# Humanizer > Rewrite existing text to remove AI patterns and match the project's voice. Use when pasting text from another AI, rewriting old drafts, or cleaning up imported content. - Skill: `eugenezhangco-spec/humanizer` (Agent Skill) - Install (CLI): `npx skillmds@latest add eugenezhangco-spec/humanizer` - Raw SKILL.md: https://api.skillmd.com/api/skills/eugenezhangco-spec/humanizer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: eugenezhangco-spec (https://skillmd.com/u/eugenezhangco-spec) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/eugenezhangco-spec/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](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing), maintained by WikiProject AI Cleanup. Adapted for founder-led project communications.