open-design-landing

nexu-io/open-design · Agent Skill

使用 Atelier Zero 的視覺語言為品牌生成單頁 editorial collage 網站。自動新增動畫和粘性導航。

前往安裝 ↓

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發布者nexu-io組織
儲存庫nexu-io/open-design ↗
技能資料夾open-design-landing
星95,886
分支11,122
附帶檔案27
授權Apache-2.0
最近更新2026年9月13日
連結技能原始碼 ↗ · 儲存庫 ↗

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安裝

技能就是一個含 SKILL.md 的資料夾。選擇你的代理,在專案根目錄的終端機(macOS、Linux 或 Windows 的 Git Bash)執行指令。

把提示貼到 Claude、Codex 或其他助理:它會自行檢查並安裝技能。

Install the “open-design-landing” skill from mozgi.io (https://mozgi.io/skills/open-design-landing/) for me.

1. Download the archive: https://mozgi.io/skills/open-design-landing/download
   If it fails, take the skill folder from the source: https://github.com/nexu-io/open-design/tree/main/design-templates/open-design-landing
2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me.
3. Put the “open-design-landing” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.).
4. Make sure <skills folder>/open-design-landing/SKILL.md exists.
5. Briefly tell me what this skill does and offer to try it on my task.

If you cannot do a step yourself, tell me what to click or type.

技能檔案

27 個檔案 下載 ZIP

安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。

SKILL.md 內容

作者原文 · 唯讀

open-design-landing

Build a single-page editorial landing site (or a slide deck — see the

sibling open-design-landing-deck skill)

in the Atelier Zero design system: warm-paper background, Inter

Tight + Playfair Display, italic serif emphasis spans, dotted hairline

rules, coral terminating dots, scroll-reveal motion, and 16 surreal

collage plates.

This is the canonical OpenDesign marketing-page recipe — the example

output is the very page you see at open-design ↗.

The skill is fully parameterized. The agent fills one typed

inputs.json from the user's brief; the composer turns that JSON +

the canonical styles.css ↗ into a deployable artifact.

inputs.json + styles.css                       16 image slots
        │                                            │
        └──────────► scripts/compose.ts ◄────────────┘
                            │
                            ▼
              <out>/index.html  (self-contained)
              <out>/assets/      (PNG or SVG)

---

What you get

A single HTML file with all of:

  • Editorial topbar (volume / issue / language strip), Headroom-style

sticky nav with live GitHub star count.

  • 8 numbered Roman-numeral sections with paper-textured background:

hero (with 3 stat rings + 4-step index), about, capabilities (4 cards),

labs (5 cards + filter pills + progress bar), method (4 steps with

thumbnails), selected work (dark slab + 2 tilted cards), testimonial

(pull quote + 5 partner glyphs), CTA (ribbon + email pill).

  • Footer with 4 link columns + huge italic-serif kicker word.
  • Scroll-reveal motion on every section (IntersectionObserver, respects

prefers-reduced-motion).

  • Fully responsive at 1280 / 1080 / 880 / 560 breakpoints.

---

Workflow contract

Run these four steps in order. The agent should complete each step

before moving on, and prefer asking the user a focused question over

inventing copy.

1. Gather brand inputs

Use AskQuestion (or the equivalent in your UI) to collect the brand

brief in chunks; do not dump the entire schema.ts on the user.

Map their answers into inputs.json matching the typed shape.

The eight question groups, in order:

| Group | Schema fields | Min answers | Notes |

| :---- | :------------------------------------------------------ | :---------- | :--------------------------------------- |

| 1 | brand.{name,mark,tagline,description,location} | 5 | Mark = single glyph (Ø, ▲, ★…) |

| 2 | brand.{license,version,year,primary_url,contact_email}| 4 | URL is required; license defaults Apache-2.0 |

| 3 | nav[] (up to 5) | 3 | Optional count badges |

| 4 | hero.{label,headline,lead,primary,secondary,stats} | All | Headline as MixedText (sans+em+dot) |

| 5 | about + capabilities.cards[4] | All | 4 cards × {num,tag,title,body} |

| 6 | labs.cards[5] + method.steps[4] | All | Both grids fixed-arity |

| 7 | work.cards[2] + testimonial | All | 5 partner glyphs as inline SVG path data |

| 8 | cta + footer.{columns[4],mega} | All | Mega kicker is a MixedText like the headlines |

Open inputs.example.json ↗ for a complete

worked example (OpenDesign itself).

2. Decide the image strategy

| Strategy | When to choose | Cost / latency |

| :---------------- | :------------------------------------------------------ | :-------------------- |

| placeholder | First pass. Demo. Slide internal. No image budget yet. | $0, <1s |

| generate | Final delivery. Brand wants original collages. | ~$0.40, ~6 min |

| bring-your-own | User has art direction PNGs. Drop them at assets_path.| $0, 0s |

Set inputs.imagery.strategy accordingly.

placeholder — frame mode
npx tsx scripts/placeholder.ts <out>/assets/

Writes 16 .svg files (with .png aliases for compatibility) into

<out>/assets/. Each placeholder shows the slot id, ratio, pixel

dimensions, and the prompt hint from image-manifest.json. The

composer's <img src='./assets/hero.png'> etc. just work.

generate — gpt-image-2 mode
FAL_KEY=... npx tsx scripts/imagegen.ts <inputs.json> --out=<out>/assets/

Calls fal.ai's openai/gpt-image-2 synchronous endpoint per slot.

Composes prompts as: style anchor (paper-collage editorial system)

+ brand variables (name / nav / headline / italic emphasis pulled

from inputs.json) + per-slot composition (e.g. cropped plaster

head + tree growing through arch). Skips slots whose target file

already exists; pass --force to re-render.

Without FAL_KEY, the script prints the prompts so the operator can

route them through the /gpt-image-fal slash-command skill manually.

bring-your-own

Drop 16 PNGs matching assets/image-manifest.json filenames at

inputs.imagery.assets_path. Done.

3. Compose the artifact

npx tsx scripts/compose.ts <inputs.json> <out>/index.html

The composer reads inputs.json and ../styles.css, then writes one

self-contained HTML file. The page includes:

  • The full Atelier Zero stylesheet, inlined.
  • All section markup with data-reveal attributes for staggered

scroll motion.

  • Inline IntersectionObserver script for [data-reveal].
  • Inline Headroom nav script (mirrors header.tsx).
  • Inline GitHub star-count fetcher (auto-detects from brand.primary_url).

---

Self-check before delivering

Before marking done, the agent must verify:

  • [ ] <out>/index.html opens in a browser without console errors.
  • [ ] All 16 image slots load (no 404s in DevTools network tab).
  • [ ] Headline italic emphasis spans render in Playfair (not sans).
  • [ ] Coral terminating dots appear at every display h1/h2 end.
  • [ ] Scroll from top to bottom; every section animates in once.
  • [ ] Resize to 880px and 560px; no horizontal scroll, no overlap.
  • [ ] prefers-reduced-motion: reduce (DevTools → Rendering) disables

transitions cleanly.

  • [ ] Lighthouse: contrast AA, font-display swap, no layout shift on the

hero (CLS < 0.05).

---

Files in this skill

design-templates/open-design-landing/
├── SKILL.md                 # this contract
├── README.md                # quick-start
├── schema.ts                # typed inputs (single source of truth)
├── styles.css               # Atelier Zero stylesheet (single source of truth)
├── inputs.example.json      # OpenDesign as the worked example
├── example.html             # canonical rendering (regenerated from inputs.example.json)
├── scripts/
│   ├── compose.ts           # inputs.json + styles.css → index.html
│   ├── imagegen.ts          # gpt-image-2 wrapper (fal.ai)
│   └── placeholder.ts       # SVG paper-textured frames
└── assets/
    ├── *.png                # 16 collage plates (OpenDesign instance)
    ├── image-manifest.json  # slot → file/dimensions/prompt mapping
    └── imagegen-prompts.md  # human-readable prompt pack

---

Boundaries

  • Do not invent new colors or typefaces. Tokens live in

design-systems/atelier-zero/DESIGN.md; extend the design system

before adding a new ramp here.

  • Do not drop data-reveal attributes from generated markup.

Without them the page goes static and feels dead.

  • Do not wrap the composed HTML in a framework that injects its

own stylesheet ordering — Atelier Zero relies on stylesheet-order

cascade for paper texture and z-index of side rails.

  • Do not add a separate stylesheet file that reorders Atelier Zero

cascade; keep styles.css as the single visual source.

See also

  • design-systems/atelier-zero/DESIGN.md — token spec.
  • design-templates/open-design-landing-deck/ — sibling slides skill that reuses this design system.

常見問題

open-design-landing 技能有什麼用途?

使用 Atelier Zero 的視覺語言為品牌生成單頁 editorial collage 網站。自動新增動畫和粘性導航。

如何安裝 open-design-landing?

打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI:那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示,讓助理自行檢查並安裝技能。

open-design-landing 適用哪些 AI 模型?

技能是純 Markdown 指示(Agent Skills 格式),因此 open-design-landing 適用任何讀取 SKILL.md 的代理:Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時,模型才會載入指示。

open-design-landing 是免費的嗎?

此技能由 nexu-io 公開發布,採用 Apache-2.0 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。

安裝技能安全嗎?

技能是代理會遵循的指示,安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作,不要授予多餘權限。

技能和 MCP 伺服器或外掛有何不同?

MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程:如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。

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