provide a brief overview of how to manage these agents effectively.
For the project 'WeatherApp', I configured the Emdash environment to utilize Python and JavaScript. I set up three coding agents: one for backend development, another for frontend design, and the last for API integration. The Python agent is responsible for fetching weather data from external APIs, while the JavaScript agent handles the user interface and interactivity. Each agent runs in parallel, allowing for efficient task management. I utilized Emdash's built-in monitoring tools to track the progress of each agent and made adjustments as necessary. By the end of the week, the backend was able to fetch and process data accurately, the frontend was visually appealing, and the API integration was seamless, resulting in a fully functional prototype of the WeatherApp.
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