agent-friendly-design
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.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/aa-on-ai/agent-friendly-designWhat This Skill Does
The agent-friendly-design skill provides a comprehensive framework for optimizing web interfaces specifically for AI agent consumption. While traditional web design focuses on visual aesthetics for human users, this skill shifts the focus toward semantic architecture and programmatic accessibility. It treats the accessibility tree as the primary machine-readable API for the web, ensuring that agents can interpret site structure, identify interactive elements, and reliably navigate complex workflows. By leveraging semantic HTML, strategic ARIA implementation, and machine-readable state management, developers ensure their products are compatible with LLM-based browsers, headless agents, and automated navigation tools.
Installation
To integrate this skill into your environment, use the command: clawhub install openclaw/skills/skills/aa-on-ai/agent-friendly-design. This will initialize the agent-ready linting rules and documentation standards in your local repository, allowing the OpenClaw agent to audit your codebase against modern AI interaction protocols.
Use Cases
- E-commerce Optimization: Ensuring purchasing agents can successfully add items to carts and complete checkout flows by providing explicit semantic labels for dynamic elements.
- SaaS Platform Integration: Building complex dashboards that are navigable by research agents, allowing them to pull specific data points without requiring manual interaction.
- Headless Testing/QA: Improving the reliability of automated testing suites that rely on accessibility snapshots rather than fragile visual DOM selectors.
- AI-Native Interface Design: Creating dual-interface products that provide a rich visual experience for humans while maintaining a strictly semantic backend for AI consumers.
Example Prompts
- "Analyze my current landing page source code and identify why my checkout button is failing to be detected by automated agent crawlers."
- "Refactor this search component to include appropriate aria-live regions so that an AI agent knows when search results have successfully updated."
- "Generate a llms.txt summary for my documentation site to ensure AI models have the correct context when indexing my API capabilities."
Tips & Limitations
- The Golden Rule: Always prioritize native HTML elements over ARIA roles. ARIA is a supplement, not a substitute. If a native button works, never use a div with an onClick handler.
- Avoid 'Div Soup': The biggest barrier to agent consumption is a DOM flooded with unsemantic divs. Keep your structure clean and logical.
- Limitations: This skill focuses on the structure of the front-end. It cannot magically make a broken backend API functional. It is strictly a design and front-end architecture toolkit. Ensure your underlying data structure is exposed via JSON-LD or similar machine-readable formats for maximum agent interoperability.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-aa-on-ai-agent-friendly-design": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
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