Token导航 LogoToken导航TokenDH.com
效率敏感数据clawhub未标认证来源可访问clear审计提醒

figma-to-mobileFigma TO mobile 浏览器

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

5,092

周安装

204

GitHub Stars

公开资料未说明

下载量

1,648
OpenClaw

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

MIT-0

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-to-mobile(Figma TO mobile 浏览器)
来源仓库:https://github.com/timeaground/figma-to-mobile
安装命令:
openclaw skills install figma-to-mobile
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install figma-to-mobile

简介

将 Figma 设计转换为移动端原生代码的转换工具。

  • 支持 Android(Jetpack Compose/ XML)和 iOS(SwiftUI/ UIKit)。
  • 提供跨平台 UI 自动生成能力。
  • 安装命令:openclaw skills install figma-to-mobile。
  • 需用户提供 Figma 链接并明确目标平台要求。

SKILL.md

name
figma-to-mobile
description
>
metadata

Figma to Mobile

Convert Figma designs to mobile UI code with interactive clarification.

Supported: Android Compose, Android XML, iOS SwiftUI, iOS UIKit.

Prerequisites

  • FIGMA_TOKEN environment variable set (Figma > Settings > Personal Access Tokens)
  • Python 3.8+ with requests package

Trigger & Input

This skill activates when a user provides a Figma link.

The user may also include inline hints alongside the link, such as:

  • Target platform: "Android XML", "Compose", "SwiftUI", "UIKit"
  • Layout preferences: "use ConstraintLayout", "prefer StackView"
  • Component notes: "the switch is our custom CompactSwitch", "this is a dynamic list"
  • Any other context about the design

If the user provides hints, respect them and skip the corresponding questions. For example, if the user says "Android XML, the 3 cards are a RecyclerView list", do NOT ask about output format or whether the cards are dynamic/static.

Workflow

Step 1: Fetch & Analyze

When user provides Figma link(s):

  1. Determine the input type:

A. Link without specific node-id (no node-id, or node-id=0-1): This link points to the entire page, not a specific frame. Tell the user: > This link points to the whole page. Please select the frame you want in Figma, right-click it, and choose "Copy link to selection", then send that link. > If you want to convert multiple frames, send multiple links.

B. Single frame link (has specific node-id): Run scripts/figma_fetch.py "<url>" → returns that frame's design data. Proceed to analysis.

C. Multiple links (user sends 2+ URLs): First, determine the relationship by examining frame names and user context:

- Same page, different visual states (e.g. "首页-有banner" and "首页-无banner"): Use --compare mode to fetch all and get a diff summary. Generate multi-state code (conditional visibility, state switching).

- Parent page + overlay/drawer (e.g. "首页" + "首页-抽屉-xxx"): Generate each as an independent layout file. Then tell the user the relationship: > Frame 1 ("首页") and Frame 2 ("首页-抽屉") look like a main page + side drawer. > I've generated two separate layout files. How you wire them together (DrawerLayout, Navigation, etc.) depends on your project architecture.

The Skill's job is generating UI layout code, not deciding architecture (Activity vs Fragment vs Navigation).

- Different independent pages (e.g. "首页" + "设置页" + "个人中心"): Process each independently. Fetch them one at a time with a pause between requests to avoid rate limiting. Present a summary of all pages, then ask which to convert first (or convert all sequentially).

- Not sure: Ask the user — "These frames look related but I'm not sure how. Are they different states of the same page, a page with an overlay, or independent pages?"

Rate limit protection for multiple links: When fetching multiple nodes, wait 2-3 seconds between requests. Never fire more than 2 requests in parallel.

  1. If the link has no specific node-id, ask the user to re-copy from the specific frame (see A above). Do NOT call the API.
  1. Analyze the structure: identify sections, repeated patterns, component types
  2. Note INSTANCE nodes — they indicate reusable components. Check variantProperties for component state (e.g. State=Default, Size=Large) — these map to multi-state code
  3. Note gradient/shadow data — flag for the user if complex
  4. Apply Figma node interpretation rules before generating code

Detailed interpretation rules: Read references/figma-interpretation.md

Step 1.5: Structure Summary

Before asking any questions, present a brief structure summary to the user so they can confirm your understanding:

I see: [navigation bar with back button + title] → [2 content sections: user profile card, settings list (8 items)] → [bottom action button]. Total ~25 nodes.

Keep it to 2-3 lines. Mention:

  • Major sections identified (nav bar, content areas, footer)
  • Repeated patterns ("8 similar list items", "3 tab labels")
  • Notable elements (gradients, complex illustrations, stacked cards)

If the user says "that's wrong" or corrects the structure, adjust understanding before proceeding to Step 2.

If the structure is simple and obvious (e.g., a single card with a few text fields), skip this step.

Step 2: Confirm & Clarify

Question priority (strict order — ask earlier questions first):

  1. Output format (MUST ask first unless user already specified)

→ Android XML / Compose / SwiftUI / UIKit This determines all subsequent analysis phrasing and code output.

  1. Structural ambiguities (only ask what you're genuinely unsure about)

→ "These N items look similar — dynamic list or fixed layout?" → "This area: single image asset or icon-on-background combo?"

  1. Component choices (only if platform-relevant)

→ "Any custom components to use? (otherwise I'll use platform defaults)"

Rules for questions:

  • Skip any question the user already answered via inline hints
  • Max 3-5 questions total, fewer is better
  • Each question gives concrete options with one-line pros/cons
  • Every question includes an open option: "or tell me more about this"
  • Use natural language, no JSON or technical dumps
  • If everything is clear (user gave full context + simple structure), skip Step 2 entirely

Confidence guide — when to ask vs. when to just generate:

  • ≥3 sibling nodes with similar structure → likely a list → ASK (dynamic vs static)
  • INSTANCE nodes sharing same componentId → reusable component → MENTION but can default
  • Single clear hierarchy, no ambiguity → high confidence → SKIP questions, go to Step 3
  • Gradient/complex shadow in design → MENTION in summary ("I see a gradient here, I'll approximate it as X")

Step 2.5: Project Scan (optional but recommended)

If the target project is available locally, run a project scan:

python scripts/project_scan.py /path/to/project --json --output scan-report.json

How to use scan results in code generation: Read references/scan-usage.md

Step 3: Generate Code

After user confirms (or if no questions needed), generate code files.

Detailed generation rules: Read references/generation-rules.md

If multiple files are needed, output each with a clear filename header:

📄 activity_notification_settings.xml
[code]

📄 item_expert_notification.xml
[code]

Step 4: Iterate & Capture Feedback

After showing code, ask briefly:

Matches the design? Any adjustments?

The user can then give feedback to refine the output. Common iterations:

  • "间距大了" → adjust specific spacing
  • "Switch 换成我们的 CustomSwitch" → swap component
  • "把标题栏去掉" → remove section
  • "换成 Compose 版本" → regenerate in different format
  • "颜色不对,这里应该是 #333333" → fix specific values

Continue iterating until the user is satisfied. Each round only regenerates the changed parts, not the entire file (unless the user asks for full regeneration).

Feedback capture (automatic): Whenever the user corrects your generated output, log the correction to feedback-log.md in the project root (create if it doesn't exist). Each entry follows this format:

## YYYY-MM-DD HH:MM
- **Platform**: Android XML / Compose / SwiftUI / UIKit
- **Figma node type**: (e.g., FRAME with icon, Tab bar, Button group)
- **Issue**: Brief description of what was wrong
- **Before**: What the agent generated (snippet or description)
- **After**: What the user wanted (snippet or description)
- **Rule candidate**: (optional) If this correction suggests a general pattern rule, note it here

Log entries should be:

  • Concise — only the relevant diff, not entire files
  • Categorized — always include platform and Figma node type for later analysis
  • Actionable — focus on the mapping error, not cosmetic preferences (e.g., "user prefers 16dp" is not a rule; "VECTOR compositions should be single ImageView" is)

Do NOT log:

  • One-off personal preferences (specific color choices, naming conventions)
  • Corrections to non-mapping issues (typos, import statements)
  • Feedback the user explicitly says is project-specific, not general

Periodically (or when asked), run scripts/feedback_analyze.py to identify patterns and generate rule candidates.

Error Handling

  • FIGMA_TOKEN not set (script outputs FIGMA_TOKEN_NOT_SET) → do NOT ask user to run commands. Instead:

1. Tell the user you need a Figma Personal Access Token 2. Tell them where to get it: Figma → avatar (top-left) → Settings → Security → Personal Access Tokens 3. Ask them to paste the token in chat 4. Once they provide it (starts with figd_), write it to the project root .env file: echo 'FIGMA_TOKEN=figd_xxx' >> .env 5. Retry the figma_fetch command — it will read from .env automatically

  • FIGMA_TOKEN invalid (API returns 403/401) → token may have expired or been revoked. Ask user to regenerate and paste new token. Update .env file.
  • Invalid URL → show valid URL example: https://www.figma.com/design/<fileKey>/<name>?node-id=<id>
  • API error → show error message, suggest checking network/proxy
  • Node too large (>200 children) → suggest selecting a smaller frame
  • Depth auto-increased → the script auto-retries with deeper depth if it detects truncated children. Inform user if this happens ("I needed to fetch deeper to get all details").

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

83.16%
按下载量换算1,370

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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