Humanizer

/humanizer — Rewrite Existing Text to Remove AI Patterns

Note: The humanizer rules are ALWAYS ON via .claude/rules/writing-style.md. Every external draft is written with these patterns in mind automatically. This /humanizer command exists as a manual tool for rewriting text that was already written — by another AI, pasted from somewhere, or from an earlier draft that slipped through.

Think of it this way: the rule file is DNA (always present in every cell). This skill is a tool (you pick it up when you need it for a specific job).

Voice DNA

Customize this section per project. Replace the descriptors below with ones that match the founder/team's natural speaking style. These are strong defaults for most founder-led projects.

Before touching patterns, understand the voice we're targeting. The builder writes like they talk: direct, confident, specific. A founder selling a real product to real people. The writing should sound like a sharp person who knows their domain, not a marketing department.

Voice descriptors:

• Direct. Say what you mean. No warm-up paragraphs.
• Confident but not arrogant. State facts. Don't inflate them.
• Specific. Name the product, the number, the person, the date. Vague = weak.
• Conversational. Write like you'd talk to a smart colleague over coffee.
• Short sentences are fine. Fragments too. Vary the rhythm.
• First person when appropriate. "I" and "we" are not unprofessional.
• Acknowledge complexity. Real humans have mixed feelings. Show that.

Banned phrases (customize per project):

• "Unlock", "Supercharge", "Revolutionize", "Game-changing", "Cutting-edge"
• "We're excited to announce", "We're thrilled to share"
• "In today's rapidly evolving landscape"
• "At the intersection of X and Y"
• "Leveraging AI" (just describe what it does)
• "Seamless", "Robust", "Scalable" (without specific evidence)
• "World-class", "Best-in-class", "Industry-leading"
• "Empowering", "Transformative", "Disruptive"
• "Passionate about [industry]" (show it, don't say it)

The 25 AI Writing Patterns

Detect and fix all of the following. Based on Wikipedia's "Signs of AI Writing" guide.

Content Patterns

1. Significance inflation. Words like "testament", "pivotal moment", "vital role", "indelible mark", "setting the stage", "deeply rooted." AI puffs up importance. Cut it. State the fact.

• Before: "This marks a pivotal moment in the evolution of modern analytics."
• After: "We shipped cross-source data aggregation. Users can now query both providers in one search."

2. Notability inflation. Listing media coverage or follower counts without context. If you cite coverage, say what was said, not just where.

• Before: "Featured in multiple industry publications."
• After: "TechReview covered our aggregation approach in their March issue, calling it 'the first practical implementation.'"

3. Superficial -ing analyses. Tacking "-ing" phrases onto sentences for fake depth: "highlighting", "underscoring", "symbolizing", "showcasing", "fostering", "ensuring."

• Before: "The platform aggregates data from multiple sources, showcasing how integration enhances transparency."
• After: "The platform pulls data from multiple sources into one view."

4. Promotional language. "Groundbreaking", "vibrant", "stunning", "breathtaking", "nestled", "boasts", "renowned." Describe what the thing does, not how impressive it is.

• Before: "The product boasts a groundbreaking approach to data management."
• After: "The product pulls data from multiple providers into one searchable database."

5. Vague attributions. "Experts say", "industry observers note", "some critics argue." Name the person or drop the claim.

• Before: "Industry experts agree that data fragmentation is the biggest challenge."
• After: "Our lead engineer, who spent 10 years in the space, calls data fragmentation the core problem."

6. Formulaic "challenges and future" sections. "Despite challenges... continues to thrive." "The future looks bright." State what's actually happening.

• Before: "Despite challenges in data standardization, the company continues to thrive."
• After: "Data formats vary by provider. We normalize them into a common schema."

Language and Grammar Patterns

7. Overused AI vocabulary. Additionally, crucial, delve, enduring, enhance, fostering, garner, highlight (verb), interplay, intricate, landscape (abstract), pivotal, showcase, tapestry (abstract), testament, underscore, valuable, vibrant. Replace with simpler words or cut entirely.

8. Copula avoidance. "Serves as", "stands as", "functions as", "boasts", "features", "offers." Just use "is", "are", "has."

• Before: "The product serves as a single source of truth for operational data."
• After: "The product is a single source of truth for operational data."

9. Negative parallelisms. "It's not just X, it's Y." "Not only... but also..." Overused. Just state the point.

• Before: "It's not just a database. It's a decision-making tool for operators."
• After: "Operators use it to compare options before committing."

10. Rule of three. AI forces ideas into groups of three. Real people don't always think in threes.

• Before: "Speed, accuracy, and transparency."
• After: "It's fast and the data is traceable."

11. Synonym cycling. Using different words for the same thing to avoid repetition: "the platform", "the tool", "the system", "the solution." Pick one and stick with it.

• Before: "The platform aggregates data. The tool normalizes formats. The system resolves conflicts."
• After: "The product aggregates data, normalizes formats, and resolves conflicts across sources."

12. False ranges. "From X to Y" constructions where X and Y aren't on a meaningful scale.

• Before: "From small startups to global enterprises, from simple to complex."
• After: "We work with companies at different scales. Most are mid-market."

Style Patterns

13. Em dash overuse. AI loves em dashes. Use commas, periods, or parentheses instead. One em dash per piece of writing maximum.

14. Excessive bold. Don't bold every key phrase. If everything is emphasized, nothing is.

15. Inline-header vertical lists. Bullet points starting with "Header: description" is an AI tell. Convert to flowing prose or simple bullets without bold headers.

16. Title Case in headings. Use sentence case. "Strategic partnerships" not "Strategic Partnerships."

17. Emojis. Never. Not in emails, not in LinkedIn posts, not anywhere in external communications.

18. Curly quotes. Use straight quotes ("like this") not curly quotes.

Communication Patterns

19. Chatbot artifacts. "I hope this helps!", "Let me know if you'd like me to expand", "Great question!", "Here is an overview of..." Delete these entirely.

20. Knowledge-cutoff disclaimers. "As of my last update", "While specific details are limited..." If you don't know something, say so plainly or don't mention it.

21. Sycophantic tone. "Great question!", "You're absolutely right!", "That's an excellent point!" React to content, not to the person asking.

22. Filler phrases. "In order to" -> "To." "Due to the fact that" -> "Because." "At this point in time" -> "Now." "It is important to note that" -> cut it.

23. Excessive hedging. "It could potentially possibly be argued that it might..." -> State it or don't.

24. Generic positive conclusions. "The future looks bright." "Exciting times ahead." End with something specific or don't end with an outlook at all.

25. Hyphenated word pair overuse. AI hyphenates with perfect consistency. Humans are inconsistent. For common compounds (cross functional, high quality, data driven), drop the hyphen unless it genuinely aids clarity.

Process

When the user says /humanizer or asks to humanize a draft:

1. Read the input text.
2. Identify every instance of the 25 patterns above.
3. Rewrite. Fix all detected patterns. Apply the voice DNA. Keep the core message.
4. Anti-AI audit. Ask yourself: "What still makes this sound like AI wrote it?" Be honest. Look for:
   • Uniform sentence length
   • Soulless neutral reporting (no opinion, no perspective)
   • Overly tidy structure
   • Missing first-person voice where it would be natural
5. Second rewrite. Fix whatever the audit caught.
6. Present the final version with a brief summary of what changed.

Output Format

## Rewrite

[The humanized text]

## What changed
- [Brief bullets listing the patterns that were fixed]

If the original text is clean, say so. Don't rewrite for the sake of rewriting.

Context-Specific Guidelines

Emails to partners / stakeholders

• Be direct. Peers are peers, not customers.
• Skip pleasantries beyond a one-line opener. Get to the point.
• End with a clear ask or next step, not a vague sign-off.

LinkedIn posts

• Hook in the first line. LinkedIn truncates after ~150 characters.
• Write like you're telling a friend what happened. Not like you're writing a press release.
• One idea per post. Don't try to cover everything.
• No hashtag spam. Two or three max, at the end.

Pitch materials (investors, partners, clients)

• Lead with the problem, not the solution.
• Use real numbers. "2,000 customers across 3 markets" beats "rapid growth."
• Name names. Specificity builds trust.
• Acknowledge what doesn't exist yet. Honesty about roadmap beats vaporware.

Investor updates

• Lead with metrics, then narrative. Numbers first, story second.
• Bad news early, not buried. Investors respect candor.
• End with a specific ask if you have one. Don't waste the touchpoint.

Reference

Patterns sourced from Wikipedia: Signs of AI writing, maintained by WikiProject AI Cleanup. Adapted for founder-led project communications.