Introduction
If you have ever struggled with messy chat logs, lost context, and broken code when working with AI coding tools, this guide will help you solve these problems with OpenSpec, a lightweight specification-driven development framework. We will explain how OpenSpec works, how it compares to other popular frameworks, and how you can use it to build stable, maintainable projects with AI.
What is Specification-Driven Development?
Before we dive into OpenSpec, let's understand the core concept: Specification-Driven Development (SDD). This approach ensures that you define clear requirements, designs, and acceptance criteria before writing any code. It solves the common problem of AI forgetting context or breaking existing code when you make changes.
There are two main methodologies: SDD (define requirements first, then build) and TDD (write tests first, then build). OpenSpec is a flexible SDD framework that forces AI to break down your requirements into three key documents:
- Proposal: Why build this?
- Design: How to build this?
- Spec: What to build (with acceptance criteria).
The OpenSpec Workflow: 4 Simple Steps
Step 1: Explore Requirements
Start by describing your idea to AI:
Help me build an AI chatbot.AI will ask follow-up questions: "What frontend framework should we use? Which model should we integrate? What is the project structure?" Answer these to refine your requirements.
Step 2: Generate Documentation
Once requirements are clear, AI generates three documents: Proposal, Design, and Spec (with detailed acceptance criteria).
Step 3: Develop
AI works through tasks in the Spec document, checking off each item as completed. You can track progress in real time.
Step 4: Archive
After development, all documents are archived and changes are merged into a central specification table, ensuring anyone can understand the project's history and current state.
OpenSpec vs. Other SDD Frameworks
| Feature | OpenSpec | Spec-Kit | Superpowers |
|---|---|---|---|
| Focus | Lightweight spec management | Full SDD lifecycle | Agent behavior constraints |
| Use Case | Small to medium projects | Large, complex projects | Ensuring AI follows rules |
| Complexity | Easy to learn | Steeper learning curve | Moderate |
- OpenSpec: Best for beginners or small teams — fast setup, flexible.
- Spec-Kit: Best for large teams or enterprise — enforces complete SDD lifecycle.
- Superpowers: Best for ensuring AI follows rules and constraints during development.
How to Use OpenSpec
Installation
npm install -g openspecBasic Commands
# Initialize a new project
openspec init
# Explore requirements
openspec explore
# Generate documentation
openspec generate
# Develop based on specs
openspec develop
# Archive changes
openspec archiveExample: Building an AI Learning Assistant
- Explore: "Help me build an AI learning assistant with a dashboard, study plan CRUD, and user authentication."
- Generate Docs: AI creates Proposal ("Build an AI learning assistant to track study progress"), Design ("React frontend, Node.js backend, PostgreSQL"), and Spec ("Dashboard should display completed tasks, study plans, and progress charts").
- Develop: AI implements features, checking off each task.
- Archive: Documents archived, changes merged.
Why OpenSpec is Perfect for Beginners
- Low Barrier to Entry: No advanced technical skills needed.
- Clear Workflow: The four-step process ensures you never get lost.
- Maintainable Projects: All changes documented, easy to update or hand off.
- Flexible: Customize the workflow to fit your needs.
常见问题
How is SDD different from just writing good prompts?
Good prompts produce good single responses. SDD produces good projects. The difference is persistence: a well-crafted prompt might generate great code once, but when you come back two weeks later to add a feature, neither you nor the AI remembers the context. SDD solves this by externalizing context into documents (Proposal, Design, Spec) that persist across sessions. When you return to a project, the AI reads the archived specs and understands exactly what was built, why, and what the acceptance criteria were. This is the difference between AI-assisted coding (which works for one-off tasks) and AI-driven development (which works for ongoing projects).
Do I need to use OpenSpec for every project?
No. OpenSpec shines for projects that will be worked on over multiple sessions, involve multiple features, or might be handed off to someone else. For one-off scripts or quick experiments, the overhead isn't worth it — just use your AI coding tool directly. A good rule of thumb: if the project will take more than one day of work, use OpenSpec. If it's a single-session task, skip it. The framework is lightweight enough that the setup cost is low (5-10 minutes to generate initial docs), but it's not zero — don't add process for process's sake.
Can I use OpenSpec with any AI coding tool?
Yes. OpenSpec is tool-agnostic — it's an npm package that generates and manages specification documents. It works with Claude Code, Codex, Cursor, or any AI coding assistant that can read files from your project. The workflow is the same: run OpenSpec commands to generate docs, then ask your AI tool to read the Proposal/Design/Spec files and implement accordingly. Some tools have built-in OpenSpec integration for smoother workflows, but even without native support, the file-based approach works universally.
What happens if I change requirements mid-project?
That's exactly what the archive and spec system is designed for. When requirements change, you run openspec explore again with the new requirements. OpenSpec generates updated Proposal/Design/Spec documents that reference the archived versions, showing what changed and why. The AI then implements only the delta — it doesn't rebuild from scratch. This is the key advantage over ad-hoc AI coding: requirement changes are documented, traceable, and implemented surgically rather than through "let's start over" prompting. The archive ensures you never lose the context of why original decisions were made.