Codebase Readiness
Most teams struggling with AI agents have a codebase problem, not a model problem.
A codebase readiness assessment evaluates how well your repository supports AI-assisted development. It measures test coverage, architecture clarity, type safety, feedback loops, and five other dimensions that determine whether AI agents produce useful output or compound drift.
AI agents replicate whatever patterns already exist in your codebase, good or bad. Weak test coverage, unclear architecture, missing type safety, no feedback loops. These aren’t minor inconveniences. They’re the reason agents produce drift instead of value.
This free assessment scores your repo across the 8 dimensions that matter most for AI-assisted development and gives you a prioritized roadmap to close the gaps.
Run It in 60 Seconds
The assessment runs inside Claude Code as a plugin. Install it, run it, get your score.
Requires Claude Code. The assessment runs locally against your repo. Nothing is sent externally.
What It Measures
Eight dimensions benchmarked against teams shipping 1,000+ AI-generated PRs per week. Each dimension gets a weighted score contributing to your overall rating (0-100).
Test Foundation
Coverage, speed, reliability. Agents need fast, trustworthy feedback to know if their changes work. Without it, they guess.
Architecture Clarity
Layering, dependency direction, separation of concerns. Agents replicate existing patterns. If boundaries are fuzzy, agent output will be too.
Type Safety
Static analysis, type coverage, schema validation. Types constrain the solution space. Less ambiguity means fewer wrong turns.
Feedback Loops
Linting, CI speed, error messages. Agents learn from error output. Poor error messages mean poor self-correction.
Documentation as Code
CLAUDE.md quality, architecture docs, inline context. This is how agents understand intent. Missing docs means agents infer intent from code alone.
Dependency Health
Outdated packages, security vulnerabilities, lockfile hygiene. Stale dependencies introduce unpredictable behavior agents can't reason about.
Development Environment
Setup scripts, containerization, reproducibility. Agents need a consistent environment. 'Works on my machine' doesn't scale to agents.
Code Consistency
Formatting, naming conventions, structural patterns. Consistency is what makes agent output feel like it belongs in your codebase.
What You Get
- A score from 0-100 with a band rating (Agent-Ready, Strong, Developing, Foundation)
- Per-dimension breakdown showing exactly where you're strong and where the gaps are
- A prioritized improvement roadmap ordered by impact, not effort
- Specific, actionable recommendations tied to your actual codebase
Who This Is For
- Engineering leads evaluating whether their codebase is ready for AI-assisted development at scale
- Teams that tried Claude Code or Copilot and got inconsistent results they couldn’t explain
- Anyone about to invest in AI tooling who wants to know what to fix first
- Teams already using AI agents who want to improve output quality systematically
After the Assessment
Work Through It Yourself
The roadmap tells you exactly what to fix and in what order. For documentation gaps, the companion agent-ready plugin scaffolds CLAUDE.md, ARCHITECTURE.md, and a docs/ structure automatically. If your team has the bandwidth, you can close most gaps on your own.
Run the AssessmentAI Workflow Enablement
From $8k
A structured 3-8 week engagement where I work with your team to close the gaps the assessment surfaces. Custom CLAUDE.md systems, shared skills, workshops built on your actual codebase. You get a team that ships confidently with AI, not just a few power users.
Related Writing
Context on why codebase readiness matters and how it connects to AI-assisted development.
How I Use Claude Code: My Complete Development Workflow
The full workflow after a year of daily use. How codebase structure directly impacts agent output quality.
Build Efficient MCP Servers: Three Design Principles
Token efficiency, clear boundaries, structured output. Design principles that apply to agent-ready codebases too.
MCPs vs Agent Skills
Understanding the architecture decisions that shape how agents interact with your codebase.
Frequently Asked Questions
What is a codebase readiness assessment?
A codebase readiness assessment evaluates how well your repository supports AI-assisted development. It scores your repo across 8 dimensions (test foundation, architecture clarity, type safety, feedback loops, documentation, dependency health, dev environment, and code consistency) and produces a prioritized improvement roadmap.
How do I know if my codebase is ready for AI agents?
Run the assessment. You'll get a score from 0-100 with a band rating: Agent-Ready (80+), Strong (60-79), Developing (40-59), or Foundation (below 40). The per-dimension breakdown shows exactly where your strengths and gaps are.
What does the assessment actually check?
Eight dimensions benchmarked against teams shipping 1,000+ AI-generated PRs per week: test coverage and speed, architecture layering, type safety, CI and linting feedback loops, CLAUDE.md and documentation quality, dependency health, development environment reproducibility, and code formatting consistency.
Does the assessment send my code anywhere?
No. The assessment runs entirely inside Claude Code on your local machine. Your code never leaves your environment.
Do I need Claude Code to run it?
Yes. The assessment is a Claude Code plugin. You need Claude Code installed and running to use it.
What if my score is low?
That's the point of running it. The assessment produces a prioritized improvement roadmap ordered by impact. Most teams can close the highest-impact gaps on their own. For teams that want hands-on help, the AI Workflow Enablement program works through these gaps on your actual codebase.
Not Sure Where to Start?
Run the assessment first. If the results raise questions or you want help prioritizing, book a free intro call. We'll look at your score together and figure out the right next step.
No pitch. No pressure. Just a conversation about what the numbers mean for your team.
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