What problem does it solve? Students with a working AI app demo often struggle to improve UX systematically. This Skill reads the existing codebase, detects which of seven UI patterns the app matches, and produces three copy-paste prompts that layer Transparency, Control, and Recovery features onto the app without rewriting it. ## Core Features & Use Cases - UI Pattern Detection: Scans the repo for signals (chat components, upload handlers, queues, streaming) and classifies the app into one of seven UI patterns. - Three Tailored Prompts: Emits a Prompt T (Transparency), Prompt C (Control), and Prompt R (Recovery), each under 80 words, additive-only, and localized to the app's existing UI language. - Reasoning Included: Every prompt ships with a "Why" explanation so students build intuition for the T-C-R framework. - Use Case: After demoing a working RAG chatbot, a student asks how to improve UX. The skill detects the chat-plus-panel pattern and returns prompts to add a sources panel with confidence colors, a stop button with AbortController, and a retry-on-error flow. ## Quick Start Run /tcr-apply from the repository root of a working AI app and ask it to retrofit the T-C-R pattern onto the existing UI.