One-click install
npx skills add https://github.com/gaoqiongxie/skills-ai --skill ai-dlc
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ai-dlc
Source: https://github.com/gaoqiongxie/skills-ai/tree/main/ai-dlc
Command: npx skills add https://github.com/gaoqiongxie/skills-ai --skill ai-dlc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Unguided AI-assisted software development often leads to inconsistent output quality, lack of decision traceability, and repeated avoidable mistakes, with no standardized process to ensure reliable, maintainable results.

Core Features & Use Cases

  • 4-stage closed-loop workflow: Covers full development cycles from requirement elaboration (Elaboration) to post-launch reflection (Reflection) for end-to-end governance.
  • Hat-based role switching: Assigns clear, specialized roles to AI at each stage (Planner, Builder, Reviewer, etc.) to avoid context confusion and improve output quality.
  • Backpressure quality gates: Blocks unqualified work from moving to the next stage via mandatory checks for test coverage, type safety, security, and code review.
  • DAG dependency orchestration: Manages dependencies between development tasks to enable parallel work and avoid circular dependencies for complex projects.
  • Use case: Engineering teams can use this skill to develop new features, fix production bugs, or refactor legacy systems with consistent quality and full audit trails of all development decisions.

Quick Start

Use the ai-dlc skill to develop a new user points system following the structured 4-stage lifecycle workflow.

Frequently Asked Questions about ai-dlc

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I standardize AI-assisted software development to ensure consistent quality?

You can standardize AI-assisted software development by adopting a 4-stage lifecycle workflow that enforces quality gates and role-based AI collaboration for full-cycle governance. This framework covers requirement elaboration to post-launch reflection.

What is the best way to manage task dependencies in AI-generated code projects?

The best way to manage task dependencies in AI-generated code is using DAG dependency orchestration, which enables parallel work and prevents circular dependencies. This approach manages dependencies between development tasks for complex projects.

How does role-based AI collaboration improve software engineering outcomes?

Role-based AI collaboration improves software engineering outcomes by assigning specialized roles like Planner or Builder to the AI at each stage. This hat-based role switching prevents context confusion and enhances output quality.

Can I enforce quality gates for test coverage and security during AI development?

Yes, you can enforce quality gates for test coverage and security during AI development by using backpressure mechanisms. These mandatory checks block unqualified work from moving to the next stage of the lifecycle.

Does the 4-stage development lifecycle support production bug fixes and system refactoring?

Yes, the 4-stage development lifecycle supports production bug fixes and system refactoring, alongside new feature development. It provides consistent quality and full audit trails of all development decisions for these scenarios.

Why does ungoverned AI-assisted development lead to maintainability issues?

Ungoverned AI-assisted development leads to maintainability issues because it lacks standardized processes, resulting in inconsistent output quality and no decision traceability. This causes repeated avoidable mistakes without reliable results.