I've been building with AI coding tools for the past year and kept noticing the same patterns in the code they generate: API routes with zero error handling, catch blocks that just do `catch (e) {}`, database queries inside loops, imports of packages that don't exist on npm, no rate limiting anywhere.
None of it breaks locally. Tests pass, types check, everything looks fine. But these are the kinds of things that blow up in production, and AI assistants produce them consistently.
So I built prodlint. It's a linter tuned specifically for patterns AI gets wrong. `npx prodlint` in any JS/TS project gives you a 0-100 score across security, reliability, performance, and "AI quality" (stuff like TODO placeholders, hallucinated imports, inconsistent naming from copy-pasting different AI outputs).
27 rules right now. Some examples:
- SQL injection via string concatenation (AI loves template literals in queries)
- dangerouslySetInnerHTML without sanitization
- Packages that don't actually exist on npm (AI hallucinates names constantly)
- .findMany() with no LIMIT
- Missing auth on API routes
Also runs as an MCP server (`npx prodlint-mcp`) so your AI editor can use it while writing code. And there's a GitHub Action that posts scores on PRs.
I ran it on my own production app and it flagged 8 critical issues across 175 files that I'd missed during review.
175 files in ~600ms, zero config, 22kb on npm. MIT licensed.
What production issues do you keep seeing in AI-generated code? Always looking for new rules to add.
I've been building with AI coding tools for the past year and kept noticing the same patterns in the code they generate: API routes with zero error handling, catch blocks that just do `catch (e) {}`, database queries inside loops, imports of packages that don't exist on npm, no rate limiting anywhere.
None of it breaks locally. Tests pass, types check, everything looks fine. But these are the kinds of things that blow up in production, and AI assistants produce them consistently.
So I built prodlint. It's a linter tuned specifically for patterns AI gets wrong. `npx prodlint` in any JS/TS project gives you a 0-100 score across security, reliability, performance, and "AI quality" (stuff like TODO placeholders, hallucinated imports, inconsistent naming from copy-pasting different AI outputs).
27 rules right now. Some examples:
- SQL injection via string concatenation (AI loves template literals in queries) - dangerouslySetInnerHTML without sanitization - Packages that don't actually exist on npm (AI hallucinates names constantly) - .findMany() with no LIMIT - Missing auth on API routes
Also runs as an MCP server (`npx prodlint-mcp`) so your AI editor can use it while writing code. And there's a GitHub Action that posts scores on PRs.
I ran it on my own production app and it flagged 8 critical issues across 175 files that I'd missed during review.
175 files in ~600ms, zero config, 22kb on npm. MIT licensed.
What production issues do you keep seeing in AI-generated code? Always looking for new rules to add.
https://prodlint.com