Traditional code metrics like cyclomatic complexity and lines of code don't capture the real blockers for AI-assisted development teams. Here are the 9 dimensions that actually matter for AI-readiness.
The 9 Dimensions of AI-Readiness
Semantic Consistency - How consistently your codebase uses naming conventions and patterns
Context Window Efficiency - How much context AI needs to understand your code
Import Chain Depth - How deep your dependency chains go
Domain Cohesion - How well related logic is grouped together
Pattern Recognition - How easily AI can identify and reuse patterns
Documentation Coverage - How well-documented your code is
Type Safety - How well your types guide AI understanding
Test Coverage - How well your tests validate AI suggestions
Architectural Clarity - How clear your system's structure isWhy Traditional Metrics Fall Short
Traditional metrics were designed for human developers, not AI assistants. They measure code complexity from a human perspective, but AI has different strengths and weaknesses:
AI excels at pattern recognition but struggles with inconsistent naming
AI needs clear context windows but traditional metrics ignore them
AI benefits from shallow import chains but complexity metrics don't measure depth
- Measuring What Matters AIReady measures these 9 dimensions to give you a comprehensive picture of how well your codebase is optimized for AI assistance. Each dimension is scored independently, and the overall AI-Readiness Score combines them into a single metric.
# Run AIReady analysis
npx @aiready/cli scan . --score
# Output:
# AI-Readiness Score: 78/100
#
# Strengths:
# ✓ Semantic Consistency: 85%
# ✓ Type Safety: 90%
#
# Areas for Improvement:
# ⚠ Context Window Efficiency: 65%
# ⚠ Import Chain Depth: 70%
- Getting Started Ready to measure your codebase's AI-readiness? Run the analysis and see how your code scores across these 9 dimensions.
# Install AIReady CLI
npm install -g @aiready/cli
# Analyze your codebase
npx @aiready/cli scan . --score
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