Every developer using Cursor, Claude Code, Windsurf, or GitHub Copilot knows this exact frustration:
You are building a cutting-edge Angular 22 application. You ask your AI coding assistant to spin up a dynamic form, a lazy-loaded list, or an asynchronous data card. Instead of leveraging modern fine-grained reactive Signals, optimized native block control flows, or proper SSR hydration hooks, the AI drops an unoptimized pile of legacy tech debt full of NgModules, *ngIf, *ngFor, and raw RxJS BehaviorSubjects.
The LLM Training Paradox
Why does this happen? Large Language Models are trained on historical code datasets. Statistically, more than 90% of the public Angular repositories and StackOverflow threads on the internet represent older paradigms. Left to their own devices, agents default to the statistical average of their training data. They literally default to the past.
The Fix: angular22-agent-skills
