Speckit enforces a spec-first development workflow for AI coding agents like Claude Code and Codex, generating four structured artifacts—spec, decisions, plan, and tasks—before any code is written.
git clone https://github.com/kargarisaac/speckit-skill.gitSpeckit is a skill for AI coding agents (Claude Code, Codex, opencode, and others) that enforces a specification-first workflow before implementation begins. For each feature, it generates four structured artifacts: spec.md, decisions.md, plan.md, and tasks.md, all organized under a numbered feature folder in a specs/ directory. The decisions.md file captures architectural decision records (ADRs) to document the reasoning behind technical choices. Commands let you create all artifacts at once with 'speckit new', or update individual artifacts independently, with implementation only beginning after planning is complete. This approach is designed to reduce ambiguity, capture context, and help developers and AI agents build higher-quality software with a clear, auditable paper trail.
Symlink or copy the speckit/ folder into your AI tool's skills directory (e.g., ~/.claude/skills/speckit for Claude Code or ~/.codex/skills/speckit for Codex). Run 'Use speckit new <feature description>' to create all four artifacts at once. Update individual artifacts with commands like 'speckit spec', 'speckit plan', or 'speckit tasks', and trigger implementation with 'speckit implement <feature folder>'.
Scaffolding a complete spec, ADR, plan, and task list for a new feature before writing any code
Capturing architectural decisions and their rationale in a structured decisions.md file
Breaking down a feature into ordered, trackable tasks that an AI agent implements sequentially
Maintaining a specs/ directory of versioned feature documentation across a project
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/kargarisaac/speckit-skillCopy 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.
Act as a developer using [PROGRAMMING_LANGUAGE] to implement a feature based on the following specification: [SPECIFICATION]. Generate clean, well-commented code that follows best practices. Include unit tests and ensure the code is production-ready. If there are any ambiguities or missing details in the specification, ask clarifying questions before proceeding.
# Feature Implementation: User Authentication System
## Overview
This implementation provides a secure user authentication system using Python and Django. The system includes user registration, login, logout, and password reset functionalities.
## Code Implementation
```python
# models.py
from django.contrib.auth.models import AbstractUser
from django.db import models
class User(AbstractUser):
bio = models.TextField(max_length=500, blank=True)
location = models.CharField(max_length=30, blank=True)
birth_date = models.DateField(null=True, blank=True)
def __str__(self):
return self.username
```
## Unit Tests
```python
# tests.py
from django.test import TestCase
from django.urls import reverse
from django.contrib.auth import get_user_model
class UserAuthTests(TestCase):
def setUp(self):
self.user = get_user_model().objects.create_user(
username='testuser',
email='test@example.com',
password='testpass123'
)
def test_user_creation(self):
self.assertEqual(self.user.username, 'testuser')
self.assertEqual(self.user.email, 'test@example.com')
self.assertTrue(self.user.check_password('testpass123'))
```
## Next Steps
1. Review the code and tests for any potential improvements.
2. Integrate the authentication system with the frontend.
3. Deploy the system to a staging environment for further testing.Automated meeting notes and action tracking
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