curation

Validate AI-generated code and content against human-centric quality checklists.

Updated Feb 10, 2026

Search Tags:#code review #refinement #checklist #curation #ai quality

Ready to run

npx skills add https://github.com/0r0loo/my-claude-code-toolkit --skill curation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: curation
Source: https://github.com/0r0loo/my-claude-code-toolkit/tree/main/.claude/skills/Curation
Command: npx skills add https://github.com/0r0loo/my-claude-code-toolkit --skill curation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the gap between an AI's 80% accurate output and the desired 100% quality, by providing a human-centric checklist for reviewing AI-generated code and content.

Core Features & Use Cases

Code Quality Assurance: Ensures AI-generated code is natural, practical, and consistent with existing project standards.
AI Output Refinement: Helps identify and correct AI "quirks" like excessive comments, over-abstraction, or hardcoded values.
Use Case: After an AI generates a new feature implementation, use this Skill's checklist to review the code for adherence to project conventions, natural language, and practical edge-case handling before merging.

Quick Start

Review the AI-generated code using the curation skill checklist to ensure it aligns with project standards and natural coding practices.

Frequently Asked Questions about curation

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

FAQPage Schema

How do I review AI-generated code to ensure it meets project conventions?▼

Review AI-generated code using a human-centric checklist to validate naturalness, practicality, and consistency. This refines AI output by checking adherence to criteria for comments, variable naming, error handling, abstraction levels, and import validation.

What is the best way to fix AI code quirks like excessive comments and over-abstraction?▼

Fixing AI code quirks requires a refinement checklist to identify and correct excessive comments, over-abstraction, or hardcoded values. This process bridges the gap between AI capabilities and human quality expectations for code and content.

When do I need a checklist for AI output refinement?▼

You need a checklist for AI output refinement when bridging the gap between an AI's 80% accurate output and the desired 100% quality. It validates AI-generated code and content for practical edge-case handling before merging.

Does this code review checklist validate import statements and error handling?▼

Yes, the code review checklist validates import statements and error handling. It requires adherence to specific criteria for comments, variable naming, abstraction levels, and import validation to ensure natural and practical code.

Can I use this curation checklist for content refinement as well as code review?▼

Yes, you can use this curation checklist for content refinement as well as code review. It provides a human-centric validation process applicable to both code and content to ensure naturalness and consistency.

Why does AI-generated code often fail to match existing project standards?▼

AI-generated code fails to match existing project standards due to gaps in naturalness, practicality, and consistency. A human-centric review checklist enforces specific criteria for variable naming and abstraction to achieve 100% quality.