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ai-ready-repo

Convert codebases into DDD artifacts and code-intel.json for AI agents.

36|4|Updated Feb 11, 2026
Search Tags:#automation#ddd#ai-ready#code-understanding#repo-analysis#engineering-ops#code-intel
One-click install
npx skills add https://github.com/xg-gh-25/SwarmAI --skill ai-ready-repo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-ready-repo
Source: https://github.com/xg-gh-25/SwarmAI/tree/main/backend/skills/s_ai-ready-repo
Command: npx skills add https://github.com/xg-gh-25/SwarmAI --skill ai-ready-repo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Generate AI-ready artifacts for codebases to empower AI agents to understand, plan, and evolve software systems.

Core Features & Use Cases

  • Transforms a repository into a structured set of DDD-like documents (AGENTS.md, PRODUCT.md, TECH.md, IMPROVEMENT.md, PROJECT.md, code-intel.json)
  • Provides deterministic, replayable output for AI-driven analysis and automation
  • Use case: onboard a new project by generating a self-describing context that guides automation and decision-making

Quick Start

Say "make AI-ready" and point SwarmAI at the repository to generate the AI-ready outputs.

Frequently Asked Questions about ai-ready-repo

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

FAQPage Schema
How do I make a codebase AI-ready for automated code understanding?▼

To make a codebase AI-ready, you generate structured DDD artifacts and a code-intel.json file that enable AI agents to understand and operate on the project. This provides deterministic, replayable context for automated analysis and evolution.

What is the best way to onboard AI agents to a new repository using DDD artifacts?▼

Onboarding AI agents involves generating DDD-like documents such as AGENTS.md, PRODUCT.md, and TECH.md. These files create a self-describing context that guides automation, planning, and decision-making for the new repository.

How do I generate deterministic code-intel.json outputs for repo analysis?▼

Generating deterministic code-intel.json outputs requires validating generated artifacts against a v2 code-intel schema. This ensures the structured DDD docs provide consistent, replayable results for AI-driven analysis.

Does ai-ready-repo require any dependencies to analyze codebases?▼

No, ai-ready-repo does not require external dependencies to analyze codebases. It operates directly on the repository to generate structured DDD artifacts and code-intel.json for AI agents.

Can I use generated DDD documents for automated software evolution planning?▼

Yes, generated DDD documents such as IMPROVEMENT.md and PROJECT.md enable automated software evolution planning. They provide the structured context AI agents need to understand the system and plan improvements.

When should I not use ai-ready-repo for engineering ops?▼

You should not use ai-ready-repo for engineering ops if your project requires non-deterministic outputs or cannot validate against the v2 code-intel schema, as deterministic output is mandatory for proper AI agent operation.

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