Spec Kit - GitHub's Toolkit for Spec-Driven AI Development

GitHub recently open-sourced Spec Kit, a toolkit that brings structure and methodology to AI-assisted development. If you’ve been experimenting with AI coding tools but found the results inconsistent, Spec Kit offers a systematic approach worth exploring.

What is Spec Kit?

Spec Kit is an open-source toolkit that implements spec-driven development for AI coding assistants. Instead of jumping straight into code generation with ad-hoc prompts, it guides you through a structured workflow: define specifications first, then let AI generate implementations that match those specs.

The core idea: specifications become executable artifacts that directly drive code generation, not just documentation that gets ignored.

Why Spec-Driven Development?

Traditional AI coding often looks like this:

1. Write a prompt
2. Get code
3. Fix what’s wrong
4. Repeat until it works

This works for small tasks but breaks down for complex features. You end up with inconsistent code, missed requirements, and lots of back-and-forth.

Spec Kit flips this approach:

1. Define what you want (specification)
2. Plan how to build it (architecture)
3. Break it into tasks (execution plan)
4. Generate code that follows the spec

The result is more predictable, consistent output that aligns with your actual requirements.

The Spec Kit Workflow

Spec Kit organizes development into seven sequential steps:

1. Establish Principles

Create a constitution—foundational guidelines for consistent decision-making across your project.

/speckit.constitution

This defines coding standards, architectural patterns, and constraints that apply to all generated code.

2. Create Specifications

Document what you’re building without diving into technical details.

/speckit.specify

Focus on requirements, user stories, and acceptance criteria. What should the feature do? What are the edge cases?

3. Clarify Requirements

Address ambiguities before planning begins.

/speckit.clarify

This step surfaces unclear requirements and resolves them upfront, preventing confusion during implementation.

4. Generate Plans

Define the technical architecture and implementation approach.

/speckit.plan

The AI creates a technical plan based on your specifications, considering your project’s patterns and constraints.

5. Validate Plans

Audit for completeness and potential issues.

/speckit.analyze

Cross-reference specifications against plans to catch gaps, inconsistencies, or oversights.

6. Break Into Tasks

Create ordered, dependency-aware task lists.

/speckit.tasks

Transform the plan into actionable tasks that can be executed sequentially.

7. Execute Implementation

Build according to specifications.

/speckit.implement

Generate code that follows your specs, plans, and established principles.

Getting Started

Prerequisites

• Python 3.11+
• Git
• uv package manager
• A compatible AI coding assistant

Installation

Install the Spec Kit CLI:

uv tool install spec-kit

Or run it directly without installation:

uvx specify init my-project

Initialize a Project

specify init my-project

This creates the necessary configuration files and sets up your project for spec-driven development.

You can specify which AI assistant you’re using:

specify init my-project --agent claude-code

Supported AI Assistants

Spec Kit works with 18+ AI coding tools:

• Claude Code - Anthropic’s CLI tool
• GitHub Copilot - In VS Code and other IDEs
• Cursor - AI-first code editor
• Gemini - Google’s AI assistant
• Windsurf - Codeium’s AI IDE
• Aider - Terminal-based AI coding
• And many more

This flexibility means you can adopt Spec Kit regardless of which AI tool you prefer.

Practical Example

Let’s walk through building a user authentication feature with Spec Kit.

Step 1: Define the Constitution

# Project Constitution
 
## Architecture
- Follow clean architecture with separate domain, application, and infrastructure layers
- Use dependency injection for all services
- Keep business logic in the domain layer
 
## Code Standards
- TypeScript strict mode enabled
- All public APIs must have JSDoc comments
- Error handling uses Result types, not exceptions
 
## Security
- Never store plain-text passwords
- All endpoints require authentication unless explicitly public
- Input validation at API boundaries

Step 2: Write the Specification

# User Authentication Specification
 
## Overview
Users can register, login, and manage their sessions.
 
## Requirements
 
### Registration
- Users register with email and password
- Email must be unique and valid format
- Password must be at least 12 characters
- Send verification email on registration
 
### Login
- Users login with email and password
- Return JWT token on successful login
- Lock account after 5 failed attempts
 
### Session Management
- Tokens expire after 24 hours
- Users can logout (invalidate token)
- Users can logout from all devices
 
## Acceptance Criteria
- [ ] Registration creates unverified user
- [ ] Login fails for unverified users
- [ ] Account lockout works correctly
- [ ] Token refresh extends session

Step 3: Generate and Execute

With specifications in place, run through the Spec Kit commands to generate plans, tasks, and finally implementation—all aligned with your documented requirements.

Benefits of This Approach

Consistency

Every feature follows the same process. New team members can understand how decisions were made by reading the specs.

Traceability

You can trace any piece of code back to its specification. Why does this function exist? Check the spec.

Better AI Output

AI assistants perform better with clear, structured context. Specs provide that context systematically.

Reduced Iteration

Clarifying requirements upfront means less back-and-forth during implementation. The AI gets it closer to right the first time.

Documentation as a Byproduct

Your specifications become living documentation. They explain what the system does and why.

When to Use Spec Kit

Spec Kit adds value when:

• Building features with multiple components
• Working on a team where consistency matters
• Requirements are complex or have many edge cases
• You want documentation that stays in sync with code
• You’re using AI assistants for significant code generation

It may be overkill for:

• Quick fixes or small changes
• Exploratory coding or prototypes
• Solo projects with simple requirements

Integrating with Your Workflow

Spec Kit doesn’t replace your existing tools—it layers on top. You still use:

• Your preferred IDE
• Your AI coding assistant
• Your version control workflow
• Your CI/CD pipeline

Spec Kit adds structure to the AI interaction part of your workflow.

Conclusion

Spec Kit represents a maturing of AI-assisted development. Instead of treating AI as a magic code generator, it positions AI as a tool that executes well-defined specifications.

The investment in writing specs pays off through more consistent, predictable output. You spend less time fixing AI mistakes and more time building features.

If you’ve been frustrated with inconsistent AI code generation, give Spec Kit a try. The structured approach might be exactly what you need.

More info: github.com/github/spec-kit