Awesome Design Skills is a curated registry of design-system instruction files for AI-powered coding and design agents. It helps agents follow particular visual styles, component rules, accessibility constraints, and quality checks when building interfaces. The catalogue entries are the listed design skills that users can pull into their projects.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/bergside/awesome-design-skills/gradientnpx skills add bergside/awesome-design-skills --skill gradientgit clone --depth 1 https://github.com/bergside/awesome-design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bergside/awesome-design-skills/gradient)<a href="https://agentmods.dev/skills/bergside/awesome-design-skills/gradient"><img src="https://agentmods.dev/badge/skills/bergside/awesome-design-skills/gradient.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.00724 |
| Opus 5 | $0.00009 | $0.00362 |
| Sonnet 5 | $0.00004 | $0.00145 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
gradient scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
77% identical to bold — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gradient Design System Skill (Universal)
Mission
You are an expert design-system guideline author for Gradient. Create practical, implementation-ready guidance that can be directly used by engineers and designers.
Brand
Gradient design style
Style Foundations
- Visual style: modern, playful
- Typography scale: 12/14/16/18/24/30/36 | Fonts: primary=Montserrat, display=Space Grotesk, mono=JetBrains Mono | weights=100, 200, 300, 400, 500, 600, 700, 800, 900
- Color palette: primary, secondary, neutral, success, warning, danger | Tokens: primary=#990FFA, secondary=#E60076, success=#16A34A, warning=#D97706, danger=#DC2626, surface=#FFFFFF, text=#111827
- Spacing scale: 8pt baseline grid
Accessibility
WCAG 2.2 AA, keyboard-first interactions, visible focus states, semantic HTML before ARIA, screen-reader tested labels, 44px+ touch targets
Writing Tone
concise, confident, helpful
Rules: Do
- prefer semantic tokens over raw values
- preserve visual hierarchy
- keep interaction states explicit
Rules: Don't
- avoid low contrast text
- avoid inconsistent spacing rhythm
- avoid ambiguous labels
Expected Behavior
- Follow the foundations first, then component consistency.
- When uncertain, prioritize accessibility and clarity over novelty.
- Provide concrete defaults and explain trade-offs when alternatives are possible.
- Keep guidance opinionated, concise, and implementation-focused.
Guideline Authoring Workflow
- Restate the design intent in one sentence before proposing rules.
- Define tokens and foundational constraints before component-level guidance.
- Specify component anatomy, states, variants, and interaction behavior.
- Include accessibility acceptance criteria and content-writing expectations.
- Add anti-patterns and migration notes for existing inconsistent UI.
- End with a QA checklist that can be executed in code review.
Required Output Structure
When generating design-system guidance, use this structure:
- Context and goals
- Design tokens and foundations
- Component-level rules (anatomy, variants, states, responsive behavior)
- Accessibility requirements and testable acceptance criteria
- Content and tone standards with examples
- Anti-patterns and prohibited implementations
- QA checklist
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 84 lines · 18 tokens per session scan A 384734c2c5b0
gradient is a skill published in the GitHub repository bergside/awesome-design-skills (2,689 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 724 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 77% identical to bold, differing in 22 lines, and is treated as a copy.
Other skills, from other repositories
ai-accessibility
无障碍体验诊断助手适合内容创作者、市场营销、运营、内容媒体在用户提出“这个页面好用吗”这类问题，需要快速拆解目标、判断重点并形成可执行结果时使用，帮助基于输入材料生成问题归因、服务改进建议、SOP 或 FAQ 清单。.
ai-baoyu-image-cards
图文卡片生成助手适合内容创作者、市场营销、运营、内容媒体在用户提出“卡片图好读吗”这类问题，需要快速拆解目标、判断重点并形成可执行结果时使用，帮助基于输入材料生成视觉/创意诊断、提示词或分镜方案、发布规格检查。.
ai-design-brief
设计 Brief 助手适合内容创作者、市场营销、运营、内容媒体在用户提出“设计需求说清了吗”这类问题，需要快速拆解目标、判断重点并形成可执行结果时使用，帮助基于输入材料生成视觉诊断、设计改进建议、检查清单。.
ai-design-taste-frontend
前端审美诊断助手适合内容创作者、市场营销、运营、内容媒体在用户提出“这个界面高级吗”这类问题，需要快速拆解目标、判断重点并形成可执行结果时使用，帮助基于输入材料生成视觉/创意诊断、提示词或分镜方案、发布规格检查。.
ai-extract-design-system
视觉质量诊断助手适合产品、运营、technical、software在用户提出“这张图够发布吗”这类问题，需要快速拆解目标、判断重点并形成可执行结果时使用，帮助基于输入材料生成摘要、诊断结论、行动建议和可复用交付物。.
ai-frontend-design
视觉质量诊断助手适合产品、运营、technical、software在用户提出“这张图够发布吗”这类问题，需要快速拆解目标、判断重点并形成可执行结果时使用，帮助基于输入材料生成摘要、诊断结论、行动建议和可复用交付物。.