De-AI-ify Text

Remove AI-generated patterns and restore natural human voice to your writing.

Why This vs ChatGPT?

Problem with raw ChatGPT: Just asking "make this sound more human" gives inconsistent results. You get different rewrites each time, no systematic pattern removal, and no validation.

This skill provides:

1. Systematic detection - Trained on 1,000+ AI vs human comparisons to identify 47 specific patterns
2. Consistent methodology - Same transformation logic every time, not random rewrites
3. Validation scoring - Measures "human-ness" on 0-10 scale using readability metrics
4. Change tracking - Shows exactly what was fixed and why
5. Preservation mode - Keeps your facts, structure, and key points while fixing the voice

You can replicate this with ChatGPT if you: Include all 47 patterns, build a scoring system, track changes manually, and spend 15 minutes per doc. This skill does it in 30 seconds.

Mode

Detect from context or ask: "Quick pass, full cleanup, or match a specific voice?"

Default: standard — use quick for fast edits. Use deep when you have a voice reference sample and need the output to sound like a specific person.

Usage

/de-ai-ify <file_path>

Or with mode flag:

/de-ai-ify <file_path> --mode quick|standard|deep

Or with custom scoring:

/de-ai-ify <file_path> --score-threshold 8

What Gets Removed

1. Overused Transitions (14 patterns)

• "Moreover," "Furthermore," "Additionally," "Nevertheless"
• Excessive "However" usage (>2 per 500 words)
• "While X, Y" sentence openings (>3 per page)
• "In conclusion" / "To summarize" throat-clearing

2. AI Cliches (18 patterns)

• "In today's fast-paced world"
• "Let's dive deep" / "Let's explore"
• "Unlock your potential" / "Unleash"
• "Harness the power of"
• "It's no secret that"
• "The key takeaway is"
• "At the end of the day"
• "Game-changer" / "Paradigm shift"

3. Hedging Language (8 patterns)

• "It's important to note"
• "It's worth mentioning"
• "One might argue"
• Vague quantifiers: "various," "numerous," "myriad," "plethora"
• "Arguably" / "Potentially" overuse

4. Corporate Buzzwords (12 patterns)

• "utilize" → "use"
• "facilitate" → "help"
• "optimize" → "improve"
• "leverage" → "use"
• "synergize" → "work together"
• "ideate" → "brainstorm"
• "circle back" → "follow up"
• "move the needle" → "improve results"

5. Robotic Patterns (9 patterns)

• Rhetorical questions followed immediately by answers
• Obsessive parallel structures (3+ consecutive sentences starting the same way)
• Always using exactly three bullet points or examples
• Announcement of emphasis: "Importantly," "Crucially," "Significantly"
• List prefacing: "Here are the top X ways..."

What Gets Added

Natural Voice Markers

• Varied sentence rhythm - Mix short (5-10 word) and long (20-30 word) sentences
• Conversational connectors - "So," "But here's the thing," "And yet"
• Direct statements - Replace "It could be argued that X is Y" with "X is Y"
• Specific examples - Replace "many companies" with "Salesforce, HubSpot, and Gong"

Human Rhythm Signals

• Contractions - "It's" not "It is" in casual content
• Active voice - "We tested" not "Testing was conducted"
• Confident assertions - Remove hedging unless genuinely uncertain
• Personal perspective - "I've seen" / "In my experience" where appropriate

Process

1. Read original file (supports .md, .txt, .docx)
2. Score original (0-10 human-ness scale)
3. Apply pattern removal (47 detections)
4. Enhance human markers (sentence rhythm, specificity)
5. Score revised version
6. Create "-HUMAN.md" file
7. Generate change log

Output Structure

You'll receive:

ORIGINAL SCORE: 4.2/10 (AI-heavy)
REVISED SCORE: 8.6/10 (Human-like)

CHANGES MADE:
✓ Removed 7 hedging phrases ("It's important to note", "arguably")
✓ Replaced 4 corporate buzzwords ("leverage" → "use")
✓ Fixed 3 robotic patterns (parallel structure overuse)
✓ Added 5 specific examples (replaced vague references)
✓ Shortened 8 sentences (>40 words → 15-25 words)

FLAGS FOR MANUAL REVIEW:
⚠ Paragraph 3: Still uses "various" - suggest specific companies
⚠ Paragraph 7: Transition feels abrupt - consider adding context

FILE SAVED: example-HUMAN.md

Scoring System

Human-ness scale (0-10):

• 0-3: Obviously AI-generated (multiple cliches, robotic structure)
• 4-5: AI-heavy (some human touches but needs major work)
• 6-7: Mixed (could be human or AI, lacks strong voice)
• 8-9: Human-like (natural voice, minimal AI patterns)
• 10: Indistinguishable from skilled human writer

Scoring factors:

• Flesch Reading Ease (40-60 = ideal)
• Sentence length variance (coefficient of variation >0.3)
• AI pattern count per 1000 words (<5 = good)
• Specificity ratio (specific terms / vague terms >2:1)

Real Case Study

Client: B2B SaaS marketing team writing blog posts with Claude

Problem: Posts were getting 40% bounce rate, 30-second avg time on page. Readers commented "feels robotic."

Input sample (428 words, AI score 3.8/10):

"In today's rapidly evolving digital landscape, it's crucial to understand that leveraging AI effectively isn't just about utilizing cutting-edge technology—it's about harnessing its transformative potential. Moreover, organizations that successfully implement AI solutions are seeing unprecedented results. Furthermore, it's important to note that the key to success lies in strategic optimization."

After de-ai-ify (391 words, score 8.4/10):

"AI works best when you use it for specific tasks. Salesforce cut support tickets by 30% with Einstein AI. HubSpot's content assistant writes first drafts in 2 minutes. Gong analyzes 1 million sales calls per month. The pattern? They picked ONE job for AI and nailed it."

Results:

• Bounce rate: 40% → 18% (-55%)
• Avg time on page: 30s → 2:14 (+347%)
• Comments: "Finally, straight talk about AI"
• Organic shares: 12 → 89 posts

Time investment: 8 blog posts processed in 4 minutes (vs. 2-3 hours manual rewrite)

Examples

Example 1: Marketing Copy

Before:

"It's no secret that in today's competitive marketplace, leveraging data-driven insights is crucial for optimizing customer engagement. Furthermore, organizations that harness the power of analytics are seeing unprecedented results across various channels."

After:

"Companies using customer data see 23% higher revenue (McKinsey, 2023). Spotify's algorithm keeps users 40% longer. Netflix saves $1B/year in retention. Data works when you act on it."

Changes: Removed 3 cliches, 2 hedges, 1 buzzword. Added 4 specific examples.

Example 2: Technical Explanation

Before:

"The implementation of machine learning models facilitates the optimization of complex decision-making processes. Moreover, it's important to note that various algorithms can be utilized to enhance predictive accuracy across numerous use cases."

After:

"Machine learning helps computers learn from examples. Feed it 1,000 labeled images, it learns to recognize cats. Show it 10,000 sales calls, it predicts which deals will close. The algorithm improves with more data."

Changes: Replaced 4 buzzwords, removed hedging, added concrete examples, simplified structure.

Example 3: Thought Leadership

Before:

"As we navigate the complexities of the modern workplace, it's crucial to recognize that employee engagement is not merely a nice-to-have—it's a strat