Even With The Figma MCP, AI Eyeballs Your Design and Ships Pixel-Wrong UI A developer has created a reusable Claude Code skill, 'implementing-figma-designs', that transforms Figma-to-code conversion from a screenshot-based eyeballing loop into a staged extract-then-prove protocol. The skill ensures every hardcoded value traces to a Figma node and verifies the implementation in a real browser by comparing computed CSS styles to node values, eliminating pixel-wrong UI caused by LLM guesses. Every hardcoded value now traces to a Figma node, and "looks right" is no longer accepted as proof. We packaged the fix as a reusable Claude Code skill, implementing-figma-designs . It turned Figma-to-code from a "build it, then eyeball it against the PNG for three correction rounds" loop into a staged extract-then-prove protocol. An LLM handed a Figma frame will happily invent a 1px solid 91A3B3 border, size a 24px icon at 28px, and guess the icon→text gap — because a faint border and an off-by-4px value both look correct in a screenshot, so a screenshot-based "does it match?" check passes while the running DOM is wrong. get variable defs / get metadata / get design context per node, or be explicitly flagged as off-system. No value is read off the PNG. orient → classify nodes → confirm component mappings → triangulate code+tokens+geometry → implement . get design context emits as EE0F51 gets reconciled back to the project's design token; anything with no token is raised to the designer, not silently hardcoded.