GitHub Development Standard

To prevent quality issues in AI-assisted development, enforce a structured engineering standard that guides issue understanding, task scoping, coding discipline, and release practices. This approach ensures consistent output quality across teams using AI models of varying capability.

A 4-layer validation system sequentially checks syntax, imports, behavior, and regression in software projects. It acts as a structured gating mechanism before shipping to ensure AI-generated code meets required behavioral and structural standards.

Implement a structured AI coding workflow by applying a 9-step process spanning from initial issue understanding to final review. Combine this with a 15-item acceptance checklist to gate releases and systematically grow engineering discipline.

Yes, you can integrate a lightweight pre-commit validation workflow using optional scripts. This executes automated checks before code is committed, enforcing the development standard early in the engineering cycle.

Yes, the specification requires frontmatter-defined metadata, specifically name and description, in SKILL.md. This metadata is mandatory to properly identify and validate the skill within the structured engineering workflow.