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# AI DevKit
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**The standard for AI-assisted software development.**
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**The toolkit for AI-assisted software development.**
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AI DevKit is an open-source ecosystem designed to bridge the gap between human intent and AI execution. It provides a suite of tools that standardize how AI agents interact with codebases, manage context, and execute development workflows.
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AI DevKit helps AI coding agents work more effectively with your codebase. It provides structured workflows, persistent memory, and reusable skills — so agents follow the same engineering standards as senior developers.
The official command-line interface for **AI DevKit**.
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# AI DevKit CLI
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This tool orchestrates the setup and management of AI-assisted development environments, ensuring your project is ready for agents like Cursor, Claude Code, Antigravity, and more.
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The command-line interface for **AI DevKit** — set up and manage AI-assisted development environments in your project.
|[Codex CLI](https://github.com/openai/codex)| ✅ | Only Global |
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|[Amp](https://ampcode.com/)| 🚧 | Testing |
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Templates are designed to provide structure while remaining concise and AI-friendly.
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## Workflow Examples
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### Initial Project Setup
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1.**Initialize your project:**
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```bash
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ai-devkit init
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```
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2.**Start with requirements:**
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- Use slash command to start filling the requirement `/new-requerement`
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- Your AI assistant will help clarify and document requirements
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- Use slash command to review the requirement `/review-requirement`
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3.**Design your system:**
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- Review `docs/ai/design/` and feature-specific files
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- Use slash command to review the design `/review-design`
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- Your AI assistant will help clarify and document design
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4.**Plan your work:**
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- Review `docs/ai/planning/` and feature-specific plans
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- Clarify and document plan with AI assistant
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5.**Implement with guidance:**
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- When the plan is ready, your AI assistant will help you implement the code
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- You can start the execution of the plan with the command `/execute-plan`
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- AI assistant will help you to review the code and update the implementation notes
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- After executing each task, you can check the implementation and compare it with the design with AI support by using the command `/check-implementation`
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- Once the implementation is good, you can review the code with AI support by using the command `/code-review`
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6.**Test thoroughly:**
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- After implementation is done, you can start testing
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- Use `docs/ai/testing/` as your testing guide
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- Use slash command to start writing test `/writing-test`
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7.**Deploy confidently:**
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- Follow deployment procedures in `docs/ai/deployment/`
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8.**Monitor and iterate:**
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- Set up monitoring per `docs/ai/monitoring/`
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## Use Cases
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-**New Projects**: Scaffold complete development documentation
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