agent-friendly-publishing

Audit publishing surfaces and design agent-readable information architecture.

1|Updated Mar 6, 2014

Search Tags:#documentation #content strategy #publishing #ai agents #information architecture #llms.txt

Ready to run

npx skills add https://github.com/79yuuki/dotfiles --skill agent-friendly-publishing

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-friendly-publishing
Source: https://github.com/79yuuki/dotfiles/tree/main/claude/skills/agent-friendly-publishing
Command: npx skills add https://github.com/79yuuki/dotfiles --skill agent-friendly-publishing

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps teams structure publishing surfaces so people and AI agents can quickly discover, understand, cite, and act on important information without searching through fragmented or ambiguous content.

Core Features & Use Cases

Publishing Surface Audits: Review documentation, websites, READMEs, API pages, changelogs, policies, and preview environments for discoverability gaps.
Agent-Readable Information Architecture: Design canonical page structures, machine-readable metadata, stable URLs, and content layers that improve AI agent parsing and reuse.
Controlled Preview Guidance: Create safer publishing lanes for internal prototypes and demos with ownership, access control, lifecycle, and data isolation considerations.

Quick Start

Use the agent-friendly-publishing skill to audit my documentation site and create an agent-ready publishing improvement plan.

Frequently Asked Questions about agent-friendly-publishing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I make documentation sites discoverable for AI agents?▼

Make documentation sites discoverable for AI agents by applying structured content hierarchy, machine-readable metadata, and stable URLs to publishing surfaces. This ensures agents can reliably interpret, cite, and act on information without parsing fragmented content.

What is an agent-readable information architecture?▼

An agent-readable information architecture uses canonical page structures, machine-readable metadata, and content layers to improve AI agent parsing and reuse. It organizes publishing surfaces so agents can reliably discover and interpret information.

How do I audit a website for AI agent content discoverability gaps?▼

Audit a website for AI agent discoverability gaps by reviewing documentation, READMEs, API pages, changelogs, and preview environments. This identifies missing structured content hierarchy or machine-readable metadata preventing reliable agent parsing.

Does my API reference need machine-readable metadata for LLM parsing?▼

Your API reference needs machine-readable metadata and stable URLs for reliable LLM parsing. Structured content hierarchy and lifecycle governance ensure AI agents can discover, interpret, and cite API information accurately.

What is the best way to structure an llms.txt file for content strategy?▼

Structure an llms.txt file for content strategy by establishing canonical page structures, machine-readable metadata, and content layers. This creates stable publishing practices that improve AI agent parsing and information reuse.

How do I create controlled preview environments for internal prototypes?▼

Create controlled preview environments for internal prototypes by defining safer publishing lanes with ownership, access control, lifecycle, and data isolation considerations. This ensures internal demos and preview surfaces remain governed and isolated.