Build a bundle around your current AI problem.
Start with the outcome you need, add only the missing skills, and keep duplicate items out of checkout.
Free starters help you orient first. Paid skills move to checkout only once, even when bundles overlap.
Select a guided path
Choose a bundle for a connected route instead of deciding one by one.
Turn a vague app idea into clear scope, acceptance criteria, and architecture direction before build starts.
Helps when: The idea is promising, but scope, workflow, and acceptance criteria are still vague.
Pick a practical platform path, architecture boundary, and data model before implementation grows.
Helps when: The app direction is clearer, but stack, database, and architecture choices can still cause expensive rebuilds.
Review implementation, interface, code quality, and security risk before trusting an AI-built MVP.
Helps when: The MVP looks usable, but generated code, UI behavior, and security risk have not been checked together.
Package the app, check SRS compliance, and produce an owner-ready release report.
Helps when: The product may work locally, but packaging, compliance evidence, and owner handoff are not ready.
Keep a serious AI-agent project traceable across tasks, cleanup, final gates, and agent rules.
Helps when: Long AI-agent work can lose context, skip gates, or let cleanup and rule changes drift.
Choose individual skills
Start with free foundations if unsure. Add paid skills as you progress.
Early discovery
Clear brand direction and process map from day one.
Requirements
Less ambiguity, fewer prompt loops, better acceptance checks.
Architecture and data
Cleaner technical decisions and fewer late rebuilds.
Build and clean code
Lower rework risk and cleaner project structure.
UI/UX and security
Better visual trust and safer release decisions.
Package and release
Fewer release surprises, clearer owner reports, and stronger handoff evidence.
Governance loop
Traceable AI-agent work across the whole project lifecycle.
