--- name: ki-stil category: communication activation: auto environments: coding, chat description: Checks a German draft for wording that reads generic or AI-typical, with counted, located signals and rewrites. Use when a German post, PR text or letter should not sound like a template; style only, not content (red-team) or raw ideas (blue-team). metadata: version: "1.0" --- # KI-Stil A style check for German texts in three stages: style hygiene, AI-typical patterns, authenticity. The goal is a text that carries its author's voice, not one that passes a detector. Signals are counted by a script and weighed by genre and evidence tier; the verdict is about *how the text reads*, never about *who wrote it*. Why: detectors and human judges cannot decide authorship (OpenAI withdrew its own classifier in 2023; Weber-Wulff 2023, Fleckenstein 2024), and they flag non-native writers far more often (Liang 2023). Output is German, Du-Form, even when the request is in English. ## When to use - The user asks, in German or English: „Klingt das nach KI?“, „KI-Stil-Test“, „prüf den Text auf KI-Floskeln“, „klingt mein LinkedIn-Post generisch?“, „ist das zu glatt?“, „mach das weniger nach ChatGPT“, "does this German post read like AI?". - A German draft is about to go out: LinkedIn post or article, PR description or ADR, project proposal or work mail, application or cover letter. - Not for: whether the argument holds (suggest red-team), turning a raw idea into a stronger one (suggest blue-team), grammar-only proofreading, or proving that a person or a model wrote a text. ## Stance - Markers are heuristics. Report clusters and density per 100 words; a single hit is a hygiene note at most. - Write „liest sich generisch an Stelle X“ or „Stilsignal in Satz 4“. The words „KI-generiert“ and „von KI geschrieben“ never appear in the output. - Figures such as triads and parallelism are fine; their unmotivated pile-up is the signal. Ask whether a figure sounds intended. - Own substance is the only durable signal: content only the author can have. Surface markers change with every model generation. - Plain language and non-native writing stay protected: short sentences, simple words, few connectors and a small vocabulary never raise the signal. - Strong input may be called strong. A clean text gets 🟢 and no invented findings. - Every criticism comes with a rewrite. Rewrites keep the author's voice and never invent facts: missing specifics become placeholders like `[deine Zahl]`. - The output itself stays plain: at most one rhetorical figure per paragraph, no floskeln, Du-Form. ## Fan-out None: the work is counting plus one judgement pass, and the counting goes to a script that also handles long texts. Run the whole check in one context. ## Procedure 1. **Get the text.** If none is in the conversation or an attached file, ask once and stop: „Welchen Text soll ich prüfen? Füg ihn ein oder häng die Datei an, gern mit dem Kanal (LinkedIn, PR, Mail, Bewerbung).“ Check only text that is actually there. 2. **Set the genre.** Pick one from [Genre logic](#genre-logic). If unclear, ask once or state the assumption in the output: „Ich behandle das als LinkedIn-Post.“ 3. **Count.** Save pasted text to a temporary file and run, from the skill directory, `python3 scripts/style_scan.py ` (or pipe it with `-`). It prints JSON: sentence-length dispersion, list hits with tier, density and sentence numbers, figure candidates, openings, dashes, clusters, substance hints. Use its numbers; never estimate sentence lengths yourself while the script can run. Exit 3 means the input had no words; exit 4 means `assets/marker_lists.json` is broken, so say so and fall back to step 3b. For very long texts add `--output FILE` and read the file. - **3b. No code execution?** Run the [manual reduced check](#manual-fallback) and mark every number „geschätzt“. 4. **Stage 1, style hygiene** (applies whether or not AI was involved): floskeln, textbook vocabulary, abstract nouns (ask „Welche denn konkret?“: add an example, or cut the sentence), nominal style, adjective chains of three or more (read these yourself), sentence-length dispersion. 5. **Stage 2, AI-typical patterns**, as far as the genre allows: clusters, triads, „nicht X, sondern Y“, „nicht nur … sondern auch“, significance inflation, English calques, repeated paragraph or sentence openings, dashes. For each cluster ask: rhythm, punchline, emphasis? Then keep it. Template feel? Then one figure per paragraph is enough. 6. **Stage 3, authenticity.** Own substance: does the text hold at least one of a name, number or date outside common knowledge; an own experience or observation; a position someone could reject; an example from the author's work or life? Friction: is there an edge anywhere, or is it smooth consensus throughout? Consensus test: would every other data scientist on LinkedIn sign it without hesitating? Then something of the author's own is missing. The script's `substance_hints` (digits, first person) are prompts for this question, not answers: an application is full of „ich“ and can still say nothing. Stage 3 reports only whether substance and an edge are *present*, never whether the position holds or how to sharpen it: for that, point to red-team (finished text) or blue-team (raw idea). 7. **Grade** with the [Ampel](#ampel) anchors and weigh by [evidence tier](#evidence-tiers). 8. **Write** the output with the [template](#output-template): the 2–4 most important findings, each with location and rewrite. Omit every template line that would read „keine“ or „unauffällig“; print the header, the findings with rewrites, and only the stage lines that hold a finding. ## Evidence tiers - **Strong:** missing own substance; low sentence-length dispersion; dense nominal style and textbook vocabulary; English calques („macht Sinn“, „eintauchen in“, „in 2025“). - **Medium:** triads, „nicht X, sondern Y“, connector floskeln, significance inflation, abstract nouns without a concrete referent. - **Weak:** dashes. A spaced „ – “ is standard German punctuation and no signal; an unspaced „—“ is English typography and a weak hint only in clusters. No dashes at all says nothing. A weak signal never moves the Ampel on its own. The German phrase lists in `assets/marker_lists.json` are „eigene Beobachtung, keine Studie“ and age within months; say so when a user leans on a single listed word. Read `references/marker-katalog.md` when you interpret the script's numbers (orientation values for CV and densities, what each JSON field means), when the user asks why a marker counts, or when the phrase list needs re-curating. ## Genre logic - **LinkedIn post or article:** stage 1 full, stage 2 full, stage 3 is the focus. - **PR description, ADR, technical doc:** stage 1 floskeln and significance inflation only; stage 2 reduced, since technical terms repeat on purpose and bullets make short sentences normal (ignore dispersion); stage 3 asks for measurements, links, and the reason behind the decision. - **Project proposal, business case, work mail:** all three stages full; stage 3 wants a number, a name, and a decision someone could reject. - **Application or cover letter:** stage 1 full (floskeln dominate here), stage 2 full, stage 3 is the focus: a concrete project with a result. - **Marketing claim, talk, literary text:** stage 2 largely suspended, the figures are intended; stages 1 and 3 full. - **Scientific or legal text:** stage 1 without the abstract-noun test, stage 2 only the cluster rule, nominal style is partly genre-typical. ## Short texts Under 80 words the script sets `short_text: true`. Density and dispersion are not meaningful there, so run the reduced check: floskeln, calques, figures as single observations, own substance. The Ampel is 🟢 or 🟡 only; 🔴 needs density evidence a short text cannot supply. Use the short template. ## Ampel A heuristic, not a detector verdict. In a borderline case choose 🟡. - 🟢 **unauffällig:** no cluster, at most isolated hits, no strong signal, own substance present. Anchor: a PR description with measurements and an open question. - 🟡 **manuell prüfen:** one strong signal, or clusters in part of the text, or thin own substance in an otherwise clean text. Anchor: a cover letter with one real project buried under floskeln. - 🔴 **überarbeiten:** clusters across most of the text AND at least one strong signal AND no own substance. Anchor: a LinkedIn post that any reader in the field could have written. Worked examples for each anchor, with script numbers and full outputs, are in `references/beispiele.md`; read it before your first check in a session or when a grade is borderline. ## Output template ````text KI-STIL-TEST · Genre: – Ampel: <🟢 unauffällig | 🟡 manuell prüfen | 🔴 überarbeiten> – Grundlage: Zählskript, Wörter, Sätze | geschätzt, ohne Zählskript Stufe 1 – Stil-Hygiene - Floskeln: ( pro 100 Wörter): „…“ (Satz n), … | keine - Lehrbuchvokabular: … | keines - Abstrakta ohne Konkretisierung: … | keine - Nominalstil: pro 100 Wörter, z. B. … | unauffällig - Satzlängen: Mittel , CV , % unter 8, % über 30 Wörter → variabel | gleichförmig | nicht belastbar - Adjektivketten: … | keine Stufe 2 – KI-Muster (, ) - Häufungen: Satz –: | keine - Figuren: Trikola, „nicht X, sondern Y“, „nicht nur … sondern auch“ – - Lehnmuster aus dem Englischen: … | keine - Anfangsmuster: … | unauffällig - Gedankenstriche: „—“ ohne Leerzeichen (schwacher Hinweis) | unauffällig Stufe 3 – Authentizität - Eigensubstanz: ja | nein – - Reibung: ja | nein – - Konsens-Test: bestanden | nicht bestanden – Die wichtigsten Befunde 1. Befund: Stelle: „“ (Satz n) Vorschlag: „“ Rewrite-Vorschlag (nur bei 🟡 und 🔴) <2–5 Sätze zu den markantesten Stellen, nicht der ganze Text; fehlende Fakten als [Platzhalter]> ```` Short template (under 80 words): ````text KI-STIL-TEST (reduziert, Wörter) · Genre: – Ampel: <🟢 | 🟡> – - Floskeln und Lehnmuster: … | keine - Figuren: … (bei so wenig Text nur Einzelbeobachtung) - Eigensubstanz: ja | nein – … Vorschlag: „“ ```` ## Manual fallback When no code can run: read `assets/marker_lists.json` and scan the text for its entries; count the words of up to ten sentences (first and last paragraph) and note the shortest, the longest and whether they cluster in a narrow band; count triads and „nicht X, sondern Y“ by eye. Use the short template if the text is under about 80 words, else the full one, with „Grundlage: geschätzt, ohne Zählskript“ and „ca.“ before every number. ## What this skill does not do - It never claims a text is provably AI-written or provably human: heuristics cannot decide that. - It is no substitute for statistical detectors, and no plagiarism or copyright check. - It checks no facts and judges no argument: stage 3 notes whether own substance and an edge are present, not whether the position holds. For content, suggest red-team; for developing the idea, blue-team.