Documentation
Make your codebase AI-ready with our suite of analysis tools
🚀 Quick Start
Get started in seconds with zero configuration:
Installation
You can use AIReady tools without installation via npx, or install globally for faster runs:
⏩ Next Steps
Once installed, initialize your project to save your settings:
Use with AI Agent
Prefer using AI agents like Cline, Cursor, or GitHub Copilot? Copy these ready-to-use prompts.
AI Readiness Scoring
📊 One Number, Complete Picture
Get a unified 0-100 score combining tools with proven default weights:
Default Weights
- Pattern Detection: 40%
- Context Analysis: 35%
- Consistency: 25%
Rating Scale
- 90-100 Excellent
- 75-89 Good
- 60-74 Fair
- 40-59 Needs Work
- 0-39 Critical
🎯 Customizable Weights
Adjust weights to match your team's priorities:
Understanding Metrics
🧩 Knowledge Concentration
Measures "bus factor" for AI training. High concentration means AI only learns from one person's patterns.
📉 Tech Debt Interest
Calculates the "interest" paid every time an AI assistant uses a confusing pattern.
Measure what matters to your team
Need a custom metric for data quality, pipeline standards, or security?
Unified CLI
$ npx @aiready/cli scan ./src --score
🔍 Scanning 47 files...
🛡️ Pattern Detection: 85/100
📈 Context Analysis: 72/100
⚡ Consistency: 91/100
✨ AI Readiness Score: 82/100 (GOOD)
Initialization & Persistence
While AIReady works with zero configuration, serious teams use an aiready.json file to ensure consistent rules across the entire team and CI/CD pipelines.
✨ Easy Setup
Generate a configuration file with smart defaults based on your project structure:
🛠️ Full Fine-Tuning
Need to tweak every single detail? Generate a "full" template with every available parameter documented.
Need an Expert Audit?
While our tools give you the data, our experts provide the strategy. Get a comprehensive AI Readiness Audit for your enterprise codebase.
Professional AI Readiness Audit
Request a comprehensive strategy session and codebase audit for your team.
What you'll get:
- ✓ Semantic duplicate report
- ✓ Context window optimization
- ✓ Naming consistency analysis
- ✓ Actionable AI adoption roadmap
We'll never share your data. We'll email from the address on the site footer.
Prefer direct contact?
Email us: [email protected]CLI Options
--scoreCalculate AI Readiness Score--jsonOutput report in JSON format--includeGlob patterns to include--excludeGlob patterns to exclude--thresholdSet similarity threshold (0-1)--verboseShow detailed outputAnalysis Tools
Pattern Detection
@aiready/pattern-detectFind semantic duplicates that look different but do the same thing
✨ Features
- ✓Semantic detection using Jaccard similarity on AST tokens
- ✓Pattern classification (API handlers, validators, utilities)
- ✓Token cost analysis showing wasted AI context budget
- ✓Auto-excludes tests and build outputs
- ✓Adaptive threshold based on codebase size
🚀 Quick Start
# Run without installation npx @aiready/pattern-detect ./src # Or use unified CLI npx @aiready/cli scan ./src
📊 Example Output
📊 Duplicate Pattern Analysis ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📁 Files analyzed: 47 ⚠️ Duplicate patterns: 12 files with 23 issues 💰 Wasted tokens: 8,450 CRITICAL (6 files) src/handlers/users.ts - 4 duplicates (1,200 tokens) src/handlers/posts.ts - 3 duplicates (950 tokens)