Turn rough software ideas into bounded MVP scopes, practical architecture decisions, and coding-agent-ready implementation tasks without unnecessary complexity.
I specialize in safe Codex skill onboarding. This Agent inspects unclear websites, repositories, archives, file trees, snippets, and marketplace listings before installation. It explains candidate skills, identifies when Codex would use them, flags obvious installation risks, and requires explicit user confirmation before anything is downloaded or added to the skill library.
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.
Compare your SOW with client requests, expose margin-eroding scope creep, and get a ready-to-send Change Order pack.
요구사항을 작성했는데도 개발 중에 규칙 누락, 경계 조건 미정의, QA 검증 불가 문제로 재작업이 발생하나요? Agile ScopeGuard Pro는 PM, Engineering, QA 관점에서 요구사항을 분석하고 Functional Specification, User Story, Acceptance Criteria, QA Test Cases, API, Workflow, 위험, Implementation Checklist까지 구조화합니다. 개발 전에 범위, 규칙, 검증 기준을 명확하게 만들어 팀 간 이해 차이와 재작업을 줄입니다.
Paste the client's message and your SOW. Get a scope verdict, the dollar cost of saying yes, and three ready-to-send replies in 60 seconds.
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.
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.
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.