把模糊想法通过7轮结构化对话，生成精确到色值/变量的机器可读设计规范

Product, design, and engineering teams today lose hours and introduce bugs because high-level, fuzzy ideas never translate cleanly into machine-readable design specifications; ambiguous color, spacing, and type decisions lead to repeated clarification cycles, inconsistent implementations, and costly rework across distributed teams. This pain is especially acute for cross-functional teams at mid-to-large companies and for agencies supporting multiple clients, where handoff scale multiplies the inefficiency and slows time-to-prototype and release. You could build a guided authoring tool that runs a structured, seven-round conversational workflow to turn a vague brief into a deterministic, machine-readable design spec—complete with exact color values (HEX/RGBA/HSL), named design tokens, spacing/type variables, and exportable artifacts (JSON, CSS variables, Figma tokens, Style Dictionary bundles, Storybook-ready theme files). The market conditions favor this now: an addressable market of roughly $10.0B (5,000,000 product & design teams × $2K ACV), strong momentum in generative-AI that reduces prototype time, and growing adoption of tokenized design systems that make machine-readable outputs immediately consumable. To stand out you’d focus on repeatable structure and verifiable outputs—multi-turn disambiguation, built-in validation tests (contrast, accessibility, token collisions), and one-click connectors to common toolchains—plus enterprise controls for versioning and governance. Strengths include clear ROI and tight technical integration, while challenges are real: natural language ambiguity, fragmented tool ecosystems, data/privacy for training, and convincing teams to change established handoff habits in the face of medium-level competition.