What is the AI-DLC methodology?

AI-DLC (AI-Driven Development Lifecycle) is an AI-native approach where the AI proposes plans and handles decomposition while humans validate and approve, reversing the traditional human-initiated workflow.

Does this skill work with Jira and Confluence?

Yes, the AI-DLC skills are designed to create documentation in Confluence first for team review and then transfer verified tasks and bolts into Jira for execution.

What is the difference between a Bolt and a Sprint?

A Bolt is a much shorter iteration cycle lasting only hours or days, focusing on a testable increment, whereas a Sprint is a traditional 2-4 week capacity-based window.

How do I start a new project using this skill?

Start by using the /aidlc-intent command to gather requirements and create an Intent document in Confluence, which serves as the foundation for the entire development lifecycle.

AI-DLC Methodology & Help

Name: AI-DLC Methodology & Help
Author: onesmartguy

byonesmartguy

0•

Learning & Documentation

Guides developers through the AI-Driven Development Lifecycle (AI-DLC) methodology and plugin ecosystem for AI-native software engineering.

The AI-DLC Help skill provides comprehensive documentation and guidance for the AI-Driven Development Lifecycle, an AWS-originated methodology that puts AI at the center of the software development process. It helps users understand core concepts like Intents, Units, and Bolts, while providing step-by-step instructions for navigating the automated workflow between Confluence and Jira. Whether you are starting a new initiative or troubleshooting specific plugin commands, this skill ensures you maintain alignment with AI-native best practices and high-value decision-making throughout the design and implementation phases.

Key Features

01Clarifies terminology differences between traditional and AI-native workflows

02Outlines the sequential transition from Intent documentation to Jira execution

03Offers troubleshooting for Confluence and Jira integration prerequisites

04Provides detailed usage guides for the full suite of /aidlc commands

05Explains AI-DLC core principles and AI-native architectural patterns

060 GitHub stars

Use Cases

01Understanding the specific purpose and order of operations for planning skills

02Onboarding developers to the AI-Driven Development Lifecycle methodology

03Mapping traditional project artifacts like Epics and Sprints to AI-native Units and Bolts

Key Features

01Clarifies terminology differences between traditional and AI-native workflows

02Outlines the sequential transition from Intent documentation to Jira execution

03Offers troubleshooting for Confluence and Jira integration prerequisites

04Provides detailed usage guides for the full suite of /aidlc commands

05Explains AI-DLC core principles and AI-native architectural patterns

060 GitHub stars

Use Cases

01Understanding the specific purpose and order of operations for planning skills

02Onboarding developers to the AI-Driven Development Lifecycle methodology

03Mapping traditional project artifacts like Epics and Sprints to AI-native Units and Bolts