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Agent Skill · 研究检索

agent-friendly-design(Agent 友好设计)

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。

agent-friendly-design 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为操作浏览器;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 20,143。

总安装 5,316周安装 213GitHub Stars 公开资料未说明下载 1,721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
MIT-0
版本
1.0.0

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:agent-friendly-design(Agent 友好设计)
来源仓库:https://github.com/aa-on-ai/agent-friendly-design
安装命令:
openclaw skills install agent-friendly-design
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 agent-friendly-design

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。

辅助界面设计、视觉规范和交互体验优化。agent-friendly-design 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合生成 UI 方案、检查组件层级和响应式表现。使用时需结合品牌规范和设计系统,避免过度装饰。通过 clawhub 安装,适用于 OpenClaw 宿主环境。

涉及页面改动时应通过截图或预览验证实际效果。

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 OpenClaw;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 openclaw skills install agent-friendly-design;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

    它需要操作浏览器,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

agent-friendly-design 怎么安装?

在终端执行 openclaw skills install agent-friendly-design。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

agent-friendly-design 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

agent-friendly-design 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

agent-friendly-design 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

agent-friendly-design 用的人多吗?

公开数据是累计安装 5,316 次,近一周安装 213 次,下载 1,721 次。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。

agent-friendly-design 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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 OpenClaw,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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 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
agent-friendly-design

Agent 友好设计 · 本页

5,316—操作浏览器MIT-0通过

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

name
agent-friendly-design
description
Design websites and applications that AI agents can consume, navigate, and interact with. Use when building any site, app, or product that agents will use as an end-user — not just crawl or index. Covers semantic structure, accessibility-as-agent-interface, machine-readable data, API-first patterns, and the emerging protocols (llms.txt, MCP, NLWeb, A2UI) that make sites agent-ready. Triggers on: agent-friendly, agent-readable, agent-accessible, AX, agent experience, agentic web, dual-interface, machine-readable, llms.txt, MCP integration, NLWeb, accessibility tree, ARIA for agents, structured data, JSON-LD, Schema.org, API-first design, build for agents, agent-ready.

agent-friendly design

how to build sites and apps that AI agents can actually use.

why this exists

most design guidance is about agents *building* UI. this skill is about agents *consuming* UI. the audience for your site is no longer just humans — automated traffic surpassed human traffic in 2024 (51% of all web interactions). agents browse, compare, purchase, research, and interact on behalf of users.

if your site is invisible to agents, you're invisible to a growing majority of the web.

the core insight

the accessibility tree — originally built for screen readers — is becoming the primary interface between AI agents and your website. OpenAI's Atlas explicitly uses ARIA tags to interpret page structure. Microsoft's Playwright MCP provides accessibility snapshots, not screenshots. even vision-first agents (Anthropic Computer Use, Google Mariner) are incorporating accessibility data for reliability.

building for agents and building for accessibility are the same work. every improvement you make for one benefits the other.

the checklist

1. semantic HTML (non-negotiable)

agents can't "see" your layout. they read structure. div soup is invisible.

  • use native elements: <button>, <nav>, <main>, <article>, <section>, <header>, <footer>, <aside>
  • proper heading hierarchy: <h1> through <h6>, no skipped levels
  • <label> elements explicitly associated with form inputs (use for attribute)
  • <table> for tabular data with <thead>, <th>, and scope attributes
  • links are <a> elements, buttons are <button> elements. never <div onclick>
  • use <form> for forms, with <fieldset> and <legend> for groups
  • meaningful <img alt> text (not "image1.png")

test: disable CSS. can you still understand the page? if yes, agents can too.

2. ARIA: useful, not magic

ARIA supplements semantic HTML — it doesn't replace it. use it when native elements can't express the interaction.

  • aria-label on buttons and links that lack visible text (icon buttons, close buttons)
  • aria-expanded on toggles, dropdowns, accordions
  • aria-live regions for dynamic content agents need to notice (status messages, notifications)
  • role attributes only when there's no native element (e.g., role="tablist", role="dialog")
  • aria-describedby for complex form fields that need additional context
  • aria-hidden="true" on decorative elements agents should ignore

don't do: put ARIA on everything. over-labeling creates noise. if the native element already communicates the role, skip the ARIA.

3. machine-readable state

agents can't interpret visual cues. a grayed-out button means nothing if disabled isn't set.

  • use disabled attribute on inactive controls
  • use aria-selected, aria-checked, aria-pressed for toggle states
  • loading states: aria-busy="true" on containers updating content
  • error states: aria-invalid="true" on fields with validation errors, plus aria-errormessage linking to the error text
  • empty states: render actual DOM content (not just CSS ::after pseudo-elements)
  • pagination: current page marked with aria-current="page"

the rule: if a human can tell the state by looking, an agent should be able to tell the state by reading the DOM.

4. structured data (JSON-LD + Schema.org)

helps agents understand *what* your content is, not just how it's structured.

  • Organization, Product, Article, BreadcrumbList, FAQPage, HowTo schemas as appropriate
  • JSON-LD in <script type="application/ld+json"> (preferred over microdata or RDFa)
  • keep structured data in sync with visible content — don't stuff keywords
  • include dateModified and datePublished so agents know freshness
  • product pages: price, availability, reviews, SKU
  • articles: author, datePublished, headline, description

5. llms.txt

a markdown file at /llms.txt that gives LLMs a concise overview of your site. like robots.txt, but for language models.

# Your Project Name

> Brief description of what this site/product does.

## Docs

- [Getting Started](https://yoursite.com/docs/getting-started): How to set up...
- [API Reference](https://yoursite.com/docs/api): Full API documentation...

## Optional

- [Changelog](https://yoursite.com/changelog): Recent updates...
  • keep it under 2000 tokens for quick context loading
  • link to detailed markdown versions of important pages (append .md to URLs if possible)
  • update when content changes — stale llms.txt is worse than none
  • spec: https://llmstxt.org

6. crawlability and rendering

agents can't all execute JavaScript. many rely on raw HTML.

  • server-side render (SSR) or pre-render critical content
  • don't hide key information behind client-side-only JavaScript
  • update robots.txt to allow known AI user-agents (ChatGPT-User, OAI-SearchBot, Googlebot, etc.)
  • maintain an up-to-date sitemap.xml
  • fast response times (agents may time out at 10-30 seconds)
  • important content should be high in the HTML source (agents may not scroll)

7. predictable interactions

agents automate by finding patterns. inconsistency breaks them.

  • consistent naming: if "Add to Cart" is the button text on one page, don't make it "Buy Now" on another
  • standard HTML form controls (not custom JavaScript widgets that look like inputs but aren't)
  • predictable IDs and class names across similar pages
  • avoid modals, popups, and interstitials that require complex dismissal sequences
  • minimize login walls for browsing (allow guest checkout where possible)
  • avoid CAPTCHAs on non-critical flows — they block agents completely

8. API-first patterns

if agents can get data without scraping HTML, everyone wins.

  • REST or GraphQL endpoints for core data (products, content, status)
  • OpenAPI spec published and linked from llms.txt
  • MCP (Model Context Protocol) server for direct tool access — agents can call your API through MCP without browser automation
  • NLWeb integration for natural language queries against your data
  • A2UI for agent-driven native interfaces (Google's open project — agents send component blueprints that inherit host app styling)
  • webhooks for state changes agents need to react to

9. content for agents, not just humans

  • write clear, descriptive page titles (agents use <title> heavily)
  • meta descriptions that actually describe the page content
  • use descriptive link text ("View pricing details") not generic ("Click here")
  • break long content into sections with headings
  • provide summaries or TL;DRs for dense pages
  • don't rely on images alone to convey information — always have text equivalents

10. test with agents

  • use browser dev tools to inspect the accessibility tree (Chrome: Accessibility pane in Elements)
  • test critical flows with AI browser agents (ChatGPT Atlas, Playwright MCP)
  • run Lighthouse accessibility audit — most issues it catches affect agents too
  • check that forms can be completed programmatically
  • verify that dynamic content updates are reflected in the DOM, not just visually

the dual-interface mental model

you're building two interfaces on one page:

human interface: visual hierarchy, color, typography, whitespace, motion, delight agent interface: semantic structure, ARIA roles, JSON-LD, predictable controls, API endpoints

they don't conflict. a well-structured semantic page is easier for humans AND agents. the dual-interface is additive, not a trade-off.

routing

  • every web project: items 1-3 (semantic HTML, ARIA, machine-readable state) are non-negotiable
  • public-facing sites: add items 4-6 (structured data, llms.txt, crawlability)
  • products/e-commerce/SaaS: add items 7-9 (predictable interactions, API-first, content optimization)
  • full agent-readiness: all 10 items

emerging protocols to watch

protocolwhat it doesstatus
llms.txtmarkdown site overview for LLMsadopted by many, simple to implement
MCPdirect tool access for agents (no browser needed)growing fast, Anthropic-led
NLWebnatural language queries against your dataMicrosoft open-source, early
A2UIagent-driven native UI componentsGoogle, early spec
AGENTS.mdproject-level agent instructionscommunity adoption growing

compounding

after building or auditing a site for agent-friendliness:

  • document which patterns agents struggled with → add to this skill's checklist
  • note which ARIA patterns made the biggest difference → share with the team
  • track agent traffic in analytics (user-agent strings) → understand actual usage
  • test new protocols as they mature (MCP, A2UI) → update this guide

the web is shifting from page-driven to action-driven. designing for agents isn't future-proofing — it's building for today's traffic.

平台分布

OpenClaw

72.13%
按下载量换算1,241

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。目前只有一个公开来源,建议同时核对源仓库。

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