AI Can Build Your App. It Can't Engineer It. (9 Skills)

The Problem: Why AI Projects Decay00:00:00

Building with AI often leads to a spiral where features become harder to add over time. Without a cohesive plan or record of decisions, the codebase suffers from disconnected logic, messy code, and features that break existing functionality, leading to a project that doesn't compound.

Planning and Architecting00:02:16

The 'Scope' skill forces a planning phase to clarify requirements before coding, similar to professional engineering practices. The 'Architect' skill then helps make design decisions—such as technology stacks and database choices—explicitly, ensuring the system is built with purpose rather than AI-guessed defaults.

Development Discipline and Context00:07:10

The 'Develop' skill enforces strict boundaries by refusing to build if a decision hasn't been properly made. Meanwhile, 'Audit' and 'Sync' skills manage context files, ensuring that the AI maintains an accurate understanding of the project's state without the need for repetitive prompting.

Handling Legacy Codebases00:11:32

The workflow can be applied to real-world, inherited projects. The 'Audit' skill reads existing legacy code to document how it works, allowing the development cycle to operate within existing constraints and patterns rather than constantly fighting or regenerating the codebase.

Verification and Debugging00:14:31

To prevent broken features, the workflow employs verification through the 'Check', 'Test', 'Review', and 'Document' skills. The 'Debug' skill ensures issues are addressed by identifying root causes through reproducible testing rather than simple pattern-matched trial-and-error.

Conclusion: Building with Judgment00:20:18

True engineering isn't just about using specific tools, but about applying human judgment. By planning properly, making conscious decisions, and maintaining state, developers can ensure their projects remain maintainable and scalable.