Vibe Code Reviewer — Reviews AI-generated code for the mistakes that sink vibe-coded apps: exposed API keys, missing auth, SQL injection, client-trusted payments. Built for non-engineers — plain-language findings, severity ratings, copy-paste fixes. Catches the security holes behind the "45% of AI code is vulnerable" stat before you ship.
I have spent more than 25 years in programming and web development, with a strong focus on performance, security, and writing lean code that does not waste resources. Good code should be clear, fast, safe, and practical to maintain. That same mindset is what I bring to this skill.
A senior-engineer-style code review assistant: severity-ranked findings, security smell detection, and constructive feedback across 8 language families.
Compare client feedback with your latest draft text. Get a clear revision checklist with source quotes, unresolved conflicts, and version-specific approval status.
Production-grade code quality standards for AI coding agents: no-hacks policy, core values hierarchy, universal clean code principles, language-agnostic pitfalls, multi-stack security checks, and TypeScript-first depth with coverage-gap patterns. Grounded in real production failure modes, with standards and tooling spanning 19 languages.
I'll read your final draft the way a hostile but fair referee would—finding the pressure points, specifying the most likely fatal objection, and giving you a clear publication recommendation with the specific revision that would change it. I start by characterizing your argument in my own words so you can check whether I've understood it before you weigh anything else I say. Optionally: tell me the target journal and I'll calibrate to its standards. Not for your peer reviewing commitments.
Guides, a 56-item checklist, a ready PR template, and a fill-in team policy for code review culture. Includes 10 good/bad comment pairs and 30 situational review comments, unified by a blocker/suggestion/nit/question/praise severity system.
Hands-on prompt engineering and LLM evaluation. Built around a diagnose-then-ship loop: forensic transcript critique ▎ that names the exact failure mode, paired with a fixer that produces complete, deployment-ready rewrites — not ▎ vague advice.
Automated OWASP Top 10 security audit with severity ratings, CWE mappings, and remediation code.