Analyze codebases for T-stage reviews using LLM-as-Judge and output structured JSON.
npx skills add https://github.com/isaenter/UES-PRO --skill code-review-isaenter
Please help me install this Agent Skill. Skill: code-review Source: https://github.com/isaenter/UES-PRO/tree/main/frontend-vben/.claude/skills/code-review Command: npx skills add https://github.com/isaenter/UES-PRO --skill code-review-isaenter
T 阶段代码审查 — 含 LLM-as-Judge 模式，帮助团队自动化、统一化地评估代码质量、合规性与安全性，减少人工偏差。
Run the skill on a codebase to generate a JSON report in the LLM-as-Judge format.
High-intent search queries and answers about installing and using this skill.
Automate code review by running an LLM-as-Judge skill on your codebase to evaluate spec compliance, testing coverage, boundary handling, and security. It outputs a structured JSON report containing severity level, identified issues, and a summary for consistent quality assurance.
The LLM-as-Judge pattern applies large language models to evaluate code quality, security, and spec compliance against a predefined rubric. This approach provides explainable review decisions and structured outputs, reducing human bias in T-stage code assessments.
Yes, you can integrate automated code review results into CI/CD pipelines. The skill generates delivery-ready structured JSON with predefined fields like level, issues, and summary, making it straightforward to parse and act upon in automated workflows.
Unlike traditional static analysis tools that rely on fixed rules, LLM-as-Judge evaluates codebases holistically for spec compliance, boundary handling, and readability. It provides explainable assessments validated against a predefined rubric, reducing manual bias across reviews.
No specific configuration is needed. The checklist-driven review automatically assesses spec compliance, TDD coverage, boundary and error path handling, data security, and code readability across typical projects without requiring manual checklist setup.
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