figma

figma is a skill for Codex from seaworld008/Commonly-used-high-value-skills. It costs 32 tokens per session (1,878 once invoked), scanned A, original, MIT.

A connection between Figma and coding tools that retrieves design details, screenshots, variables, and assets for implementation. Figma is a collaborative app for designing interfaces.

In plain words
What is it for?
Use it to implement Figma components or pages, export design assets, inspect responsive variants, and compare code with the design.
Why use it?
It gives developers the source design information needed to reproduce layouts and visual details consistently in code.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to implement Figma components or pages, export design assets, inspect responsive variants, and compare code with the design.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/seaworld008/commonly-used-high-value-skills/figma
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add seaworld008/Commonly-used-high-value-skills --skill figma
Clone the repo
git clone --depth 1 https://github.com/seaworld008/Commonly-used-high-value-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for figma

README.md
[![agentmods](https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/figma/github.svg)](https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/figma)
Your own site
<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/figma"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/figma/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for figma

Your own site · 80×15
<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/figma"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/figma.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,878 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Privilege Escalation · line 117
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00032 $0.01878
Opus 5 $0.00016 $0.00939
Sonnet 5 $0.00006 $0.00376
Haiku 4.5 $0.00003 $0.00188

Measured 4d ago against content hash 5945caec2c3f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

figma scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

openclaw-skills/figma/SKILL.md · 131 lines

How it starts

The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Figma MCP (Design-to-Code)

Figma MCP 是连接设计稿与生产代码的超级引擎。它通过 Figma API 获取结构化设计上下文、变量定义及原始素材,并结合大模型的推理能力,将视觉稿一键转化为符合项目规范的 React/Tailwind/Vue 代码。

安装与前提条件

# 确保已安装 Figma MCP Server
mcp_call({ action: "search", keyword: "figma" });
# 获取访问令牌 (PAT)
# 详见 references/figma-mcp-config.md

Figma MCP 核心集成流程 (Workflow)

为了确保 1:1 的视觉还原度和高性能的代码实现,必须严格遵循以下流程:

  1. 获取设计上下文 (get_design_context):首先获取目标 Node ID 的结构化 JSON 表示,理解其层级、间距、填充和排版属性。
  2. 处理大数据量 (Metadata First):如果 Node 层级过深,先运行 get_metadata 获取高层级节点地图,再精准抓取需要的子节点。
  3. 视觉对齐 (get_screenshot):运行 get_screenshot 获取节点的实时渲染图片,作为实现时的绝对参考。
  4. 素材下载 (Asset Acquisition):在开始编码前,下载所有图标(Icons)和装饰图(Images)。
  5. 规范化翻译 (Code Translation):将 Figma 的属性(如 cornerRadius: 8)映射到项目的 Design Tokens(如 rounded-lg)。
  6. 最终验证 (Visual Regression):完成开发后,再次对比 Figma 截图,确保 1:1 还原。

触发条件 / When to Use

  • 新功能 UI 开发:用户给出一个 Figma 链接,要求实现对应的组件或页面。
  • UI 细节优化:针对现有页面进行微调,以对齐设计稿的最新改动。
  • 设计规范审计 (Design Audit):自动提取 Figma 里的 Color Palette 或 Typography 并与代码库中的 tailwind.config.js 对比。
  • 图标与静态资源同步:需要批量导出 SVG 图标并转换为 React 组件。
  • 响应式布局实现:需要查看不同 Breakpoints(Desktop, Tablet, Mobile)下的设计差异。

核心能力 / Core Capabilities

1. 结构化设计解析 (Structural Parsing)

  • 操作步骤
    1. 调用 mcp_call({ name: "get_design_context", arguments: { fileKey, nodeIds } })
    2. 解析 children 数组,识别 TEXT, VECTOR, INSTANCE 等节点类型。
    3. 自动计算 Auto-layout 属性(Gap, Padding, Alignment)。
  • 最佳实践:如果发现节点名称是“Frame 1234”,主动询问设计人员其真实的语义名称(如“Primary Button”)。

2. 图像与变量提取 (Variable Extraction)

  • 操作步骤
    1. 检索 Figma Variables 和 Styles。
    2. 将十六进制颜色码转换为项目使用的命名变量(如 #3B82F6 -> brand-primary)。
    3. 通过 get_file_variables 获取全局主题定义。
  • 最佳实践:优先使用 variable-id 而非硬编码颜色。

3. 视觉引导编码 (Vision-guided Coding)

  • 操作步骤
    1. 在编写 CSS/Tailwind 时,将 get_screenshot 的结果传入多模态模型进行视觉校验。
    2. 捕捉微小的阴影(Box-shadow)和渐变(Gradient)细节。
  • 最佳实践:使用 image_edit 技能在截图上标注出具体的像素间距,辅助编码。

4. 资产自动化处理 (Asset Handling)

  • 操作步骤
    1. 检测 Figma 返回的 localhost 素材源。
    2. 自动将其下载并命名为合规的格式(如 icon-user-profile.svg)。
    3. 如果是 SVG,自动通过 svgr 转化为 React 组件。

Read the full file on GitHub · 131 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago Changed · -27 tokens per session 5945caec2c3f
  2. 8d ago First seen · 131 lines · 59 tokens per session scan A 86867fb69dab

Subscribe to this mod's changes

figma is a skill published in the GitHub repository seaworld008/Commonly-used-high-value-skills (70 stars, last pushed 4d ago), licensed MIT. It adds 32 tokens to every session and 1,878 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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