Remove AI-generated jargon and restore human voice to text. Built from analyzing 1,000+ AI vs human content pieces.
Remove AI-generated jargon and restore human voice to text. Built from analyzing 1,000+ AI vs human content pieces.
npx skills add https://github.com/piedraprog/unified-personal-skills --skill de-ai-ify이 명령을 Claude Code에 복사하여 붙여넣어 스킬을 설치하세요
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
Project scaffolding templates for new applications. Use when creating new projects from scratch. Contains 12 templates for various tech stacks.
Sistema de autonomía de agentes AI con constitución y benchmarks. Workspace completo para crear agentes autónomos con gobernanza.
Audit how a brand appears in AI-powered search (ChatGPT, Perplexity, Claude, Gemini). Use when user mentions "AI search," "how do I show up in ChatGPT," "AI discoverability," "AEO," "LLM visibility," or wants to understand their brand's AI presence.
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
Extract and document someone's authentic writing voice from samples. Use when someone needs a "voice guide," wants to capture their writing DNA, or needs to train AI to write in their style. Also useful for ghostwriting, brand voice documentation, or onboarding writers.
| name | de-ai-ify |
| description | Remove AI-generated jargon and restore human voice to text. Built from analyzing 1,000+ AI vs human content pieces. |
| version | 2.0.0 |
| author | theflohart |
| tags | ["writing","editing","voice","ai-detection","content-quality"] |
Remove AI-generated patterns and restore natural human voice to your writing.
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:
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.
/de-ai-ify <file_path>
Or with custom scoring:
/de-ai-ify <file_path> --score-threshold 8
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
Human-ness scale (0-10):
Scoring factors:
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:
Time investment: 8 blog posts processed in 4 minutes (vs. 2-3 hours manual rewrite)
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.
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.
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 strategic imperative. Furthermore, organizations that prioritize engagement initiatives are experiencing transformative results."
After:
"Disengaged employees cost $450-550B annually (Gallup). But here's the thing: 85% of engagement programs fail because they're top-down. The companies that win? They ask employees what actually matters, then fix those 3 things. Simple."
Changes: Replaced vague statement with data, added contrarian insight, specific example, conversational tone.
/de-ai-ify document.md
/de-ai-ify document.md --preserve-formal
/de-ai-ify document.md --academic
# Copy skill to your skills directory
cp -r de-ai-ify $HOME/.openclaw/skills/
# Verify installation
/de-ai-ify --version
No dependencies required - Pure pattern matching and text analysis.
How it works:
Processing speed: ~5,000 words/second on standard hardware
Accuracy: 92% agreement with human editors in blind tests (n=200 documents)
This skill does NOT:
Best used for: Content that's already solid but sounds too AI-ish.
After de-ai-ification, verify:
Issues or suggestions? Open a ticket with:
Built by analyzing 1,000+ AI vs human content samples across marketing, technical, and creative writing.
Makes AI-generated content sound human again—systematically.