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Agent Skill · 研究检索

figma-variables-tokens-generator(Figma variables tokens 生成器)

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。

figma-variables-tokens-generator 是研究检索类 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,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 47,657。

总安装 724周安装 29GitHub Stars 3下载 234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: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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/figma-variables-tokens-generator

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/shanmus4/figma-variables-tokens-generator --sk…;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

figma-variables-tokens-generator 怎么安装?

在终端执行 npx skills add https://github.com/shanmus4/figma-variables-tokens-generator --skill figma-…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

figma-variables-tokens-generator 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。

figma-variables-tokens-generator 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。

figma-variables-tokens-generator 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。

figma-variables-tokens-generator 用的人多吗?

公开数据是累计安装 724 次,近一周安装 29 次,下载 234 次,GitHub 3 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。

figma-variables-tokens-generator 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 需要处理研究检索类任务、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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
figma-variables-tokens-generator

Figma variables tokens 生成器 · 本页

7243只读未明确有提醒

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

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.

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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