Structured specs for AI collaboration
Fission-AI/OpenSpec uses plain Markdown files in a `specs/` folder to define requirements with concrete scenarios. This approach allows developers to create detailed plans that AI coding assistants can follow, ensuring both human and machine understand the same goals before any code is written. The specs serve as a shared reference point, making it easier to review and refine plans before implementation begins.
Streamlining team workflows
For teams, OpenSpec introduces the concept of 'Stores'—separate repositories that hold shared specifications. This setup enables cross-repo features to be planned and managed in one place, with requirements owned by specific teams and consumed by others. By maintaining a single source of truth, OpenSpec helps prevent miscommunication and ensures that all team members and AI tools are working from the same set of requirements.
Quick setup for AI integration
Getting started with OpenSpec is straightforward. Developers can install the CLI using package managers like npm or Homebrew, then initialize the project with a simple command. Once set up, AI coding assistants can use slash commands like `/opsx:propose` to generate proposals based on the defined specs. This integration allows for a smooth workflow where AI tools can contribute to the development process from the very beginning.
Flexible and extensible framework
OpenSpec is designed to work with a variety of AI coding assistants, supporting over 30 tools. It allows developers to customize workflows through profiles and project configurations, making it adaptable to different team structures and project needs. The framework also supports community schemas, enabling third-party extensions that integrate OpenSpec with other development tools.
Ensuring clarity and predictability
By requiring specifications to be created before code, OpenSpec helps avoid the pitfalls of vague prompts and unpredictable AI outputs. This structured approach ensures that both developers and AI assistants have a clear understanding of what needs to be built, leading to more reliable and consistent results. The framework also encourages good context management, which is crucial for maintaining effective AI collaboration.
Start by setting up OpenSpec in your project and defining your first spec to establish a clear plan for your AI coding assistant.