| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 63 | | tagDensity | 0.365 | | leniency | 0.73 | | rawRatio | 0.043 | | effectiveRatio | 0.032 | |
| 77.54% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1558 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 90.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1558 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "flickered" | | 1 | "traced" | | 2 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 56 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 56 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 70 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 35 | | totalWords | 1566 | | ratio | 0.022 | | matches | | 0 | "live" | | 1 | "Zurich" | | 2 | "You'll be a barrister like your dad and you'll be very good and very unhappy and I'll come to your chambers and I'll say I told you so." | | 3 | "did" | | 4 | "only interesting feature" | | 5 | "chose" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 28 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 940 | | uniqueNames | 13 | | maxNameDensity | 1.6 | | worstName | "Eva" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Rory | 11 | | Nest | 1 | | Silas | 5 | | Hollander | 1 | | Eva | 15 | | Student | 1 | | Union | 1 | | Pre-Law | 1 | | April | 1 | | Astra | 1 | | Wardour | 1 | | Street | 1 | | Zurich | 1 |
| | persons | | 0 | "Rory" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Hollander" | | 4 | "Eva" |
| | places | | 0 | "Student" | | 1 | "April" | | 2 | "Wardour" | | 3 | "Street" | | 4 | "Zurich" |
| | globalScore | 0.702 | | windowScore | 0.5 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a document that would tell so" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1566 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 62 | | mean | 25.26 | | std | 28.89 | | cv | 1.144 | | sampleLengths | | 0 | 91 | | 1 | 12 | | 2 | 61 | | 3 | 7 | | 4 | 1 | | 5 | 19 | | 6 | 57 | | 7 | 82 | | 8 | 7 | | 9 | 1 | | 10 | 1 | | 11 | 37 | | 12 | 5 | | 13 | 3 | | 14 | 38 | | 15 | 2 | | 16 | 68 | | 17 | 15 | | 18 | 48 | | 19 | 20 | | 20 | 6 | | 21 | 5 | | 22 | 62 | | 23 | 5 | | 24 | 2 | | 25 | 49 | | 26 | 3 | | 27 | 1 | | 28 | 43 | | 29 | 76 | | 30 | 14 | | 31 | 5 | | 32 | 4 | | 33 | 2 | | 34 | 3 | | 35 | 96 | | 36 | 3 | | 37 | 19 | | 38 | 6 | | 39 | 2 | | 40 | 104 | | 41 | 14 | | 42 | 78 | | 43 | 11 | | 44 | 1 | | 45 | 56 | | 46 | 32 | | 47 | 8 | | 48 | 40 | | 49 | 7 |
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| 86.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 56 | | matches | | 0 | "gets shot" | | 1 | "being asked" | | 2 | "was allowed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 169 | | matches | | 0 | "was drying" | | 1 | "was wearing" |
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| 23.81% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 96 | | ratio | 0.042 | | matches | | 0 | "\"Yu-Fei threw the fit. Wu Ling just relayed it with feeling.\" She dropped her bag behind the bar and pulled the stool out — her stool, the third from the end, close enough to the till that she could reach the crisps." | | 1 | "She'd cut her hair — cropped it close at the sides, longer on top, the sort of cut you got in a place with appointments and a receptionist and eucalyptus in a glass jar." | | 2 | "She considered the other honest answer, which was worse: that she had been afraid Eva would look at the life she'd built — a rented room above a chip pan, a delivery bag, a fifty-eight-year-old spy who let her sit at his bar — and see exactly what it was." | | 3 | "The silence went on long enough for Silas to reappear, take the empty cup without comment, and set down two whiskies — a small mercy, or a diagnosis." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 803 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.031133250311332503 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0049813200498132005 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 16.31 | | std | 15.81 | | cv | 0.969 | | sampleLengths | | 0 | 32 | | 1 | 26 | | 2 | 3 | | 3 | 30 | | 4 | 4 | | 5 | 8 | | 6 | 42 | | 7 | 19 | | 8 | 6 | | 9 | 1 | | 10 | 1 | | 11 | 19 | | 12 | 35 | | 13 | 4 | | 14 | 18 | | 15 | 21 | | 16 | 9 | | 17 | 34 | | 18 | 4 | | 19 | 14 | | 20 | 7 | | 21 | 1 | | 22 | 1 | | 23 | 4 | | 24 | 33 | | 25 | 5 | | 26 | 3 | | 27 | 14 | | 28 | 24 | | 29 | 2 | | 30 | 50 | | 31 | 18 | | 32 | 15 | | 33 | 36 | | 34 | 12 | | 35 | 20 | | 36 | 6 | | 37 | 5 | | 38 | 46 | | 39 | 14 | | 40 | 2 | | 41 | 5 | | 42 | 2 | | 43 | 44 | | 44 | 5 | | 45 | 3 | | 46 | 1 | | 47 | 20 | | 48 | 23 | | 49 | 48 |
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| 51.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.375 | | totalSentences | 96 | | uniqueOpeners | 36 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 37.39% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 46 | | matches | | 0 | "She came in through the" | | 1 | "He never did." | | 2 | "He'd told her once that" | | 3 | "She dropped her bag behind" | | 4 | "He set the glass down" | | 5 | "he repeated, in the tone" | | 6 | "She was three sips in" | | 7 | "It was the walk." | | 8 | "You could change a face," | | 9 | "She'd cut her hair —" | | 10 | "Her coat was cashmere." | | 11 | "Her shoes had done nothing" | | 12 | "She got four steps in" | | 13 | "She hung her coat on" | | 14 | "She turned the wine glass" | | 15 | "*You'll be a barrister like" | | 16 | "She considered the other honest" | | 17 | "she said quietly" | | 18 | "She pressed her lips together" | | 19 | "She kept her eyes on" |
| | ratio | 0.457 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 46 | | matches | | 0 | "The rain had let up" | | 1 | "She came in through the" | | 2 | "He never did." | | 3 | "He'd told her once that" | | 4 | "She dropped her bag behind" | | 5 | "He set the glass down" | | 6 | "he repeated, in the tone" | | 7 | "She was three sips in" | | 8 | "It was the walk." | | 9 | "You could change a face," | | 10 | "Eva Hollander came in the" | | 11 | "The difference was that now" | | 12 | "She'd cut her hair —" | | 13 | "Her coat was cashmere." | | 14 | "Her shoes had done nothing" | | 15 | "She got four steps in" | | 16 | "Neither of them moved." | | 17 | "Silas, in the way of" | | 18 | "A smile flickered, uncertain of" | | 19 | "Eva sat down without asking," |
| | ratio | 1 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 5 | | matches | | 0 | "Silas, in the way of a man who had spent thirty years learning when to be furniture, drifted toward the far end of the bar and began an unnecessary inventory of…" | | 1 | "She hung her coat on the hook under the bar as if she'd known the hook was there." | | 2 | "Rory considered the honest answer, which was that in April of that year she had still been checking the reflection in shop windows for a man who drove a grey As…" | | 3 | "She considered the other honest answer, which was worse: that she had been afraid Eva would look at the life she'd built — a rented room above a chip pan, a del…" | | 4 | "Eva picked up her wine and drank half of it, which was the least Zurich thing she'd done since she walked in." |
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| 81.52% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 2 | | matches | | 0 | "She turned, a quarter turn, then back" | | 1 | "Eva said, not looking at her" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 2 | | fancyTags | | 0 | "he repeated (repeat)" | | 1 | "She pressed (press)" |
| | dialogueSentences | 63 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0.143 | | effectiveRatio | 0.063 | |