After scanning 200+ repos built with Claude Code, Cursor, and Copilot, we've identified two distinct types of debt:
1. Structural Debt — Scannable and Fixable
Missing CI pipeline, zero test coverage, hardcoded secrets, unmanaged dependencies. This is measurable:
- 73% of AI-built repos have no CI pipeline
- 68% have zero tests
- 41% have hardcoded secrets
These are binary checks. Either CI exists or it doesn't. Either secrets are in env vars or they're hardcoded. A scanner can catch all of this.
2. Comprehension Debt — Harder to Measure
Code that works but nobody understands because nobody wrote it with intent. AI generates 200 lines where 40 would do. The abstractions are illogical. The reviewer's eyes glaze over.
This debt compounds because each untested module interacts with other untested modules. The failure modes multiply faster than the code volume.
The Gap
Most teams don't realize they have both types until the first production incident. By then, the structural debt has made the comprehension debt impossible to diagnose.
We built RepoFortify to catch Type 1 — the structural gaps that AI coding tools consistently skip. It checks 9 production readiness signals in seconds.
Type 2 is the next frontier. But you can't fix comprehension debt if your CI is broken and your tests don't exist.
Start with structure. The rest follows.
Free scan: repofortify.com — paste your repo URL, get a production readiness score across 9 signals. No signup required.
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