LintMyAI

Why Lint Your AI Code

AI coding assistants like Cursor, Copilot, and Claude have changed how developers build software. They write code fast, handle boilerplate, and can scaffold entire features in minutes. But speed without quality creates a different kind of technical debt -- one that's harder to spot because the code looks right.

The AI Code Quality Problem

AI-generated code compiles. It runs. It often passes basic tests. But it has patterns that experienced developers would catch in code review:

  • Hardcoded secrets sitting in source files instead of environment variables
  • Async functions with no await -- marked async out of habit, wrapping return values in unnecessary Promises
  • TODO placeholders that look like real implementations but do nothing
  • Fake error handling -- catch blocks that swallow errors silently
  • Hallucinated packages -- import statements referencing npm packages that don't exist

These aren't edge cases. They're systematic patterns that AI assistants produce because they optimize for "code that looks correct" rather than "code that is correct."

What Linting Catches

LintMyAI organizes its 28 rules into 7 categories, each targeting a specific class of AI-generated code issues:

  • Hallucinated Imports -- Packages that don't exist on npm, config options that aren't real
  • Security -- Hardcoded secrets, placeholder credentials, insecure defaults
  • Dead Code -- Empty implementations, unreachable catch blocks, unused parameters
  • AI Behavior -- Fake async, excessive comments, hedging comments ("you may want to adjust this")
  • Boilerplate -- Over-abstraction, verbose conditionals that could be simplified
  • Complexity -- Functions that are too long, too deeply nested, or too complex
  • Testing -- Missing test files for source modules
  • Framework -- React hooks violations, Vue patterns, Express middleware issues

Each category represents a pattern that AI assistants get wrong more frequently than human developers.

Better Foundation Means Better AI Output

Here's the insight that makes linting AI code uniquely valuable: AI coding assistants work better on clean codebases.

When you ask an AI to add a feature to a project full of dead code, placeholder implementations, and hardcoded secrets, it learns from those patterns. It produces more of the same. The quality degrades with each iteration.

When you lint AI-generated code and fix the issues, you create a cleaner foundation. The next time an AI assistant works on your project, it has better context. Better context means better output. Better output means faster iteration.

Linting isn't just about catching bugs -- it's about maintaining the quality baseline that makes AI-assisted development actually productive over time.

Zero Config, One Command

The barrier to linting AI code should be as low as the barrier to generating it. That's why LintMyAI works with a single command:

npx lintmyai .

No configuration files. No plugin setup. No framework-specific instructions. It detects your project type, loads the right rules, and reports issues. If you want to customize, you can -- but you don't have to.

AI Is Great. Linting Makes It Better.

This isn't about replacing AI coding assistants or slowing down development. AI tools are genuinely productive, and the speed they provide is real. Linting is the quality check that makes that speed sustainable.

Think of it like spell-check for AI-generated code. You don't stop writing because spell-check exists -- you write faster because you know it's there to catch mistakes.

Get started in 60 seconds or browse the full rule list to see what LintMyAI catches.