ai-pattern-detection（AI 模式检测）

AI Pattern Detection Skill

Purpose

Automatically scan content for AI-generated writing patterns and provide authentic alternatives. This skill activates when Claude generates or reviews text content, ensuring outputs maintain human-like authenticity.

When This Skill Applies

• Generating any prose, documentation, or written content
• Reviewing or editing existing documents
• User mentions "AI detection", "writing quality", "authentic voice"
• User asks to "make it sound more natural" or "less robotic"
• Creating marketing copy, documentation, or communications

Detection Categories

Critical Patterns (Always Flag)

These immediately identify content as AI-generated:

1. Corporate Buzzwords: "seamlessly integrates", "cutting-edge", "revolutionary", "next-generation", "comprehensive solution"
2. Vague Intensifiers: "dramatically improves", "significantly enhances", "vastly superior"
3. Formulaic Transitions: "Moreover,", "Furthermore,", "Additionally,", "In conclusion,"
4. Performative Language: "aims to provide", "strives to achieve", "designed to enhance"
5. Academic Passive: "It has been observed that...", "It can be argued that..."

Structural Patterns (Flag When Overused)

1. Three-item lists: "reliable, scalable, and secure"
2. Em-dash overuse: Multiple em-dashes in a paragraph
3. Identical paragraph structure: Topic → 3 points → conclusion repeated
4. Balanced hedging: "While X has challenges, it also offers opportunities"

Contextual Patterns (Check Frequency)

Words acceptable at 1:1000 ratio but problematic at 1:100:

• manifest, revolutionary, next-generation
• robust, scalable, comprehensive
• synergy, leverage, utilize

Replacement Guidelines

Authenticity Markers to Include

Strong authentic content includes:

1. Specific opinions: "I prefer X because..." not "X is preferred"
2. Acknowledged trade-offs: "This approach sacrifices Y for Z"
3. Real-world constraints: "Budget limited us to..."
4. Uncertainty where appropriate: "We're not sure yet whether..."
5. Varied sentence structure: Mix short and long, different openings
6. Domain-specific vocabulary: Use actual technical terms, not generic descriptions

Application Process

When generating or reviewing content:

1. Scan for critical banned patterns
2. Count contextual pattern frequency
3. Check structural variety
4. Suggest specific replacements
5. Verify authenticity markers present

Examples

Before (AI-Detected)

The platform seamlessly integrates cutting-edge technology to dramatically improve workflow efficiency. Moreover, it plays a crucial role in enabling next-generation solutions. In conclusion, this comprehensive approach transforms how teams collaborate.

After (Authentic)

The platform connects to existing tools through standard APIs. Initial tests show 40% faster task completion. Teams report fewer context switches between applications.

Script Reference

For automated scanning, use scripts/pattern_scanner.py which:

• Counts pattern frequencies
• Flags critical violations
• Generates replacement suggestions
• Produces authenticity score (0-100)

Integration

This skill works with:

• /writing-validator command for explicit validation
• writing-validator agent for deep analysis
• Any content generation task automatically

References

• @$AIWG_ROOT/agentic/code/addons/voice-framework/README.md — Voice framework for target style profiles
• @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/instruction-comprehension.md — Parsing content requirements accurately
• @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/README.md — SDLC framework context for documentation quality
• @$AIWG_ROOT/docs/cli-reference.md — CLI reference for writing-related commands
• @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/research-before-decision.md — Research patterns before making writing recommendations