设计变量映射与品牌规范统一|简诗 AI

SKILL前端设计
前端设计 未标注权限 许可证不明确
安装这个技能

Map an extracted Figma / source-code token bag onto the active OD design system, producing a deterministic mapping the generate stage can consume.

一句话选型结论

按资料字段整理

设计变量映射与品牌规范统一|简诗 AI是前端设计类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类前端设计 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
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许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install user_fccf428b/token-map
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

前端设计
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方user_fccf428b仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 community
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791655043
正文体量5.8 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
出页面与组件

按描述生成页面结构和组件草稿。

调整样式与布局

改间距、配色与响应式布局。

生成设计说明

把设计稿整理成开发能接的说明。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在前端设计场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
设计变量映射与品牌规范统一|简诗 AI当前 未标注权限 未标注 未标注 未标注 有
前端设计 只读 GitHub 仓库 882.3 万 Codex、Claude、Cursor 有
Vercel 组合模式 联网访问 GitHub 仓库 385.9 万 Codex、Claude、Cursor 有
Vercel React native skills 前端 只读 GitHub 仓库 246.9 万 Codex、Claude、Cursor 有
adapt 搜索 浏览器控制 GitHub 仓库 202.7 万 Codex、Claude、Cursor 有
使用我的浏览器 浏览器控制 GitHub 仓库 184.8 万 Codex、Claude、Cursor 有

SKILL.md 全文

5.8 KB
模型实际读到的说明书原文,来自技能仓库

Token map

Spec §10 / §21.3.1: every figma-migration / code-migration run
crosses the same boundary — "the source has its own tokens; the
target uses the active OD design system; we need a deterministic
mapping". This atom produces that mapping.

Inputs

  • figma/tokens.json from figma-extract (figma-migration), OR
  • code/tokens.json from design-extract (code-migration).
  • The active design system DESIGN.md (already injected into the
    prompt; the atom reads from the staged design-system context).

Output

project-cwd/
└── token-map/
    ├── colors.json     # { source: '#hex' | 'tokenName', target: '--ds-token' }[]
    ├── typography.json # font + size + weight pairings
    ├── spacing.json    # spacing scale crosswalk
    ├── unmatched.json  # { source: ..., reason: 'no-target-equivalent' }[]
    └── meta.json       # { sourceKind: 'figma' | 'code', generatedAt, atomDigest }

unmatched.json is the audit list a human reviews; the agent must
not invent target tokens silently.

Semantic token inference

Figma often exports anonymous source names such as color-3,
paint/17, or raw #5B8DEF. Do not ask the user to rename those
before mapping. First infer the semantic role from usage evidence:

  • Node path, component name, instance overrides, variant/state labels,
    frame name, layer name, and nearby text such as Primary,
    Selected, Link, Error, Focus, Nav, Button, or CTA.
  • CSS-like position in the rendered tree: background fill, foreground
    text/icon, border, divider, overlay, shadow tint, focus ring, status
    badge, chart series, or brand/accent treatment.
  • Contrast relationships: a color paired repeatedly with the main
    canvas is likely foreground; one paired with foreground inside CTA
    components is likely primary/accent background; a thin outline around
    interactive elements is likely border or focus-ring.
  • Reuse topology: a value that appears across primary buttons,
    selected tabs, and active nav items is stronger evidence for
    --ds-color-primary than a value that appears once in an illustration.

Use that role evidence to choose among existing active design-system
tokens and to decide whether an anonymous token should be renamed or
left unmatched before the executable mapping pass. Keep the on-disk
token-map contract unchanged: the atom still writes the existing bucket
files, unmatched.json, and meta.json only.

For example, this is a useful reasoning note for deciding whether
color-3 should map to the active primary token:

{
  "source": "color-3",
  "value": "#5B8DEF",
  "role": "primary",
  "targetCandidates": ["--ds-color-primary", "--ds-color-link"],
  "evidence": [
    "Button/Primary fill",
    "Selected tab indicator",
    "Link text in Settings frame"
  ]
}

Then map to an active design-system token only when the evidence is
role-based, not value-only. If the top candidates are too close to
call, or if the evidence points to conflicting roles (primary vs
link vs focus-ring), leave the source token unmatched using the
existing no-target-equivalent reason and include the competing
candidates in the hint. This keeps automation useful for common
anonymous-token cases while preserving human review for ambiguous
brand decisions.

Before / after expectation

Without semantic inference, an anonymous Figma token can only produce
an uncertain value-level mapping:

{
  "source": "color-3",
  "value": "#5B8DEF",
  "target": null,
  "reason": "no-target-equivalent"
}

With semantic inference, the same token should carry role evidence
before it is accepted:

{
  "source": "color-3",
  "value": "#5B8DEF",
  "target": "--ds-color-primary",
  "via": "name"
}

This deterministic v1 atom does not claim a measured accuracy lift by
itself. Treat the expected improvement as coverage of previously
manual anonymous-token cases when the Figma tree contains enough role
evidence. Real accuracy numbers require a fixture suite with known
source tokens, expected semantic roles, and a before/after agent run.
See examples/semantic-inference-before-after.json for a deterministic
same-token-batch simulation that compares the old value-level output
with the semantic inference output.

Convergence

The atom completes when every input token is either mapped or
explicitly recorded under unmatched.json with a non-empty
reason. The until evaluator reads tokens.unmatched.length === 0
on strict mode; default is "soft converge" (proceed with
unmatched.json populated).

Anti-patterns the prompt fragment forbids

  • Injecting a new token into DESIGN.md without explicit user
    approval (use a confirmation GenUI surface for that).
  • Mapping hex colours by visual proximity alone; perceptual ΔE
    thresholds belong in the visual-diff evaluator (Phase 7).
  • Collapsing distinct source tokens onto the same target token
    silently; record collisions in unmatched.json with reason
    target-collision.

Status

Implemented by the daemon runner in
apps/daemon/src/plugins/atoms/token-map.ts. It parses design-system tokens,
performs deterministic mapping, and writes the mapped and unmatched outputs.

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常见问题

5 问
答案由本站依据条目字段整理
设计变量映射与品牌规范统一|简诗 AI怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_fccf428b/token-map。安装方式为未标注。装完刷新技能列表或重开客户端。

设计变量映射与品牌规范统一|简诗 AI支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

设计变量映射与品牌规范统一|简诗 AI权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

设计变量映射与品牌规范统一|简诗 AI收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

设计变量映射与品牌规范统一|简诗 AI用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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