Context window cost & dependency fragmentation analysis for AI-ready codebases

Semantic duplicate pattern detection for AI-generated code

Shared utilities, types, and scoring logic for all AIReady analysis tools

AIReady MCP Server - Standardized code analysis for AI agents

Interactive visualization tool for AIReady codebase analysis

Analyzes betweenness centrality and fan-out to assess ripple effect risks in AI-assisted development

Dependency health analyzer for AI training-cutoff skew and package freshness

Tracks documentation freshness vs code churn to identify outdated AI agent context

AI-Ready Data Mesh is a concise MkDocs site for senior architects and data leaders, focused on the structural foundations required for scalable, trustworthy AI. It reframes Data Mesh as an enterprise architecture approach covering ownership, governance, platform design, organisational change, and practical adoption, with reusable templates for read

Measures structural type safety and boundary validation to reduce fallback cascades for AI agents

Detects hallucination-risk patterns like boolean traps and magic literals to improve AI comprehension

AIReady best practices skill for AI agents - procedural knowledge for writing maintainable code

Check naming conventions and pattern consistency across your codebase - @aiready/consistency package

Measures how well a codebase provides structured context for AI agents to understand domain boundaries

Unified shared UI components library (shadcn/ui based) and D3 charts for AIReady analysis tools

Measures code verify-loop friction and side-effect density for autonomous AI agents