ai-dlc

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.