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Agent Skill · 前端设计

color-palette(调色板)

color-palette 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

color-palette 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 4,894。

总安装 776周安装 33GitHub Stars 2下载 272核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:color-palette(调色板)
来源仓库:https://github.com/nhatmobile1/color-palette-skill
仓库路径:skills/color-palette
安装命令:
npx skills add https://github.com/nhatmobile1/color-palette-skill --skill color-palette
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/nhatmobile1/color-palette-skill --skill color-palette

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

用于处理 GitHub 仓库、Issue 和 Pull Request 协作信息。适用于围绕仓库状态、代码变更或协作事项进行整理。可帮助分析颜色配置和样式变更。color-palette 属于前端设计类 Skill,可作为该场景下的辅助能力补充。安装方式:通过 GitHub 仓库安装,使用前确认权限与维护状态。

注意是否会触发联网、命令执行或文件读写操作。

来源仓库内路径:skills/color-palette

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/nhatmobile1/color-palette-skill --skill color-…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

color-palette 怎么安装?

在终端执行 npx skills add https://github.com/nhatmobile1/color-palette-skill --skill color-palette。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

color-palette 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

color-palette 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

color-palette 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

color-palette 用的人多吗?

公开数据是累计安装 776 次,近一周安装 33 次,下载 272 次,GitHub 2 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

color-palette 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
color-palette

调色板 · 本页

7762只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Color Palette Creation

Overview

This skill helps create distinctive, accessible color palettes for UI and web design that stand out from generic AI-generated aesthetics. It provides color theory guidance, curated domain-specific examples, accessibility validation tools, and strategies to avoid overused patterns.

When to Use This Skill

Use this skill when:

  • Designing color palettes for websites, apps, or digital interfaces
  • Users request color schemes for specific domains or industries
  • Building design systems or style guides
  • Ensuring accessibility compliance (WCAG AA/AAA)
  • Breaking away from generic "AI-looking" designs
  • Need validation of existing color choices

Workflow

Step 1: Understand Context and Requirements

Before selecting or generating colors, gather essential context:

Domain/Industry Questions:

  • What industry or domain is this for? (Tech/SaaS, E-commerce, Healthcare, Finance, Creative, Food, etc.)
  • What emotions or associations should the palette convey?
  • Are there existing brand colors to work with or extend?

Technical Requirements:

  • Light mode, dark mode, or both?
  • Accessibility requirements? (WCAG AA minimum, AAA preferred)
  • How many colors needed? (Typically: 1-2 primary, 1-2 secondary, 1-2 accents, full neutral scale, semantic colors)

Distinctiveness Goals:

  • Should this avoid looking "AI-generated"?
  • Any specific colors or combinations to avoid?
  • Preference for warm vs cool tones?

Step 2: Select Base Approach

Choose one of these approaches based on the context:

Approach A: Domain-Specific Curated Palettes

When to use: Clear industry/domain, want proven effective combinations

Process:

  1. Read references/color-palette-ui-design-reference.md focusing on the relevant domain section
  2. Present 2-3 palette options from that domain
  3. Explain what makes each effective
  4. Adapt the chosen palette if needed

Example domains:

  • Tech/SaaS: Trust & stability, Modern minimal, Professional dashboards, Dark mode
  • E-commerce: Fashion, Beauty, Sports, Home, Luxury
  • Healthcare: Calm professional, Soothing apps, Mental health/wellness
  • Finance: Traditional banking, Modern fintech, Investment platforms
  • Creative/Portfolio: Bold & modern, Minimalist, Artistic, Developer portfolios
  • Food/Restaurant: Warm & appetizing, Delivery apps, Organic, Upscale
Approach B: Generate from Brand Color

When to use: Starting from existing brand color(s), need to build complete system

Process:

  1. Use scripts/generate_palette.py to create color scales: python scripts/generate_palette.py #3B82F6 scale
  2. Generate harmonious colors using color theory: python scripts/generate_palette.py #3B82F6 complementary python scripts/generate_palette.py #3B82F6 triadic python scripts/generate_palette.py #3B82F6 analogous
  3. Validate contrast ratios (Step 3)
  4. Apply 60-30-10 rule (see reference document)
Approach C: Custom Palette from Inspiration

When to use: Need unique, distinctive palette; have specific inspiration sources

Process:

  1. If user provides inspiration (image, artwork, location), extract colors from that source
  2. Review "Common AI Design Pitfalls" section in references to avoid generic patterns
  3. Apply color harmony principles from reference document
  4. Use "Anti-AI Checklist" to ensure distinctiveness
  5. Validate accessibility (Step 3)

Step 3: Validate Accessibility

Always validate contrast ratios using the contrast checker script:

python scripts/check_contrast.py <foreground> <background> <text-size>

Examples:

# Check normal text on background
python scripts/check_contrast.py #1A1A1A #FFFFFF normal

# Check large text (headings)
python scripts/check_contrast.py #4A4A4A #FFFFFF large

# Check button contrast
python scripts/check_contrast.py #FFFFFF #3B82F6 normal

Requirements:

  • Normal text: 4.5:1 minimum (AA), 7:1 preferred (AAA)
  • Large text (18pt+): 3:1 minimum (AA), 4.5:1 preferred (AAA)
  • UI components (buttons, borders, icons): 3:1 minimum

If validation fails:

  • Darken the foreground color
  • Lighten the background color
  • Use the palette generator to create darker/lighter shades
  • Consider alternative color combinations

Step 4: Structure Complete Color System

Organize the palette following design system best practices:

Primary Colors
  • Main brand identity (1-2 colors)
  • Full scale (50-950 shades)
  • Use for: Logo, primary buttons, active states
Secondary Colors
  • Supporting brand colors (1-3 colors)
  • Full or partial scale depending on usage
  • Use for: Secondary buttons, icons, less prominent features
Accent Colors
  • Attention-drawing colors (1-2 colors)
  • Apply 60-30-10 rule: Use for only 10% of the design
  • Use for: CTAs, highlights, badges, notifications
Neutral Colors
  • Grays/neutrals for structure
  • Full scale (50-950) required
  • Use for: Text, backgrounds, borders, disabled states
Semantic/Functional Colors
  • Success: Green shades
  • Error: Red shades
  • Warning: Yellow/Orange shades
  • Info: Blue shades

Naming Convention:

Primitive tokens: blue-500, gray-100, red-700
Semantic tokens: background-surface-primary, text-body, button-primary-background

Step 5: Avoid AI Design Pitfalls

Before finalizing, review against the Anti-AI Checklist from the reference document:

Critical checks:

  • NOT purple/blue gradients on white (unless brand-appropriate)
  • NOT orange and teal combination
  • NOT high-saturation gradients everywhere
  • Includes context-specific colors (not generic)
  • Has real-world inspiration source
  • Reflects specific brand story or domain
  • Uses unique shades, not default values

If any red flags appear, revise using:

  • Nature-inspired gradients instead of purple-blue
  • Muted accents instead of neon on dark
  • Unexpected color combinations that still harmonize
  • Industry-specific references from the curated palettes

Step 6: Present the Palette

Format the final palette clearly:

Primary Color - [Name] (#HEX)
├─ 50:  #XXXXXX
├─ 100: #XXXXXX
├─ ...
└─ 950: #XXXXXX

Secondary Color - [Name] (#HEX)
[Similar structure]

Accent Color - [Name] (#HEX)
[Similar structure]

Neutral Colors - Gray (#HEX)
[Full scale]

Semantic Colors:
├─ Success: #XXXXXX (contrast: X.X:1)
├─ Error:   #XXXXXX (contrast: X.X:1)
├─ Warning: #XXXXXX (contrast: X.X:1)
└─ Info:    #XXXXXX (contrast: X.X:1)

Include:

  • Color names and hex values
  • Contrast ratios for key combinations
  • Usage guidance (what each color is for)
  • Why this palette is effective for the domain
  • How it avoids generic AI patterns

Quick Reference

Color Theory Harmonies

Complementary: Colors opposite on color wheel (high contrast, maximum tension) Analogous: Adjacent colors (harmonious, smooth, low contrast) Triadic: Three colors equally spaced (vibrant, balanced) Split-Complementary: Base + two adjacent to complement (high contrast, less tension)

For detailed explanations, see references/color-palette-ui-design-reference.md → Color Theory Fundamentals

60-30-10 Rule

60% - Dominant color (backgrounds, main surfaces) 30% - Secondary color (cards, secondary surfaces) 10% - Accent color (CTAs, highlights, focal points)

Common Pitfalls to Avoid

  1. Purple/blue gradients - Most overused AI pattern
  2. Orange and teal - Instantly recognizable as AI-generated
  3. High-saturation everywhere - No visual rest
  4. Generic semantic colors - Standard blue/green/red without uniqueness
  5. Template thinking - Not considering specific context

Resources

scripts/check_contrast.py - Validate WCAG contrast ratios scripts/generate_palette.py - Generate color scales and harmonies references/color-palette-ui-design-reference.md - Comprehensive guide including:

  • Color theory fundamentals
  • Color psychology
  • 60+ curated palette examples by domain
  • Design system approaches (Material Design, Tailwind, Ant Design)
  • Accessibility guidelines
  • Anti-AI design strategies

Examples

Example 1: Tech SaaS Dashboard

User request: "Create a professional color palette for a B2B SaaS analytics dashboard"

Process:

  1. Read Tech/SaaS section from references
  2. Choose "Professional SaaS Dashboard" palette as base
  3. Validate contrast ratios
  4. Extend with full scales using generator

Output:

Primary: Professional Blue (#0072BB)
├─ Use for: Primary actions, active states, links
└─ Contrast validated: 4.6:1 on white background ✓

Secondary: Dark Navy (#28324E)
├─ Use for: Headers, important text, secondary buttons
└─ Contrast validated: 12.4:1 on white background ✓

Success: Soft Green (#77D38C)
Warning: Amber (#F0AD4E)
Error: Muted Red (#C1666B)

Neutral: Blue-Gray scale (#B5BDD7 to #28324E)

Why this works:
- Calm blue conveys trust and professionalism
- Muted semantic colors don't overwhelm data visualizations
- Full blue-gray neutral scale maintains cohesive color temperature
- Avoids purple-gradient cliche while staying professional

Example 2: E-commerce Fashion Site

User request: "I need warm, earthy colors for a sustainable fashion e-commerce site"

Process:

  1. Read E-commerce → Fashion & Apparel from references
  2. Select earthy palette, modify for sustainability emphasis
  3. Validate all key combinations

Output:

Primary: Dark Teal (#264653)
├─ Evokes nature, sustainability, calm
└─ Use for: Navigation, primary buttons

Secondary: Living Teal (#2A9D8F)
├─ Fresh, natural, growth
└─ Use for: Highlights, hover states

Accent 1: Terracotta (#E76F51)
├─ Warm, earthy, organic
└─ Use for: CTAs, sale badges

Accent 2: Gold (#E9C46A)
└─ Use for: Premium products, special offers

Why this avoids AI-generic:
- Uses earthy tones instead of bright tech colors
- Warm accents on cool base (intentional temperature mixing)
- Terracotta is distinctive, not overused
- Reflects real sustainable fashion brand aesthetics

Example 3: Dark Mode App

User request: "Dark mode palette for a creative tools app, want to avoid the generic neon-on-dark look"

Process:

  1. Review "Dark Mode SaaS" and "Common AI Pitfalls" sections
  2. Choose muted accents instead of full neon
  3. Generate custom scales
  4. Validate all contrast ratios

Output:

Background: Very Dark Navy (#0A0E27)
Surface: Dark Blue-Gray (#1E2749)

Primary: Vibrant Blue (#4A9EFF)
├─ Bright enough for contrast, not neon
└─ Contrast: 8.2:1 on background ✓

Secondary: Lavender Indigo (#8B7FE8)
├─ Creative, sophisticated
└─ Contrast: 7.1:1 on background ✓

Text: Off-White (#E8EAF6)
└─ Contrast: 13.2:1 on background ✓

Why this works:
- Deep, rich backgrounds (not pure black)
- Accents are vibrant but not harsh neon
- Lavender adds creativity without purple-gradient cliche
- Excellent contrast without eye strain

Tips for Success

  1. Always start with context - Don't jump to colors before understanding domain, audience, and purpose
  2. Use references liberally - The curated palettes have been validated in real products
  3. Validate early and often - Check contrast ratios as you build, not at the end
  4. Be distinctive - Actively avoid the most common AI patterns
  5. Tell a story - Every color choice should relate to brand narrative or domain
  6. Consider the whole system - Think beyond just primary colors to complete scales and semantic colors
  7. Test in context - Colors look different on screens, in different lighting, next to different content

Advanced Usage

Creating Color Palettes from Images

If user provides an image for inspiration:

  1. Identify 2-3 dominant colors manually or describe the color scheme
  2. Extract hex values or approximate them
  3. Use those as base colors for palette generation
  4. Apply color theory to create harmonious extensions
  5. Validate and refine for accessibility

Multi-Brand Color Systems

For products with multiple brands or themes:

  1. Establish shared neutral scale (used across all themes)
  2. Create distinct primary colors for each brand
  3. Ensure all themes meet same accessibility standards
  4. Use semantic token naming for easy theme switching
  5. Document when to use each brand variant

Accessibility-First Approach

For products requiring AAA compliance:

  1. Start with contrast requirements (7:1 for normal text)
  2. Work backward to find valid color combinations
  3. Use darker/lighter shades than typical
  4. Test with colorblind simulators
  5. Provide high-contrast mode as option

Common Questions

Q: How do I make a palette feel unique without sacrificing usability? A: Use unique shades of standard semantic colors. Green for success is expected, but use a distinctive shade with appropriate cultural/domain context.

Q: The palette generator created colors that don't match my brand. What do I do? A: The generator creates mathematically uniform scales. You can manually adjust specific levels while keeping others, or use it as a starting point and refine by hand.

Q: How many colors should a complete palette have? A: Minimum: 1 primary (with scale), 1 neutral (with scale), 4 semantic colors. Typical: 1-2 primary, 1-2 secondary, 1-2 accent, 1 neutral (all with scales), 4 semantic colors. Complex: Multiple primaries, extensive semantic colors, specialized scales.

Q: What if the user insists on purple gradients? A: If it's genuinely part of their brand, make it distinctive: use unique purple shades, unexpected gradient combinations, or pair with unexpected accent colors. Avoid the default #6366F1 → #8B5CF6 → white pattern.

平台分布

Codex

36.69%
按下载量换算100

Claude

26.66%
按下载量换算73

Cursor

18.54%
按下载量换算50

Gemini CLI

8.7%
按下载量换算24

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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