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Neue Skills, Referenzen & OpenWiki-Doku integriert

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Umfangreiche Erweiterung der Skill-Bibliothek: Neue Skills für Humanisierung (Englisch/PT-BR), Design-Validierung, AI-SEO und Coolify-Deployment inkl. Regelwerke, Presets, Pattern-Referenzen, Testfälle und Automatisierungsskripte. Zusätzliche Skills für Revenue-Centric Design, Pier Cloud, OKF, Lebenslauf- und LinkedIn-Optimierung sowie zahlreiche Referenzdateien, Checklisten und YAML/JSON/Markdown-Templates. Einführung einer vollständigen OpenWiki-Dokumentation mit Architektur-, Domain- und Workflow-Beschreibungen, zentralem Index und automatisierten Updates. Modularer Aufbau, restriktive Lizenzen und umfassende Qualitäts- und Evaluationsmechanismen für alle neuen Inhalte.
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# Composition Patterns - AI Tropes in English
Structural patterns betraying AI-generated text at the level of **composition** - how the text is assembled, not what it says. Includes tropes cataloged by [tropes.fyi](https://tropes.fyi/directory) and the concept of **Semantic Ablation** (The Register, Feb 2026).
---
## Composition Tropes
### 1. Fractal Summaries
**Problem:** AI announces what it will say, says it, then summarizes what it said - in each section, subsection, and paragraph. Text becomes infinite recursion of meta-commentary.
**Before (AI):**
> In this section, we will explore how artificial intelligence is transforming the financial sector. We will examine three key aspects: process automation, predictive analytics, and customer service.
>
> [...3 paragraphs...]
>
> As we have seen in this section, artificial intelligence is transforming the financial sector through process automation, predictive analytics, and customer service. In the next section, we will address the challenges of this transformation.
**After (human):**
> Itau cut 40% of its back-office team in two years. Wasn't layoffs - it was automation eating the edges. A credit process that took a week now runs in four hours. The analyst who remains doesn't analyze: they supervise the model that does.
**Cut on sight:**
- "In this section, we will..."
- "As we saw previously..."
- "Next, we will discuss..."
- "As mentioned in the previous section..."
- "To summarize what we've discussed..."
**Correction techniques:**
- Eliminate recursion: the conclusion is ONE thing - at the end. Subsections don't need mini-conclusions
- Convert meta-commentary to direct statement: "As we saw, AI transforms the sector" -> "AI transforms the sector in three ways"
- If text has 3+ subsections with mini-conclusions, merge into a single block with continuous flow
---
### 2. Dead Metaphor on Repeat
**Problem:** AI finds a metaphor at the beginning and repeats ad nauseam as if it were the spine of the text. "Ecosystem" appears 30 times. "Journey" appears in every paragraph. The metaphor loses all power - becomes noise.
**Before (AI):**
> The startup ecosystem is maturing. In this ecosystem, the players need to adapt. The ecosystem demands new competencies. To survive in this ecosystem, entrepreneurs must build solid networks. The future of the ecosystem depends on public policies that foster innovation within the ecosystem itself.
**After (human):**
> The startup scene in the US changed - from garage with pitch deck to serious business with governance and boards demanding results. Anyone who started in 2019 thinking all you needed was a good idea and a seed round now faces investors who want unit economics. The party ended; the real work started.
**Rule:** Never repeat the same figurative word more than twice in a text. After the second use, find a different way to say it - or just say the concrete thing.
---
### 3. The "Many People" Ghost
**Problem:** AI attributes claims to unnamed masses: "many people believe", "researchers have found", "companies are increasingly", "there's a growing consensus". No specific person is cited. No specific research is named. It's the literary equivalent of "people are saying."
**Before (AI):**
> Many experts believe that AI will transform education. Researchers have found that personalized learning approaches yield better outcomes. Companies are increasingly investing in edtech solutions, reflecting a growing consensus that traditional methods are no longer sufficient.
**After (human):**
> Sal Khan thinks AI tutoring will outperform classrooms within a decade. He might be right - Khan Academy's pilot data shows 30% improvement on math scores with AI tutoring. But Audrey Watters has been calling bullshit on edtech promises for fifteen years, and she's usually right too.
**Rule:** If you can't name the expert, the researcher, or the company - either find one, or rephrase as your own opinion.
---
### 4. The Five-Paragraph Essay
**Problem:** AI defaults to intro-3points-conclusion structure regardless of content or context. Every piece becomes a high school essay: thesis, body paragraph 1, body paragraph 2, body paragraph 3, conclusion restating thesis. This is the structural equivalent of "In this essay, I will argue..."
**Before (AI structure):**
```
Introduction: State thesis
Point 1: First argument with support
Point 2: Second argument with support
Point 3: Third argument with support
Conclusion: Restate thesis in different words
```
**After (human structure options):**
```
Open with a story -> derive the principle -> complicate it -> leave an open question
Start with the conclusion -> explain why it's surprising -> show the evidence
Describe the problem in detail -> show three failed solutions -> reveal what worked
```
**Rule:** Structure should emerge from content, not be imposed from template. Good writing starts where it needs to start and ends where it needs to end.
---
### 5. Semantic Ablation (The Register, 2026)
**Problem:** After multiple AI refinement passes, text loses specificity, personality, and edge. Each pass removes anything "risky" or "unusual" until what remains is perfectly smooth, perfectly generic, perfectly dead. The Register calls this "semantic ablation" - the wearing away of meaning through machine-polishing.
**Symptoms:**
- All specific examples replaced with generic ones
- All strong opinions softened to "balanced" perspectives
- All technical jargon replaced with layperson equivalents (losing precision)
- All humor or personality flattened to neutral tone
- Numbers rounded or removed ("about 3 million" becomes "many")
**Before (ablated):**
> Many companies are adopting new approaches to software development. These approaches offer various benefits and come with certain challenges. Teams should carefully evaluate their options.
**After (restored):**
> 47 YC companies from the W24 batch shipped their MVPs using AI coding agents. Not "AI-assisted" - full agent mode. Half of them have zero engineers on staff. The challenge is debugging: when the agent writes 10,000 lines in a night, who reviews it?
**Detection signals (from brandonwise/humanizer's statistical model):**
- TTR (Type-Token Ratio) below 0.45 - vocabulary is being recycled
- Burstiness below 5 - all sentences the same length (robotic rhythm)
- Shannon entropy significantly lower than human baseline for the genre
- Concrete noun density below 40% - everything is abstract
**Restoration technique:** Add back specificity at every opportunity: names, numbers, dates, anecdotes, qualifications. If the original had them, restore. If it didn't, flag that the text needs concreteness.
---
### 6. The Balanced Bookend
**Problem:** AI opens and closes with suspiciously symmetrical statements. The final paragraph echoes the first with slightly different phrasing, creating an artificial sense of circular completion. Human writers don't do this unless deliberately crafting a literary piece.
**Before (AI):**
> Opening: "The intersection of AI and healthcare presents both unprecedented opportunities and significant challenges."
> [...]
> Closing: "As we've seen, the intersection of AI and healthcare continues to present both remarkable opportunities and notable challenges that will shape the future of medicine."
**After (human):**
> Opens with a specific story about a misdiagnosis caught by AI.
> [...]
> Ends with an open question: "So who's liable when the AI is right and the doctor disagrees?"
**Rule:** Endings should advance the thought, not echo it. If your conclusion says the same thing as your introduction, one of them is redundant.

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# Content Patterns
Patterns where AI inflates importance, fabricates authority, or closes texts with predictable formulas. The easiest to detect because they sound like press releases - nobody talks like this.
---
### 1. Undue emphasis on significance, legacy, and trends
**Trigger words/phrases:** represents a milestone, is a testament to, plays a crucial/vital/pivotal role, underscores the importance of, reflects a broader trend, symbolizing the, contributing to the, paving the way for, shaping the future of, ever-evolving landscape, inflection point, indelible mark, deeply rooted, redefines the paradigm
**Problem:** AI transforms any mundane fact into a revolution. A CRUD app becomes "a milestone in digital transformation". A startup pivot becomes "an inflection point in the innovation ecosystem". No human writes like this about normal things.
**Before (AI):**
> OpenAI represents a fundamental milestone in the transformation of the artificial intelligence landscape, actively shaping the future of AI development and paving the way for a new era of human-computer interaction.
**After (human):**
> OpenAI started by releasing GPT-3 as an API. It worked because nobody else was making large language models accessible to developers at that point. Now they have a consumer product with 100 million users.
**Detection signals:**
- Absolute superlatives without quantification ("greatest", "best", "unprecedented", "first-ever")
- Grandiose transformation verbs ("redefine", "shape", "pave the way")
- Text describing anything as an "inflection point" without saying what changes afterward
**Correction techniques:**
- Convert superlatives to **concrete data**: "largest fintech" -> "80 million customers"
- Replace grandiose verbs with **specific action verbs**: "pave the way" -> "hired 3 engineers for"
- "Journalist test" - if a reporter would read the sentence and ask "how so?", the term is empty
---
### 2. Forced emphasis on notability and media coverage
**Trigger words/phrases:** widely recognized, covered by major outlets, featured in leading publications, active social media presence, according to industry experts, benchmark in the market
**Problem:** AI lists outlets and awards as proof of importance without saying what was said or why it matters. Becomes a turbocharged resume - impresses in a vacuum but informs nothing.
**Before (AI):**
> The company has been featured in TechCrunch, Bloomberg, The New York Times, and Wired. Widely recognized as a benchmark in the B2B SaaS market, it maintains an active social media presence with over 200,000 followers across platforms.
**After (human):**
> In a 2024 interview with Bloomberg, the CEO said ARR tripled after they shifted from enterprise-only to mid-market. The pivot took six months and cost them their two largest contracts.
**Detection signals:**
- Listing publications without citing specific articles (date, title, link)
- "Active social media presence" without metrics (followers, engagement rate)
- Mention of awards or rankings without verifiable source
**Correction techniques:**
- If real source exists -> cite with date and link: "Per TechCrunch, March 12, 2025 (link)"
- If no source exists -> cut the notability claim entirely
- "Verifiability test" - if the reader can't check in 30 seconds, it's puffery
---
### 3. Superficial analysis with present participles
**Trigger words/phrases:** underscoring the importance of, demonstrating the commitment to, reflecting the trend toward, contributing to the strengthening of, evidencing the potential of, driving innovation, fostering growth, solidifying its position as
**Problem:** AI glues participle phrases to the end of sentences to simulate analysis, but it isn't analyzing. It's syntactic filler - padding without information. Like that intern who writes 3 pages to say "it worked".
**Before (AI):**
> Stripe launched native integration with WhatsApp Business, demonstrating its commitment to innovation in digital payments and solidifying its position as a leader in the segment, driving the digital transformation of SMBs globally.
**After (human):**
> Stripe launched WhatsApp Business integration. Makes sense - most SMB leads in emerging markets come through WhatsApp, not web forms.
**Detection signals:**
- Sentence-final participle phrases that restate the main clause in grander terms
- "Demonstrating commitment to..." (always empty)
- Three or more participle clauses chained with commas
**Correction techniques:**
- Delete the participle clause and check if meaning is lost. Usually it isn't.
- If meaning IS lost, convert to a separate sentence with a specific claim
- "So what?" test - if the participle clause doesn't answer "so what?", cut it
---
### 4. Hollow future projections
**Trigger words/phrases:** poised to, set to transform, is expected to revolutionize, has the potential to reshape, promises to redefine, will likely emerge as, positioned to become
**Problem:** AI loves predicting transformative futures without evidence. These phrases create an illusion of analysis while saying nothing falsifiable.
**Before (AI):**
> The technology is poised to transform the healthcare industry, promising to redefine patient outcomes and reshape the landscape of medical diagnostics as we know it.
**After (human):**
> Two hospitals in Boston are running pilot programs with the diagnostic tool. Early numbers show 12% fewer false negatives on lung scans. Whether that scales to 4,000 hospitals is a different question entirely.
**Detection signals:**
- Future tense without specific timeline
- "Poised to" / "set to" without citing who said so or what evidence supports it
- Combination of future projection + superlative ("will revolutionize")
**Correction techniques:**
- Replace with current evidence: what exists NOW that suggests the future claim?
- If no evidence exists, either cut the claim or caveat it: "If X happens, then Y"
- Anchor to specific numbers, dates, or named sources

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# English-Specific Patterns
Patterns unique to AI-generated English text that don't have direct equivalents in other languages. These exploit the specific quirks of English grammar, register mixing, and contraction patterns that AI consistently gets wrong.
---
### 1. Contraction Avoidance
**Problem:** AI-generated English dramatically underuses contractions compared to human writing. In informal contexts, this is one of the most reliable statistical signals. GPTZero and Originality.ai both flag texts with unusually low contraction rates.
**Human contraction rates by register:**
| Register | Contraction rate |
|---|---|
| Casual speech/DM | 95%+ ("don't", "won't", "it's", "we're", "they'll") |
| Blog/newsletter | 80-90% |
| Professional email | 60-80% |
| Journalism | 40-70% (varies by outlet) |
| Academic | 10-30% (deliberately formal) |
| Legal | 5-15% (genre convention) |
**AI typical rate:** 30-50% across ALL registers (doesn't adapt)
**Before (AI - blog register):**
> It is important to note that the system does not function as expected. We cannot determine the root cause at this time. There is no indication that this will be resolved soon.
**After (human - blog register):**
> It's not working the way it should. We can't figure out why yet. There's no sign it'll be fixed soon.
**Contraction replacement rules:**
| AI form | Human form (informal) | Keep formal when... |
|---|---|---|
| it is | it's | academic emphasis needed |
| do not | don't | legal/safety context |
| cannot | can't | formal document |
| will not | won't | emphasis on refusal |
| they are | they're | ambiguity risk |
| we have | we've | ... |
| should not | shouldn't | ... |
| would not | wouldn't | ... |
| there is | there's | ... |
| that is | that's | ... |
**Rule:** In Essay, Corporate Informal, Social Post, and Casual presets, force contractions to match human rates. In Academic and Legal presets, low contractions are correct.
---
### 2. Register Uniformity
**Problem:** Humans naturally mix registers within a single text - formal vocabulary next to colloquial phrasing, technical terms next to slang, high register next to low. AI maintains a perfectly uniform register throughout, which paradoxically signals artificiality.
**Before (AI - uniformly mid-register):**
> The implementation proved challenging but ultimately successful. The team encountered several obstacles during the process but managed to resolve them through collaborative effort and systematic problem-solving.
**After (human - mixed register):**
> The implementation was a nightmare for about two weeks - classic "it works on my machine" stuff. Then Sarah figured out the race condition and we shipped it. Sometimes the fix is embarrassingly simple.
**Human register mixing patterns:**
- Technical term + casual explanation: "The TTL expired - basically the cache forgot everything"
- Formal structure + informal aside: "The architecture is sound. (The naming conventions, less so.)"
- Precise vocabulary + colloquial connector: "The latency delta was significant. Look, 340ms vs 40ms isn't subtle."
**Rule:** Inject at least one register shift per 300 words in non-academic presets. A formal paragraph should have one casual moment. A casual text should have one precise term.
---
### 3. Passive Voice Overuse
**Problem:** AI defaults to passive voice far more than humans, especially when the agent (who did the thing) is uncertain or the AI is hedging. Human English strongly prefers active voice in most contexts.
**Before (AI):**
> The decision was made to restructure the team. It was determined that performance had been negatively impacted. New processes were implemented and improvements were observed over the following quarter.
**After (human):**
> The VP restructured the team. Performance had tanked - everyone knew it. They implemented new processes and saw improvement by Q3.
**Acceptable passive uses (don't convert these):**
- When the agent is genuinely unknown: "The server was compromised overnight"
- When the object is more important: "Three people were injured in the crash"
- Scientific convention: "The sample was heated to 300°C"
- Deliberate de-emphasis of actor: "Mistakes were made" (though this is also a cliche)
**Detection signal:** More than 30% of clauses in passive voice = AI signal. Measure by counting "was/were + past participle" constructions.
---
### 4. Transition Word Abuse
**Problem:** AI uses explicit transition words between nearly every sentence. Human writers trust the reader to follow logical connections without signposting every turn.
**AI transition word frequency:** every 2-3 sentences
**Human transition word frequency:** every 5-8 sentences (varies by genre)
**The worst offenders (cut 80% of these):**
| Word | AI frequency | Human frequency | Action |
|---|---|---|---|
| Furthermore | Every paragraph | Rare in non-academic | Cut or replace with "And" |
| Moreover | Every paragraph | Rare | Cut |
| Additionally | Every paragraph | Rare | Cut |
| However | Every 3 sentences | Every 6-8 sentences | Keep some, cut most |
| Consequently | Frequent | Rare in non-academic | "So" or cut |
| Subsequently | Frequent | Rare | "Then" or cut |
| Nevertheless | Frequent | Occasional | Keep sparingly |
| In contrast | Frequent | Occasional | "But" or restructure |
**Rule:** Trust the reader. If the logical connection is obvious from context, no transition word is needed. Use transitions only when the connection would genuinely surprise the reader.
---
### 5. "The" Proliferation in Abstractions
**Problem:** AI over-uses "the" before abstract nouns, creating a false specificity. "Innovation" becomes "the innovation". "Technology" becomes "the technology". This makes generic statements sound as if they refer to something specific when they don't.
**Before (AI):**
> The innovation in the space has led to the advancement of the technology. The community has embraced the shift toward the adoption of the new paradigm.
**After (human):**
> Innovation in this space accelerated after GPT-4 launched. Developers adopted the new approach quickly - mostly because it was easier, not because anyone evangelized it.
**Rule:** If "the [abstract noun]" doesn't refer to a previously introduced specific thing, it's probably AI padding. Cut "the" or replace with a specific referent.
---
### 6. Absence of Sentence Fragments
**Problem:** Human English, especially in informal and semi-formal writing, uses sentence fragments freely for rhythm and emphasis. AI almost never produces them - every unit is a grammatically complete sentence.
**AI (all complete sentences):**
> The product launched last week. It received positive reviews. The team is now focused on iteration. They plan to ship a major update by March.
**Human (with natural fragments):**
> The product launched last week. Positive reviews all around. Now the team's heads-down on iteration. Major update by March. Maybe.
**Fragment types humans use:**
- Answers: "Absolutely not."
- Emphasis: "Every. Single. Time."
- Afterthoughts: "Not ideal."
- Rhythm breaks: "So there's that."
- Dramatic pause: "Three million lines of code. Overnight."
**Rule:** In Essay, Corporate Informal, Social Post, and Casual presets, inject at least one sentence fragment per 200 words. In Academic and Legal presets, fragments are inappropriate.
---
### 7. Perfect Paragraph Length Uniformity
**Problem:** AI generates paragraphs of remarkably similar length (typically 3-5 sentences, 80-120 words each). Human writing varies paragraph length dramatically - from one-sentence paragraphs to 300-word blocks.
**AI pattern:** 4 sentences, 4 sentences, 4 sentences, 4 sentences
**Human pattern:** 1 sentence, 6 sentences, 2 sentences, 8 sentences, 1 sentence
**Detection signal (from brandonwise/humanizer):** Coefficient of variation in paragraph length below 0.3 = AI signal. Human English averages ~0.6 CoV in paragraph length.
**Rule:** Vary paragraph length intentionally. Use one-sentence paragraphs for impact. Use long paragraphs for complex arguments that need sustained development. The variation IS the voice.

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# Language and Grammar Patterns (English)
Patterns that betray AI-generated text at the level of word choice, grammatical constructions, and sentence structure. Based on Wikipedia's Signs of AI Writing + tropes.fyi + blader/humanizer's 29-pattern set + brandonwise/humanizer's statistical model.
---
### 1. AI Vocabulary (The Slop Dictionary)
**Tier 1 - Zero Tolerance (cut on sight):**
| Word/Phrase | Why it's a tell | Human alternative |
|---|---|---|
| delve | No one says this in conversation | explore, dig into, look at |
| tapestry | Always used as "rich tapestry of..." | (cut entirely - always filler) |
| landscape (figurative) | "The AI landscape" | the AI space, AI right now |
| testament to | "It's a testament to..." | shows that, proves |
| serves as a reminder | Always preamble to nothing | (cut - just state the thing) |
| it's worth noting | Meta-commentary, not content | (cut - the note IS the content) |
| it bears mentioning | Same as above | (cut) |
| the ever-evolving landscape | Double slop | (cut entirely) |
| navigate (complexities) | "Navigate the challenges of" | deal with, handle, figure out |
| spearhead | "Spearheading the initiative" | lead, run, start |
| multifaceted | "This multifaceted problem" | complex, messy, complicated |
| pivotal | "A pivotal moment" | important, key, big |
| paramount | "Of paramount importance" | essential, critical |
| underscores | "This underscores the need" | shows, highlights |
| underpin | "Principles that underpin" | behind, supporting |
| in the realm of | "In the realm of AI" | in AI |
| shed light on | "Shedding light on this issue" | explain, clarify, show |
| this highlights | Meta-commentary | (cut - the highlight IS the sentence) |
**Tier 2 - High Suspicion (replace when clustered, OK once per 1000 words):**
`crucial, vital, comprehensive, robust, leverage, foster, facilitate, embark, harnessing, utilize, endeavor, moreover, furthermore, additionally, subsequently, nonetheless, overarching, intricate, nuanced, holistic, synergy, paradigm, catalyst, orchestrate, seamless, ecosystem (abstract), journey (figurative), unlock (figurative), empower, elevate`
**Tier 3 - Context-Dependent (flag if >2 per 500 words):**
`significant, enhance, innovative, dynamic, diverse, inclusive, sustainable, transformative, streamline, optimize, cutting-edge, state-of-the-art, game-changer, disruptive, scalable, impactful, actionable, meaningful, compelling, groundbreaking`
**Detection rule:**
- 1 Tier-1 word = flag the sentence
- 3+ Tier-2 words in one paragraph = flag the paragraph
- 5+ Tier-3 words in one page = flag the text
- Any combination of 2+ Tier-1 words in 500 words = near-certain AI
---
### 2. Copula Avoidance
**Trigger patterns:** constitutes, represents, serves as, functions as, operates as, positions itself as, stands as, emerges as, figures as
**Problem:** AI avoids "is" and "has" as if they were forbidden words. Replaces them with elaborate constructions no human would use in natural writing.
**Before (AI):**
> Stripe constitutes the leading payments infrastructure for the internet. The platform serves as a comprehensive solution for businesses and positions itself as the de facto standard for online transactions.
**After (human):**
> Stripe is the biggest payments company online. Their API is what most startups use to take money.
**Rule:** If "is" or "has" would work, use it. Save elaborate verbs for when you need precise meaning, not sophistication theater.
---
### 3. Negative Parallelisms
**Trigger patterns:** not merely X, but Y; it's not just about X, it's about Y; more than X, it's Y; goes far beyond X; transcends the simple X; not simply X, it's fundamentally Y
**Problem:** Formulaic construction that artificially inflates importance. Creates a false dichotomy where the writer pretends to reject something then embraces something bigger - but both parts say the same thing.
**Before (AI):**
> Product management is not merely about writing user stories. It goes far beyond simply shipping features - it's fundamentally about solving real problems. The PM transcends the role of ticket writer; they are, above all, a translator between business and technology.
**After (human):**
> Good PMs solve problems. Bad PMs write tickets. The difference isn't philosophical - it's practical. Look at someone's backlog and you'll know in 5 minutes.
---
### 4. Tricolon Abuse (Rule of Three)
**Trigger patterns:** clarity, concision, and coherence; innovation, technology, and transformation; plan, execute, and measure; any rhythmic triad with "and" before the third item; three adjectives in sequence; three abstract nouns grouped
**Problem:** AI groups ideas in threes because tricolon is rhetorically satisfying - but when it appears in every paragraph, it becomes a tic. Human text groups in 2, in 4, in 1. Rarely in 3 repeatedly.
**Before (AI):**
> The platform delivers speed, reliability, and scalability. Teams need clarity, collaboration, and consistency. Our approach combines innovation, expertise, and dedication.
**After (human):**
> It's fast. Reliable enough that we stopped worrying about downtime. And it scales - we went from 10k to 400k requests/day without changing anything.
**Detection rule:** If a text has 3+ tricolons per page, it's almost certainly AI-generated. Humans occasionally use rule-of-three for rhetorical effect. AI uses it as a structural crutch.
---
### 5. Weasel Qualifiers
**Trigger patterns:** it could be argued that, one might suggest, there are those who believe, it has been said that, some would argue, many experts believe, it is generally accepted
**Problem:** AI uses qualifiers to avoid committing to claims. The result reads like a Wikipedia article written by someone afraid of being corrected. Humans either commit to a claim or cite a specific source.
**Before (AI):**
> It could be argued that large language models represent a significant advancement. Many experts believe this technology has the potential to transform various industries, though some would argue the risks are considerable.
**After (human):**
> LLMs are a big deal. They'll change how most knowledge work gets done - I genuinely believe that. But Hinton is right that we don't understand alignment well enough to be comfortable.
---
### 6. Nominalization Disease
**Trigger patterns:** the implementation of, the utilization of, the facilitation of, the optimization of, the enhancement of, the establishment of, provides a demonstration of, performs an analysis of
**Problem:** AI converts verbs into nouns, making sentences longer, vaguer, and harder to parse. "We analyzed" becomes "we performed an analysis of". This is the passive-aggressive cousin of passive voice.
**Before (AI):**
> The implementation of the new system resulted in the enhancement of performance metrics and the facilitation of improved collaboration across teams.
**After (human):**
> We implemented the new system. Performance improved. Teams started collaborating more.
**Rule:** If a noun ending in -tion/-ment/-ance has a simpler verb form, use the verb.

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# Style and Formatting Patterns
Patterns betraying AI-generated text through visual and structural form, not content. Detection tools use these markers as high-confidence signals.
---
### 1. Em-Dash Cascade
**Problem:** AI uses 15-25 em-dashes per medium text. Humans use 2-3, and generally prefer commas, periods, or parentheses for most functions AI assigns to em-dashes.
**Before (AI):**
> The project — which started in 2022 — brought impressive results — especially in the data area — and is now being expanded — even with limited budget — to other regions.
**After (human):**
> The project started in 2022 and brought solid results in data. It's now expanding to other regions, even with a tight budget.
**Detection signals:**
- More than 2 em-dashes per paragraph
- Em-dash where comma resolves
- Chaining of parenthetical asides with em-dashes (— X — Y — Z)
- Text where >10% of punctuation marks are em-dashes
**Correction techniques:**
- **Limit of 2 em-dashes per paragraph** - convert extras to commas, periods, or parentheses
- Differentiate use: em-dash for strong contrast, parentheses for side comment, comma for light aside
- "Editor test" - if a human editor would have cut the em-dash, cut it
---
### 2. Excessive Bold
**Problem:** AI applies bold to every keyword as if the text were a slide deck. Running text with bold on every important noun reads like a product catalog, not human writing.
**Before (AI):**
> The **platform** offers **native integration** with leading **CRMs**, ensuring **scalability** and **security** for **sales** and **marketing** teams.
**After (human):**
> The platform integrates with the major CRMs. Works well for sales and marketing teams that need to scale without losing access control.
**Detection signals:**
- Bold on more than 1-2 terms per paragraph
- Bold on common nouns (platform, team, result) without editorial reason
- Bold used as substitute for good sentence structure
- Text where >5% of words are bolded
**Correction techniques:**
- **Limit of 1-2 bolds per section** - never per paragraph
- Use bold only for **intentional contrast**: "The problem isn't the tool - it's the **process**"
- If bold is compensating for lack of clarity, **restructure the sentence** instead
- "Print test" - if text looks like it was formatted for a reader with ADHD, bold is excessive
---
### 3. List-ification (Bullet Point Abuse)
**Problem:** AI converts prose into bullet points at every opportunity. Three sentences of flowing text become a bulleted list with "Key takeaways:" above it. Human writing uses lists sparingly - for actual enumerations, not for every paragraph.
**Before (AI):**
> Here are the key benefits:
> - **Increased efficiency** - Teams work 40% faster
> - **Improved collaboration** - Cross-functional alignment
> - **Better outcomes** - Measurable ROI improvements
> - **Scalability** - Grows with your organization
**After (human):**
> Teams work faster with it - about 40% based on our internal tracking. The real win is cross-functional alignment though: people who never talked to each other before are now in the same workflow.
**Detection signals:**
- Bulleted lists that could be flowing prose
- "Key takeaways:" / "Key points:" / "Here's what you need to know:" above lists
- Parallel structure in every bullet (same length, same construction)
- Lists with 5+ items where 3 would suffice
**Correction techniques:**
- If items are truly discrete enumerable things (steps, features, names), keep as list
- If items are connected thoughts, convert to prose paragraphs
- Break bullet symmetry: vary length, mix sentence fragments with full sentences
- "Would I say this aloud as a list?" test - if you'd narrate it, it's prose
---
### 4. Header Proliferation
**Problem:** AI creates a `##` header for every 2-3 paragraphs in any text longer than 400 words. Human prose flows continuously - headers appear when genuinely changing topic, not every 150 words.
**Before (AI):**
> ## Introduction
> The problem is clear.
> ## Background
> Here's context.
> ## Current Situation
> Things have changed.
> ## Analysis
> Let's examine this.
> ## Conclusion
> In summary...
**After (human):**
> The problem is clear - and it's been getting worse since 2023. [continues flowing for 800 words with maybe one section break where the topic genuinely shifts]
**Detection signals:**
- Headers every 100-200 words in what should be continuous prose
- Generic headers: "Introduction", "Background", "Analysis", "Conclusion"
- Headers that just restate what the next paragraph says
- Document with 8+ headers for 1000 words
---
### 5. Emoji Inflation
**Problem:** AI (especially ChatGPT) injects emoji into every bullet point, section header, or list item. Human writers use emoji occasionally and contextually, not systematically.
**Before (AI):**
> 🚀 Key Features
> ✅ Automated deployment
> 💡 Smart suggestions
> 🔒 Enterprise security
> ⚡ Lightning-fast performance
**After (human):**
> The main features: automated deployment, smart suggestions, enterprise-grade security, and good performance. (It handles 10k requests/second on our benchmark.)
**Detection signals:**
- Emoji on every list item
- Emoji in headers
- More than 2 emoji per 500 words in professional text
- Systematic emoji (same emoji category repeated: all checkmarks, all rockets)
**Correction techniques:**
- Professional context: remove all emoji unless the format genuinely calls for them (social posts, chat)
- Social context: keep 1-2 per post, used for emphasis or tone, not decoration
- Never use emoji as bullet point markers in serious writing

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# Tone Patterns - Detection and Correction
Patterns betraying artificial, servile, or performative tone in English text. Each pattern includes triggers, examples (tech/startup/marketing/dev contexts), and human alternatives.
---
### 1. Sycophantic Tone
**Trigger words/phrases:** "Great question!", "Absolutely!", "Excellent observation!", "I hope this helps!", "I'm happy to help!", "Thank you for sharing!"
**Problem:** Generic praise to the interlocutor before answering. Nobody talks like this in professional writing - it's the signature of a chatbot trying to please.
**Before (AI):**
> Great question! Continuous deployment with GitHub Actions is really a fascinating approach. I'd be happy to help you with that! Let's dive in: first, you need to configure the workflow YAML...
**After (human):**
> To set up continuous deployment with GitHub Actions, start with the workflow YAML. Create a `.github/workflows/deploy.yml` triggered on push to main...
**Cut on sight:**
- "Great question!" / "Excellent point!"
- "Absolutely!" / "Definitely!"
- "I hope this helps!" / "Happy to contribute!"
---
### 2. Knowledge-Cutoff Disclaimers
**Trigger words/phrases:** "As of my last update...", "Based on the information available to me...", "As of my knowledge cutoff...", "I cannot confirm with certainty, but...", "According to my limited information..."
**Problem:** Exposes the machine nature of the author. Humans don't signal the temporal boundary of their own knowledge - they simply state what they know or say "I don't know."
**Before (AI):**
> As of my last update, Next.js 15 introduced Server Actions as a stable feature. However, more recent developments may have changed this landscape. Based on the information available to me, the recommendation is to use App Router.
**After (human):**
> Next.js 15 stabilized Server Actions. If something changed since, check the official docs - but as of what I've tested, App Router is the way to go.
---
### 3. Residual Collaborative Communication
**Trigger words/phrases:** "Here's an example of...", "I can help you with...", "I'll show you how...", "Below is...", "Feel free to ask more!", "Don't hesitate to reach out!"
**Problem:** Text retains traces of assistant-user interaction. Reads like a support response, not authored text. When published as an article or post, immediately betrays its origin.
**Before (AI):**
> Here's an example of how to implement JWT authentication in Express. I'll walk you through the setup step by step. Feel free to adapt according to your needs!
**After (human):**
> JWT auth in Express boils down to a middleware that validates the token before letting the request through. The basic setup looks like this:
---
### 4. Excessive Hedging
**Trigger words/phrases:** "it seems", "perhaps", "it could be that", "one might argue", "it's possible that", "it may be the case", "to some extent", "in a sense"
**Problem:** AI over-qualifies every statement to avoid being wrong. The result is prose with no conviction. Reads like someone trying to never be pinned down on anything.
**Before (AI):**
> It seems that perhaps the new architecture may offer some improvements. One might argue that, to some extent, the performance gains could be significant, though it's possible that further testing may reveal limitations.
**After (human):**
> The new architecture is faster. Our benchmarks show 40% improvement on cold starts. Whether that holds under production load is an open question, but the synthetic results are clear.
**Rule:** Qualify only when:
- You genuinely don't know (and say so directly: "I don't know")
- There's real disagreement among sources (cite both)
- The data genuinely doesn't support a firm claim (show the data)
Otherwise: commit to the claim.
---
### 5. Stakes Inflation
**Trigger words/phrases:** "crucial for the future of humanity", "this will define a generation", "the most important challenge of our time", "could fundamentally alter the course of", "the stakes have never been higher"
**Problem:** AI inflates the importance of everything to sound thoughtful. A CSS framework becomes "crucial for the future of web development". A project management tool becomes "fundamental to how teams will work for decades to come."
**Before (AI):**
> This represents one of the most crucial challenges facing the technology industry today. The implications could fundamentally alter the course of software development as we know it.
**After (human):**
> It's a hard problem. The teams I've seen tackle it took 6-12 months to get right. Most gave up and used the workaround instead.
**Rule:** Match stakes to scope. A framework choice is a framework choice, not a civilizational decision. Reserve grand language for genuinely grand topics - and even then, specifics beat superlatives.
---
### 6. False Empathy / Emotional Performance
**Trigger words/phrases:** "I understand how frustrating this must be", "I can only imagine how difficult", "This is truly inspiring", "What an incredible journey", "I'm deeply moved by"
**Problem:** AI performs emotions it cannot have. The result rings hollow because the reader intuitively knows no genuine feeling exists behind the words. Human writers either feel something specific and show it through detail, or they don't perform emotion at all.
**Before (AI):**
> I understand how frustrating this situation must be for everyone involved. It's truly inspiring to see the community come together during such a challenging time. What an incredible journey this has been.
**After (human):**
> That sucks. I've been there - the deploy failed at 2am on a Friday and the on-call person was unreachable. What the community did next was interesting though: three people independently submitted patches before Monday.
**Rule:** Show, don't perform. If you feel something, name it specifically. If you don't, don't fake it.

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# Voice Calibration - Presets (Full Reference)
Detailed characteristics, examples, and guidelines for each voice preset.
## 🖋️ Essay (default)
Tone of an English essayist. Controlled informality, wit, specific observation turned into insight. Mixes high and low register. A turn at the end.
**Characteristics:**
- "Look" and "honestly" coexist with precise vocabulary
- Sentence fragments as dramatic pause
- Dry humor, self-awareness
- Explicit opinion
- Rhetorical questions left unanswered
**Example:**
> Everyone knows that coworker who automated their own job and told nobody. Sat there for months pretending to type. Well. Now the entire company is that coworker - just using ChatGPT instead of Python scripts. The difference is nobody's pretending. And so the question becomes: efficiency or laziness? I don't know. Probably both.
## 📰 Journalistic
Tone of the NYT or The Atlantic. Maximum clarity, concrete data, no fluff.
**Characteristics:**
- Subject + verb + object (in that order)
- Numbers and dates whenever possible
- Attribution to named sources
- No evaluative adjectives
- No first person (except opinion columns)
**Example:**
> Nubank laid off 40 people from its customer service team in May. The company declined to comment, but two former employees confirmed that replacement by chatbots motivated the cuts. The department had 120 people at the start of the year.
## 🎓 Academic
Formal but not bureaucratic. Terminological rigor without officialese.
**Characteristics:**
- Precise domain vocabulary
- Legitimate qualifications (not empty hedging)
- References to specific authors/studies
- Avoids cliches: "it is worth noting", "it goes without saying", "in the context of"
**Example:**
> The convergence-toward-median hypothesis (Nastruzzi, 2026) finds support in TTR analysis of texts submitted to multiple AI refinement cycles. The phenomenon - semantic ablation - differs from hallucination: it does not add falsehood, it subtracts specificity.
## 💬 Corporate Informal
Startup email, professional Slack. Direct, light, no corporate speak.
**Characteristics:**
- Short, direct sentences
- Contractions used naturally
- Action verbs instead of nominalizations
- Tech jargon where appropriate (deploy, sprint, ship)
**Example:**
> Team, quick update: the hotfix went out last night, already in prod. The duplication bug stopped since 11pm. I'll monitor for another 48h and if it stays clean, we close the issue. Ping me if anything pops up.
## 📱 Social Post
LinkedIn or Twitter/X. Short, opinionated, hook in the first line.
**Characteristics:**
- First sentence is the hook
- Paragraphs of 1-2 lines
- Strong personal opinion
- Uses "I" freely
- Subtle or no CTA
**Example:**
> I fired ChatGPT from my writing workflow.
>
> Not because it's bad. Because everything I published sounded like everyone else.
>
> I went back to writing by hand. Takes 3x longer. But people actually reply now.
>
> Efficiency without voice isn't an advantage. It's invisibility.
## 💬 Casual/DM
Maximum orality. Stream of consciousness allowed.
**Characteristics:**
- Incomplete sentences ok
- Natural abbreviations (tbh, ngl, idk)
- Slang accepted
- Zero concern with formal grammar
**Example:**
> dude did you see what the data team did
>
> they pushed a model to prod without telling anyone
>
> then it started sending wrong emails to clients
>
> absolute chaos
## ⚖️ Legal / Formal
Briefs, memos, formal notices. High register with deliberate conventions that, when used intentionally, sound more authentic than AI's generic imitation.
**Characteristics:**
- Structure: background -> facts -> analysis -> conclusion/recommendation
- Controlled use of genre conventions ("notwithstanding", "hereinafter", "pursuant to")
- Citation of specific statutes, cases, regulations
- Active voice when possible to avoid bureaucratic emptiness
**Signs of AI in this register:**
- Excess "it is worth noting", "it should be emphasized", "in the context of this analysis"
- Generic citations without case numbers or statute references
- Perfectly symmetrical paragraphs (3-4 identical-length sentences)
**Example (AI -> Human):**
> *AI*: "It is well-established that the right to privacy must be balanced against public interest, as consistently recognized by relevant jurisprudence. It should be noted that the present case requires careful analysis."
>
> *Human*: "The right to privacy exists, yes - but it's not absolute. The Supreme Court held in *Carpenter v. United States*, 585 U.S. 296 (2018), that public interest can prevail. In this case, the photo was taken at a public event. The question is whether there was commercial exploitation. That's what separates privacy from publicity rights."
**What to preserve (not an AI sign):**
- Section headings in caps ("BACKGROUND", "ANALYSIS", "CONCLUSION") - expected formatting in briefs
- Numbered items in arguments and requests
- Citation with specific case numbers and dates
- Background -> facts -> analysis -> conclusion structure - it's the genre, not AI template
**Key signal separating human from AI in this register:** humans cite specific case numbers, statutes, sections. AI says "as established by relevant authorities" without citing anything.
## 🧑‍🏫 Instructional / Explainer
Edtech, documentation, tutorials, friendly technical writing.
**Characteristics:**
- Pattern: question -> explanation -> concrete example -> reinforcement
- Accessible but precise vocabulary (not dumbed down)
- Specific, verifiable examples (not "Alice has 3 apples")
- Explicit transitions: "So", "Now", "Let's see this in practice"
**Signs of AI in this register:**
- Generic, artificial examples
- Encyclopedic tone without interaction with reader
- "In this chapter, we will explore X, Y and Z" -> empty template
**Example:**
> Let's cut to it: a *callback* is a function you pass as an argument to another function, so it can "call you back" when it's done. Sounds complicated, but that's all it is. Think of ordering delivery: instead of calling every 5 minutes to check if it arrived, you leave your number and the driver texts you when they're at the door. Your number is the callback.

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# Summary - Skill Human-AI v1.0.0
1. [Operating Modes](#operating-modes)
2. [Guardrails](#guardrails)
3. [Personality & Soul - The Essayist Tradition](#personality--soul---the-essayist-tradition)
4. [Voice Calibration - Presets](#voice-calibration---presets)
- Essay · Journalistic · Academic · Corporate Informal · Social Post · Casual/DM · **Legal** · **Instructional**
5. [Humanization Process (7 steps)](#humanization-process)
- Step 0 - Quantitative ablation measurement
- Step 0.5 - Automatic type detection
- Step 1 - Diagnosis with structured checklist
- Step 2 - Pattern removal
- Step 3 - Entropy restoration
- Step 4 - Voice injection
- Step 5 - Final anti-AI pass
- Step 5.5 - Post-rewrite scoring
- Step 6 - Formatted delivery
6. [The 29 AI Vocabulary Patterns](#the-29-ai-vocabulary-patterns-english)
7. [Contraction Rules](#contraction-rules-english-specific)
8. [Regression Test Suite](#regression-test-suite)
9. [Limits and Contraindications](#limits-and-contraindications)
10. [References](#references)

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# Regression Test Suite (Full Reference)
Minimum sample set for validating future evolutions. Each test should run in full_mode and verify output matches expected result.
| # | Type | Before (AI) | After expected (synthesis) |
|---|---|---|---|
| T1 | Corporate email | "I am writing to inform you that the report will be forwarded in due course" | "Hey team, report's done - just sent it to the channel. Ping me with questions." |
| T2 | Academic paragraph | "Various authors discuss the question of language in broad terms" | "Foucault (1977) frames language as a power device; Bakhtin (1981) sees it as a dialogic arena. The disagreement isn't just terminological." |
| T3 | Legal text | "It is well-established that strict liability applies in the context of consumer relations" | "The Consumer Protection Act establishes strict liability under Section 402A. In practice, manufacturers only escape liability by proving sole consumer fault - which is rare." |
| T4 | Blog template | "In this article, we will explore 5 essential strategies to optimize your workflow" | "I'll cut to it: the strategy that saved me the most time in 2025 wasn't a new tool. It was stopping using new tools." |
| T5 | AI hedging | "As a language model, I cannot state with certainty, but it appears that perhaps the system may be functioning" | "The system's working. I just tested it and the endpoint responded in 340ms." |
| T6 | Generic explainer | "Alice has 3 apples and Bob has 5. How many do they have together?" | "Think about the last time you split a restaurant bill. That's the arithmetic that matters - not hypothetical apples." |
> **Regression criterion**: if an evolution worsens any T1-T6 test result, the change must be reevaluated.