Nothing to hand? Load the — a producer and worker with no retries, no retention, no failed listener and a password inlined in the connection — or the , where the correct verdict is production-ready and the useful output is what to add next.
Paste the code — the prescan is free
No upload, no AI: the prescan reads your source in the browser and lists what it mechanically found. The queue and worker inventory, then the flags — jobs added without attempts, retries without backoff, no removeOnComplete / removeOnFail retention, workers left at the default concurrency of 1, no graceful shutdown, no failed listener anywhere, unawaited add() calls, ioredis connections without maxRetriesPerRequest: null, repeatable jobs registered at module scope, and secret-looking literals. Each group explains why it matters. This part costs nothing and happens while you type.
The AI reviews them — this is the metered part
A senior infrastructure engineer's pass: a production-readiness posture with the single most important change named, the inventory with each construct's role, and prioritized findings across reliability, correctness, performance, operations, security and hygiene — each with the problem, the concrete impact, the fix and a corrected JS/TS fragment. Every prescan flag is confirmed or explicitly set aside. Findings may only cite queues, workers and files that actually appear in your code. Pricing is honest: a worst-case amount is reserved before the run and only what the run actually uses is charged — the meter next to the button shows both.
Fix, export, re-run
Every corrected snippet in one paste-ready block, the findings as GitHub PR review comments with ```suggestion fences you can apply from the review UI, a tickable action checklist for the ticket, the findings table as CSV, and Markdown or JSON export of the whole review. Then start fixing: the prescan re-runs as you edit and the strip above the run button counts the flags you have cleared, the ones still open and any you have just introduced — in the browser, for free, before you pay for a second review. Reviews are saved to your SkillSafe account when you are signed in, so they follow you to another machine; restore puts the code back in the form too.
Derived from the @sickn33/bullmq-specialist skill (Apache-2.0 license).