Enji GuardKeep AI-generated code at a quality you can ship
Speed is the easy part. Guard watches whether AI-generated code stays readable, consistent, testable, and safe to change after the first version works.
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01
Quality shows up on the next change
AI can produce code that runs today and still make tomorrow's change harder. The first version looks fine. The quality question is whether another engineer, or another agent, can understand the path, make a change, and trust the result.
02
Code health signals Guard tracks
Recurring audits measure the maintainability signals that drift as AI ships:
- Duplicated logic and near-identical modules doing the same job
- Complexity hotspots that resist safe change
- Dead code and abandoned paths
- Tests that are green but do not prove critical behavior
- Inconsistent patterns that teach future agents the wrong shape
03
Measured as the repo changes
AI code quality is not a one-time score. Guard re-measures code health on a schedule and as the repo changes, so teams can see whether the codebase is becoming easier or harder to move through.
04
From quality signal to GitHub work
Findings come back as reviewable GitHub issues with evidence and rationale. When a fix is bounded and useful, Guard can open a narrow pull request that improves code health for human review.
Quick questions
How do you actually measure AI-generated code quality?
Not as a single grade. Guard tracks maintainability signals, duplication, complexity hotspots, dead code, weak tests, and inconsistent patterns, and watches whether they trend better or worse over time.
Is this just a linter or a static-analysis tool?
No. A linter flags style and known rule violations line by line. Guard looks at whether the codebase stays changeable: duplication, drift, and code smells that no fixed ruleset catches.
Can I improve code quality without a big rewrite?
Yes. Guard surfaces quality issues that slow change and turns the safe ones into small, bounded fixes. Broader design problems stay as issues or plans instead of becoming surprise refactors.
What is the difference between code quality and code health?
We use them together. Quality is whether a change is safe to make; code health is the set of signals, duplication, complexity, and tests, that tell you whether it still is.
See your AI code's real quality.
Connect a repo and get your first code-health report in a few minutes. No card.