What makes content 'authentic' according to this skill?

Authenticity markers include specific opinions, acknowledged trade-offs, real-world constraints, and varied sentence structures.

Can I use this for technical documentation?

Yes, it is specifically designed to replace vague terms like 'robust' with specific technical metrics and domain-appropriate language.

Does it automatically rewrite my content?

It can suggest specific replacements or be used with the writing-validator agent to automatically refine text into a more natural voice.

How does this skill detect AI writing?

It analyzes content for specific markers like corporate buzzwords, formulaic transitions (e.g., 'Moreover'), and repetitive structural patterns common in LLM outputs.

AI Pattern Detection & Writing Quality

Name: AI Pattern Detection & Writing Quality
Author: jmagly

byjmagly

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Content Management

Identifies and replaces robotic AI-generated writing patterns with authentic, human-like prose.

About

The AI Pattern Detection skill is a specialized tool for Claude Code designed to eliminate 'AI-isms' from technical documentation, marketing copy, and internal communications. It automatically scans text for corporate buzzwords, formulaic transitions, and repetitive structures that flag content as machine-generated. By suggesting specific, data-driven alternatives and emphasizing authenticity markers like trade-offs and domain-specific vocabulary, it ensures your outputs sound authoritative and natural rather than generic and robotic.

Key Features

Real-time scanning for critical AI-generated buzzwords and fluff phrases
Smart replacement engine that converts vague intensifiers into specific metrics
Contextual frequency analysis for overused terms like robust and scalable
67 GitHub stars
Automatic detection of repetitive structural patterns and formulaic transitions
Authenticity scoring based on domain-specific vocabulary and acknowledged trade-offs

Use Cases

Auditing existing content to improve engagement and avoid AI detection markers
Removing robotic corporate-speak from marketing copy and product announcements
Polishing technical documentation to sound more human and authoritative

About

Key Features

Real-time scanning for critical AI-generated buzzwords and fluff phrases
Smart replacement engine that converts vague intensifiers into specific metrics
Contextual frequency analysis for overused terms like robust and scalable
67 GitHub stars
Automatic detection of repetitive structural patterns and formulaic transitions
Authenticity scoring based on domain-specific vocabulary and acknowledged trade-offs

Use Cases

Auditing existing content to improve engagement and avoid AI detection markers
Removing robotic corporate-speak from marketing copy and product announcements
Polishing technical documentation to sound more human and authoritative