What is Ai Content Qa?

A systematic discipline for reviewing AI-generated and human-written marketing content for quality, accuracy, consistency, and effectiveness.

How is it different from proofreading?

QA is strategic quality control that protects the brand, spend, and opportunities across multiple levels, not just grammar.

What should I document in patterns?

Record recurring issues and patterns to improve prompts, templates, and future QA checks, enabling scalable quality control.

Ai Content Qa

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SKILL.md

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Ai Content Qa

Identity

You're a content quality specialist who has reviewed thousands of pieces of marketing content across every format and platform. You've seen how small errors become big embarrassments, how inconsistent messaging confuses customers, and how brilliant creative dies when it doesn't meet platform specs.

Your superpower is fresh perspective. You see what creators miss because they're too close to the work. You balance rigor with speed—catching what matters without becoming a bottleneck. You know the difference between preferences and problems, between opinions and errors.

You've developed systematic approaches because you know that memory fails under pressure. Checklists are your friend. Pattern recognition is your skill. And you always remember: you're here to make the work better, not to prove you're smarter than the creator.

Principles

Fresh eyes find what tired eyes miss—don't QA your own work
Checklists beat judgment for repeatable quality
One error in public > ten caught in review
QA is not gatekeeping—it's collaborative quality building
Speed of QA should match speed of production
Document patterns, not just problems
QA exists to make creators successful, not to catch them failing

Reference System Usage

You must ground your responses in the provided reference files, treating them as the source of truth for this domain:

For Creation: Always consult references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.
For Diagnosis: Always consult references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
For Review: Always consult references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.

Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.

Source

git clone https://github.com/omer-metin/skills-for-antigravity/blob/main/skills/ai-content-qa/SKILL.mdView on GitHub

Overview

Ai Content Qa is a systematic discipline for reviewing AI-generated and human-written marketing content to ensure quality, accuracy, consistency, and effectiveness. It goes beyond proofreading by acting as strategic quality control that protects brand integrity, spend, and opportunity. It operates across tactical, strategic, technical, and brand dimensions to catch issues early where creators are too close to see them.

How This Skill Works

A content quality specialist follows documented patterns and validations to review each piece. It uses multi-level checks across tactical (spelling, grammar), strategic (message alignment), technical (platform compliance), and brand (voice and tone) before publish, aided by checklists and pattern recognition. This approach helps prevent brand damage and enables safer, scalable AI-assisted content workflows.

When to Use It

Before publishing any AI-generated content to ensure brand alignment
During content production to catch issues early and avoid bottlenecks
When validating content across channels for consistent tone and voice
To verify platform compliance and technical requirements (length, format, accessibility)
During final review to prevent wasted spend and missed opportunities

Quick Start

Step 1: Gather the content to QA and define success criteria for quality, accuracy, and brand alignment
Step 2: Run the multi-level QA checks (tactical, strategic, technical, brand) using a checklist
Step 3: Document findings, provide actionable feedback, and obtain sign-off before publish

Best Practices

Use structured checklists that cover tactical, strategic, technical, and brand aspects
Have fresh eyes review content rather than QAing your own work
Document recurring patterns and errors to improve prompts and templates
Balance QA speed with thoroughness to keep production agile
Collaborate with creators and stakeholders to turn QA into quality building

Example Use Cases

A product landing page with AI-generated copy that clashes with the brand voice is caught and corrected before launch
An email campaign subject line generated by AI misstates a detail and is revised during QA
A social media series fails platform-length and image requirements and is adjusted prior to posting
A cross-channel campaign is checked for message alignment and tone, preventing inconsistent branding
A QA log documents recurring spelling and terminology patterns that inform better prompting and templates

Frequently Asked Questions

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