The Practical Guide to Tech Debt Audit (No Fluff)

Most automated code reviews are useless. They generate generic, high-level observations that look impressive in a chat window but provide zero actionable value. If you’ve ever run an LLM-based review on a legacy codebase, you know the drill: it flags "complexity" in a file you already know is a mess, offers a vague suggestion to "refactor," and leaves you with nothing to actually commit to your backlog.

If you want to stop wasting time on surface-level analysis, you need a real tech debt audit. The difference between a generic checklist and a functional audit comes down to one thing: grounding.

Most tools fail because they treat code as text to be summarized rather than a system to be understood. When I look for a way to manage technical debt, I don't want a summary of what the code does. I want to know where the architectural rot is, which files have the highest churn, and exactly which lines are causing the most friction.

The tech-debt-skill for Claude Code changes the game by forcing the model to orient itself before it ever makes a judgment. It maps the directory structure, analyzes git churn, and builds a mental model of your architecture. If the model skips this phase, the findings are just vibes. By forcing this orientation, the tool ensures that when it flags a "god class," it’s doing so because it understands the context of the surrounding services, not just because the file is long.

Here is why this approach actually works:

1. File-level citations: Every finding must include a path/to/file.ext:LINE reference. If a finding isn't falsifiable, it isn't worth your time.
2. The "Looks Bad But Is Fine" section: This is the most important part of the audit. It forces the model to justify why it didn't flag certain patterns. This prevents the "checklist regurgitation" that plagues most automated tools.
3. Persistent artifacts: You get a TECH_DEBT_AUDIT.md file. You can commit this to your repo, track it over time, and turn it into a living document that evolves as you pay down debt.

Here’s where most people get tripped up: they try to run an audit on a massive, 100k+ LOC repo in one go. Even the best models will lose the thread. If you’re working in a large monorepo, use the sub-agent dispatch feature to audit specific subtrees. It’s better to have five focused, accurate audits than one massive, hallucination-prone report.

You should also be wary of the "recommendation trap." A good audit identifies the problem; it doesn't blindly suggest a rewrite. If your tool is constantly telling you to rewrite entire modules, it’s not auditing your debt—it’s just generating noise. Look for tools that prioritize quick wins, like removing unused dependencies or cleaning up specific error-handling blocks, rather than those that suggest massive architectural overhauls.

How do you know if your current process is failing? If you can't point to a specific file and line number that represents your biggest bottleneck, you aren't managing debt—you're just guessing. Try running a proper tech debt audit on your most problematic service today and see if the findings actually align with the pain you feel during your daily development cycle. Share what you find in the comments—I’m curious to see if your "looks bad but is fine" section reveals as much as mine did.