Token导航 LogoToken导航TokenDH.com
研究检索只读github未标认证来源可访问许可证需确认审计提醒

figma-variables-tokens-generatorFigma variables tokens 生成器

Agent Skill

用于辅助界面设计、视觉规范、排版、配色、布局和交互体验优化。它适合让 Agent 根据产品场景整理页面结构、生成 UI 方案、检查视觉一致性或改进组件层级。使用时需要结合现有品牌、设计系统和用户任务,不应只堆装饰元素;涉及真实页面改动时,应通过截图或浏览器预览检查文本溢出、对齐和响应式表现。

总安装

724

周安装

29

GitHub Stars

3

下载量

234
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:figma-variables-tokens-generator(Figma variables tokens 生成器)
来源仓库:https://github.com/shanmus4/figma-variables-tokens-generator
仓库路径:skills/figma-variables-tokens-generator
安装命令:
npx skills add https://github.com/shanmus4/figma-variables-tokens-generator --skill figma-variables-tokens-generator
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/shanmus4/figma-variables-tokens-generator --skill figma-variables-tokens-generator

简介

用于辅助界面设计、视觉规范和布局优化,适合整理页面结构或生成 UI 方案。

  • 支持根据产品场景检查视觉一致性并改进组件层级,结合品牌和设计系统使用。
  • 通过浏览器预览检查文本溢出和对齐,避免堆砌装饰元素,关注实际交互体验。
  • 安装方式:github,命令为 npx skills add https://github.com/shanmus4/figma-variables-tokens-generator --skill figma-variables-tokens-generator。
  • 注意:涉及真实页面改动时需配合截图或本地预览确认响应式表现和视觉效果。

SKILL.md

Figma Variables Tokens Generator

Generate production-ready Figma Variables JSON ZIPs that import with zero errors. Approach each system as both senior product designer and senior frontend engineer. Enforce ID stability across modes and absolute path normalization throughout.

Non-Negotiable Generation Invariants

These rules apply to every architecture, every collection, every naming style, and every user input combination.

  1. One canonical token path per variable

- A token path must have exactly one canonical representation across: - planning/data maps - prebuild_ids() - create_token() - emitted JSON nesting keys - aliasData.targetVariableName - validation registries - Never preserve one casing in JSON and a different casing in the registry. - Never let alias targets be normalized differently from emitted variable names.

  1. Variable paths are architecture data, not code syntax

- com.figma.codeSyntax may be camelCase, kebab-case, CSS custom properties, Android, or iOS. - The token path itself must remain a stable collection path. - Do not convert token paths to match code syntax conventions. - Preserve semantic separators exactly as required by the references: for example link-hover, on-brand, on-surface-variant, lineHeight, letterSpacing, borderWidth, minWidth, maxWidth.

  1. Literal path preservation

- When a reference file defines a path literally, emit that path literally. - Do not silently rewrite kebab-case to camelCase or remove hyphens from semantic names. - Component and custom collections may introduce new paths, but once chosen, that spelling must remain identical end-to-end.

  1. Artifact-level validation is mandatory

- Do not trust only the internal registry. - After building all files, flatten the actual emitted JSON keys from every collection and validate every alias target against those emitted paths. - A build is not safe unless the emitted JSON graph and the internal registry agree exactly.

  1. Scope correctness is mandatory

- Validate scopes from the emitted artifact, not just helper intent. - text/* and all text-role descendants must resolve to TEXT_FILL. - border/* and border-role descendants must resolve to STROKE. - icon/* must resolve to SHAPE_FILL + STROKE. - shadow/*/color must resolve to EFFECT_COLOR. - Numerical typography paths must resolve to FONT_SIZE, LINE_HEIGHT, LETTER_SPACING. - If a path is ambiguous, add an explicit rule before generating. Never rely on a broad fill fallback for semantic text or border tokens.

  1. Coverage floors are contractual

- If the user selected Lean, Standard, or Enterprise, the generated token inventory must satisfy that density floor in the relevant references. - Do not under-generate because a manual data map happened to stop early. - Theme/Semantic/Component outputs must be derived from the required path inventories first, then extended as needed.

  1. Backfilling prevents absence, not identity drift

- Backfill missing parent values before saving parent collections. - But do not assume backfilling solves path-casing, path-spelling, or alias-name mismatches. - Missing token and mismatched token are different failure classes and must be validated separately.

Output format: Deliver only .zip files containing the JSON tokens. Do not output .skill files or dump raw Python scripts — users expect ready-to-import ZIPs, not code, and dumping scripts into the chat causes context truncation that breaks the generation.

Local IDE / CLI Output Rule

When running in a local IDE or CLI workspace:

  • Always create an exports/ folder if it does not exist
  • Save the final token ZIP inside exports/
  • Do not scatter generated artifacts in the project root
  • Do not create extra manifests, debug JSON files, helper reports, or generator scripts unless the user explicitly asked for them or they are required to recover from a generation failure

Default expectation:

  • one final ZIP in exports/
  • minimal additional files

Read Order — STAGED LOADING

To prevent context pollution, read only the files required for your current load stage:

Load Stage 1: Discovery & Strategy (Read at Turn 1)

#FilePurpose
1instructions/01-interview-setup.mdInitial setup & Turns 1–3
2instructions/02-questionnaire-and-generation.mdQuestionnaire Turns 4–10, generation
3references/01-architecture.mdMandatory Strategy: Understanding Tiers and alias rules.

Load Stage 2: Architecture Confirmation (Read before Phase 2)

#FilePurpose
4references/05a-collections-core.mdDesign specs for Core collections
5references/05b-collections-semantic-components.mdDesign specs for Semantic/Component collections

Load Stage 3: Generation Logic (Read before Phase 3)

#FilePurpose
6references/02-scoping-rules.mdTechnical scoping tables
7references/03-json-format.mdExact W3C JSON structure
8references/04-primitives.mdRaw hex/spacing/font data
9references/06-generator-utility.mdPython generation script patterns
10scripts/generator_core.pyExecutable Python engine (for local/IDE environments)

Load Stage 4: Delivery & Handoff (Read after ZIP delivery and token count reporting are complete)

#FilePurpose
11instructions/03-import-and-handoff.mdImport guide & ZIP reference table
Do not read Load Stage 3 implementation files (scoping rules, JSON format, generator utility) until the interview is 100% complete. Reading them early fills context with technical data that is not needed yet.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.37%
按下载量换算85

Claude

30.11%
按下载量换算70

Cursor

16.92%
按下载量换算40

Gemini CLI

8.35%
按下载量换算20

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills