| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "She gestured vaguely [vaguely]" |
| | dialogueSentences | 53 | | tagDensity | 0.358 | | leniency | 0.717 | | rawRatio | 0.053 | | effectiveRatio | 0.038 | |
| 92.36% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1309 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 80.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1309 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "weight" | | 1 | "unreadable" | | 2 | "flicker" | | 3 | "silence" | | 4 | "absolutely" |
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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 | 68 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 68 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 72 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 21 | | totalWords | 1319 | | ratio | 0.016 | | matches | | 0 | "get on the train, Rory, just get on the bloody train" | | 1 | "hello, sorry, I built a whole life without mentioning it?" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 889 | | uniqueNames | 14 | | maxNameDensity | 1.8 | | worstName | "Eva" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Rory | 13 | | Raven | 1 | | Nest | 1 | | Silas | 3 | | Tuesday | 1 | | Berlin | 1 | | Vienna | 1 | | Prague | 1 | | Novak | 1 | | Eva | 16 | | Cardiff | 1 | | Singapore | 1 | | Zurich | 1 | | Czech | 1 |
| | persons | | 0 | "Rory" | | 1 | "Silas" | | 2 | "Novak" | | 3 | "Eva" |
| | places | | 0 | "Raven" | | 1 | "Berlin" | | 2 | "Vienna" | | 3 | "Prague" | | 4 | "Cardiff" | | 5 | "Singapore" | | 6 | "Zurich" | | 7 | "Czech" |
| | globalScore | 0.6 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1319 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 101 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 25.86 | | std | 27.17 | | cv | 1.051 | | sampleLengths | | 0 | 72 | | 1 | 94 | | 2 | 15 | | 3 | 64 | | 4 | 3 | | 5 | 13 | | 6 | 111 | | 7 | 3 | | 8 | 31 | | 9 | 16 | | 10 | 28 | | 11 | 3 | | 12 | 5 | | 13 | 26 | | 14 | 20 | | 15 | 33 | | 16 | 2 | | 17 | 2 | | 18 | 19 | | 19 | 4 | | 20 | 23 | | 21 | 31 | | 22 | 8 | | 23 | 100 | | 24 | 8 | | 25 | 12 | | 26 | 3 | | 27 | 63 | | 28 | 2 | | 29 | 12 | | 30 | 59 | | 31 | 66 | | 32 | 11 | | 33 | 7 | | 34 | 61 | | 35 | 26 | | 36 | 27 | | 37 | 10 | | 38 | 4 | | 39 | 3 | | 40 | 44 | | 41 | 6 | | 42 | 14 | | 43 | 51 | | 44 | 6 | | 45 | 1 | | 46 | 14 | | 47 | 10 | | 48 | 31 | | 49 | 32 |
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| 94.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 68 | | matches | | 0 | "been paid" | | 1 | "been sanded" |
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| 63.95% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 147 | | matches | | 0 | "wasn't drinking" | | 1 | "was holding" | | 2 | "was still getting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 1 | | flaggedSentences | 7 | | totalSentences | 101 | | ratio | 0.069 | | matches | | 0 | "The maps on the wall had curled a little more at the corners since spring, and the black-and-white photographs — Berlin, Vienna, someone's dead uncle on a fishing boat — hung crooked as ever." | | 1 | "The woman on the stool wore a coat that had cost more than Rory's flat deposit — proper wool, the kind that hangs." | | 2 | "There it was — a flicker of the old face, something rueful and quick, gone almost before it registered." | | 3 | "\"God.\" Eva laughed — and it wasn't the car alarm." | | 4 | "She didn't drink much these days; she'd stopped around the time she'd stopped needing to soften the edges of herself for anyone." | | 5 | "And for a moment — the length of a held breath — the expensive stillness of her face broke open and there was the girl in the bus shelter under it, wet-haired and furious on someone else's behalf, seventeen years old and absolutely certain that love meant showing up." | | 6 | "Rory looked at her — the coat, the hair, the six missing years lying between them on the bar like something neither of them wanted to be the one to pick up." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 885 | | adjectiveStacks | 1 | | stackExamples | | 0 | "smooth, expensive, unreadable face" |
| | adverbCount | 27 | | adverbRatio | 0.030508474576271188 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007909604519774011 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 13.06 | | std | 12.37 | | cv | 0.947 | | sampleLengths | | 0 | 44 | | 1 | 9 | | 2 | 4 | | 3 | 15 | | 4 | 8 | | 5 | 23 | | 6 | 34 | | 7 | 5 | | 8 | 24 | | 9 | 15 | | 10 | 8 | | 11 | 23 | | 12 | 3 | | 13 | 3 | | 14 | 27 | | 15 | 3 | | 16 | 4 | | 17 | 9 | | 18 | 17 | | 19 | 11 | | 20 | 23 | | 21 | 8 | | 22 | 14 | | 23 | 38 | | 24 | 3 | | 25 | 7 | | 26 | 6 | | 27 | 18 | | 28 | 9 | | 29 | 7 | | 30 | 17 | | 31 | 8 | | 32 | 3 | | 33 | 3 | | 34 | 5 | | 35 | 4 | | 36 | 19 | | 37 | 3 | | 38 | 3 | | 39 | 8 | | 40 | 9 | | 41 | 17 | | 42 | 16 | | 43 | 2 | | 44 | 2 | | 45 | 13 | | 46 | 6 | | 47 | 4 | | 48 | 23 | | 49 | 10 |
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| 49.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.36633663366336633 | | totalSentences | 101 | | uniqueOpeners | 37 | |
| 57.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 58 | | matches | | 0 | "Then they turned, and it" |
| | ratio | 0.017 | |
| 95.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 58 | | matches | | 0 | "She had grown into a" | | 1 | "She could hear the thud" | | 2 | "She set the bag on" | | 3 | "She knew them before she" | | 4 | "She'd had two coats her" | | 5 | "Her nails were the colour" | | 6 | "Her hair had been cut" | | 7 | "She'd put on weight in" | | 8 | "It should have been an" | | 9 | "She put her hands flat" | | 10 | "She reached for a glass" | | 11 | "It had been sanded down" | | 12 | "She poured herself a soda" | | 13 | "She didn't drink much these" | | 14 | "She said it too fast" | | 15 | "She gestured vaguely at the" | | 16 | "She had learned, somewhere between" | | 17 | "She looked at her own" |
| | ratio | 0.31 | |
| 37.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 58 | | matches | | 0 | "The green neon buzzed like" | | 1 | "That was the kind of" | | 2 | "She had grown into a" | | 3 | "The bar was a Tuesday" | | 4 | "The maps on the wall" | | 5 | "Silas was down the cellar." | | 6 | "She could hear the thud" | | 7 | "She set the bag on" | | 8 | "She knew them before she" | | 9 | "That was the strange part," | | 10 | "A set of shoulders on" | | 11 | "The shoulders went still." | | 12 | "Eva Novak had been a" | | 13 | "She'd had two coats her" | | 14 | "The woman on the stool" | | 15 | "Her nails were the colour" | | 16 | "Her hair had been cut" | | 17 | "She'd put on weight in" | | 18 | "Neither of them moved for" | | 19 | "It should have been an" |
| | ratio | 0.845 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 60.44% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 3 | | matches | | 0 | "She had grown into a person who kept a mental ledger of other people's forgetting." | | 1 | "A set of shoulders on a stool, hunched forward in that specific apologetic curve, as though the world were a low doorway you had to duck through." | | 2 | "She'd put on weight in the good way, the way that comes from eating three meals and sleeping through the night, and she had the smooth, expensive, unreadable fa…" |
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| 98.68% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva turned, a quarter turn, then another" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 53 | | tagDensity | 0.189 | | leniency | 0.377 | | rawRatio | 0 | | effectiveRatio | 0 | |