Traditional software development revolves around code.
AI-assisted development often changes this to:
However, both approaches share the same problems:
- Knowledge remains trapped inside individual projects.
- Each project repeatedly rediscovers:
- Component patterns
- Architectural decisions
- Naming conventions
- State management approaches
- Testing strategies Skill-Driven Development introduces a new layer:
Requirements
↓
Skills
↓
AI
↓
Code
Skills become reusable engineering knowledge.
Mini Exercise
Pick one repeated front-end task from your team, such as building forms or API services.
Describe how that task would move from:
- project knowledge
- to reusable skill knowledge
Example
A team may repeatedly rebuild form validation rules in every project.
In Skill-Driven Development, that becomes a reusable form-validation-skill.md that defines:
- validation library
- naming rules
- error handling
- testing expectations