
利用AI创建标志、品牌识别和视觉资产的Claude Code技能——工具选择、提示工程、矢量化流程、模型、自动化和知识产权安全。
概览
一项Claude代码技能,用于利用AI创建标志、品牌识别和视觉资产——基于真实制作经验而非理论。 我是个。多年来,我生活在两个世界——用Illustrator手工打造品牌形象,同时研究机器如何学会看见。当生成式人工智能进入设计行业时,我看到了同样的问题:设计师要么完全拒绝人工智能,要么放弃了创意控制权,产生了现在行业所称的“AI烂片”——无一无特色、缺乏品牌连贯性的通用视觉效果。 这项技能源于我对让品牌令人印象深刻的需求。我需要一个系统,能让我产出10倍的产出,同时保持贝济尔曲线的数学精度、色彩理论的意图性以及品牌原型的战略深度。
概述(AI 翻译)
一项利用AI创建标志、品牌识别和视觉资产的Claude代码技能——基于真实制作经验而非理论构建。
为什么会有这样的存在
我是一名拥有计算机科学硕士学位的平面设计师。多年来,我生活在两个世界之间——用Illustrator手工打造品牌形象,同时研究机器如何学会看见。当生成式人工智能进入设计行业时,我看到了无处不在的问题:设计师要么完全拒绝人工智能,要么放弃了创意控制权,产生了现在行业所称的“AI烂片”——毫无特色、没有品牌连贯性的通用视觉。
两种极端都行不通。
这项技能源于我需要**在不失去品牌本质和创意控制的前提下,扩大视觉识别生产规模的需求。我需要一个系统,既能让我产出10倍的产出,又能保持贝塞尔曲线的数学精确性、色彩理论的有意图性以及品牌原型的战略深度。
本页由 AI 翻译,技术细节请以原文为准。
README
以下为原文 README 节选,完整内容请查看原文。
AI Graphic Design — Claude Code Skill
A Claude Code skill for creating logos, brand identities, and visual assets using AI — built from real-world production experience, not theory.
Why This Exists
I’m a graphic designer with a Master’s degree in Computer Science. For years I lived in both worlds — crafting brand identities by hand in Illustrator and studying how machines learn to see. When generative AI hit the design industry, I saw the same problem everywhere: designers either rejected AI entirely or surrendered creative control to it, producing what the industry now calls “AI slop” — generic, soulless visuals with no brand coherence.
Neither extreme works.
This skill was born from my need to scale visual identity production without losing the essence and creative control that makes a brand memorable. I needed a system that would let me produce 10x the output while maintaining the mathematical precision of Bezier curves, the intentionality of color theory, and the strategic depth of brand archetypes.
After curating and distilling 46 specialized sources on AI-assisted graphic design — covering tools, prompt engineering, vectorization pipelines, IP law, and Python automation — I compiled everything into a single, actionable skill that any Claude Code user can install and use immediately.
What It Covers
| Section | Content |
|---|---|
| Tool Selection Matrix | 15 AI tools mapped to specific scenarios (Recraft for SVG, Midjourney for ideation, Firefly for IP-safe work, etc.) |
| Briefing Frameworks | RCAO and StoryBrand templates for defining brand identity before any visual generation |
| Prompt Engineering | Structured formulas per tool — Recraft (shape logic + constraints), Midjourney (artistic vocabulary + --sref/–cref/–seed), DALL-E (natural language), Stable Diffusion (LoRAs + ControlNet) |
| End-to-End Workflow | 5 phases: Briefing → Ideation → Vector Creation → Mockups → Delivery |
| Visual Consistency | Style IDs, seed fixing, moodboards, LoRAs, manual curation techniques |
| Vectorization Pipeline | Raster → Smart Upscaling (4 models) → Vectorizer.ai/Chat2SVG → Bezier cleanup (Box Method) |
| Python Automation | py5, vpype, Aspose.SVG, Blender API for batch generation and post-processing |
| Delivery Formats | SVG/EPS/PDF (vector) vs PNG/JPG (raster) with DPI specs for print and digital |
| IP Safety & Ethics | Risk matrix by tool (Firefly vs Midjourney vs Stable Diffusion) + legal rules (BR/US/UK) |
| Professional Mockups | Displacement Maps + Blend Options in Photoshop; AI alternatives (Phygital+, Nano Banana) |
| Anti-patterns | 7 common mistakes designers make with AI and how to avoid them |
| 2025-2026 Competency Roadmap | The skills a designer needs to evolve from executor to “AI Creative Director” |
The Philosophy
“AI tool interfaces become obsolete in months. Fundamentals last decades.”
推荐工具
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安装
npx skillfish add designrique/ai-graphic-design-skill