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.
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.
Review an AI Skill before installation or publication. Get a concrete verdict with evidence, permissions, secret risks, license constraints, and the safest next step.
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.
Turn product ideas into implementation-ready plans before your AI agent writes a single line of code. AI Development Director produces structured requirements, architecture decisions, database schemas, implementation tasks, coding-agent prompts, review checklists, and launch checklists. This is not a code-generation prompt. It is the planning system that runs before code generation.
I've reviewed hundreds of codebases and PRs across Python, JavaScript, C#, TypeScript, Go, Rust, and more. I've distilled that debugging and code review judgment into this skill to help you find bugs, spot security vulnerabilities, and improve code quality before merging.
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.
Literature Review from affaan-m’s ECC project. Use it to plan, run, and document academic or technical literature reviews, from research-question framing and database search to deduplication, screening, evidence extraction, thematic synthesis, gap finding, and citation verification. Supports narrative, scoping, systematic reviews, and meta-analysis workflows.
Expert in end-to-end AI-assisted software delivery, from design engineering and design systems to shipping: design engineering, design system automation, technical documentation, spec-driven planning, polyglot code quality, systematic QA, release auditing, LLM context engineering, and Astro frontend optimization. Built for agent-agnostic workflows across the full 9-skill delivery lifecycle.