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learn-ac-evaluate

Evaluate AI-generated code for correctness, security, performance, and maintainability.

Updated Mar 12, 2026
Search Tags:#edge cases#maintainability#issue tracking#security review#performance analysis#code validation#ai code review
One-click install
npx skills add https://github.com/novel-jp/projsight-plugin --skill learn-ac-evaluate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: learn-ac-evaluate
Source: https://github.com/novel-jp/projsight-plugin/tree/main/skills/learn-ac-evaluate
Command: npx skills add https://github.com/novel-jp/projsight-plugin --skill learn-ac-evaluate

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you critically review AI-generated code instead of trusting it blindly. It focuses on catching hidden correctness bugs, security risks, performance traps, and maintainability issues before they reach production.

Core Features & Use Cases

  • Guides a structured review across correctness, security, performance, and maintainability.
  • Highlights common AI mistakes such as missing edge cases, unsafe regular expressions, unnecessary work, and unclear constants.
  • Useful when evaluating generated validators, small utility functions, or any snippet that looks plausible but needs a careful second pass.
  • Encourages recording findings as issues and verifying fixes through diffs so review results become actionable.

Quick Start

Ask the skill to review an AI-generated code snippet and summarize the correctness, security, performance, and maintainability issues it finds.

Frequently Asked Questions about learn-ac-evaluate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I review AI-generated code for hidden security and correctness bugs?▼

To review AI-generated code for hidden bugs, systematically inspect null handling, boundary conditions, and ReDoS exposure. This process validates correctness and security before generated snippets reach production by highlighting missing edge cases and unsafe implementations.

What common maintainability issues appear in generated utility functions?▼

Common maintainability issues in generated utility functions include magic numbers and unclear constants. A structured code validation process identifies these risks alongside unnecessary work, ensuring generated snippets remain readable and safe for long-term maintenance.

How do I check AI code for ReDoS exposure and boundary condition flaws?▼

Checking AI code for ReDoS exposure and boundary condition flaws requires evaluating unsafe regular expressions and edge case handling. This systematic inspection catches hidden correctness bugs and security risks that often appear in ambiguous prompt outputs.

Can I track code validation findings as issues and verify fixes through diffs?▼

Yes, you can track code validation findings as issues and verify fixes through diffs. This approach turns performance analysis and security review results into actionable items, ensuring maintainability risks in generated snippets are resolved correctly.

When do I need a systematic code review for ambiguous prompt outputs?▼

You need a systematic code review for ambiguous prompt outputs when evaluating generated validators or small utility functions. This careful second pass catches implementation flaws, performance traps, and missing edge cases that look plausible but require validation.

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