| 57.14% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 2 | | adverbTags | | 0 | "Quinn repeated flatly [flatly]" | | 1 | "Eva said quietly [quietly]" |
| | dialogueSentences | 25 | | tagDensity | 0.56 | | leniency | 1 | | rawRatio | 0.143 | | effectiveRatio | 0.143 | |
| 73.46% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 942 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "precisely" | | 1 | "carefully" | | 2 | "suddenly" | | 3 | "really" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 52.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 942 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "familiar" | | 1 | "echoed" | | 2 | "intricate" | | 3 | "perfect" | | 4 | "echoes" | | 5 | "standard" | | 6 | "flickered" | | 7 | "tension" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 55 | | matches | (empty) | |
| 90.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 55 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 65 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 59 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 947 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 26.63% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 689 | | uniqueNames | 12 | | maxNameDensity | 2.47 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 17 | | Tube | 1 | | London | 1 | | Victorian | 1 | | Dr | 1 | | Chen | 2 | | Eva | 10 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Morris | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Dr" | | 3 | "Chen" | | 4 | "Eva" | | 5 | "Kowalski" | | 6 | "Morris" |
| | places | | | globalScore | 0.266 | | windowScore | 0.5 | |
| 36.36% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like he'd decided to take a nap in" | | 1 | "felt like she might be close to underst" |
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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 | 947 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 65 | | matches | | 0 | "authorized that arrangement" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 35.07 | | std | 17.84 | | cv | 0.509 | | sampleLengths | | 0 | 74 | | 1 | 10 | | 2 | 33 | | 3 | 26 | | 4 | 20 | | 5 | 20 | | 6 | 65 | | 7 | 47 | | 8 | 34 | | 9 | 11 | | 10 | 36 | | 11 | 40 | | 12 | 19 | | 13 | 13 | | 14 | 25 | | 15 | 48 | | 16 | 59 | | 17 | 31 | | 18 | 26 | | 19 | 28 | | 20 | 26 | | 21 | 60 | | 22 | 55 | | 23 | 45 | | 24 | 26 | | 25 | 60 | | 26 | 10 |
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| 92.50% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 55 | | matches | | 0 | "been sealed" | | 1 | "was covered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 125 | | matches | | 0 | "was really investigating" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 65 | | ratio | 0.062 | | matches | | 0 | "The platform showed signs of recent use - scuff marks on the tiles, modern graffiti layered over decades-old tags." | | 1 | "She fumbled in her satchel, producing a small brass compass that looked antique but somehow wrong - too bright in places, too tarnished in others." | | 2 | "When the lights stabilized, there was only the empty station - but now she noticed the subtle patterns worn into the floor, exactly the width of vendor booths." | | 3 | "In three years of investigating Morris's disappearance, she'd accumulated a file full of similar scenes - unexplainable deaths, impossible evidence, and always these strange symbols lurking at the edges." |
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| 85.64% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 690 | | adjectiveStacks | 1 | | stackExamples | | 0 | "over decades-old tags." |
| | adverbCount | 34 | | adverbRatio | 0.04927536231884058 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.020289855072463767 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 65 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 65 | | mean | 14.57 | | std | 10.39 | | cv | 0.713 | | sampleLengths | | 0 | 15 | | 1 | 17 | | 2 | 13 | | 3 | 6 | | 4 | 23 | | 5 | 8 | | 6 | 2 | | 7 | 6 | | 8 | 2 | | 9 | 25 | | 10 | 8 | | 11 | 8 | | 12 | 10 | | 13 | 13 | | 14 | 7 | | 15 | 20 | | 16 | 10 | | 17 | 8 | | 18 | 19 | | 19 | 8 | | 20 | 20 | | 21 | 8 | | 22 | 15 | | 23 | 24 | | 24 | 34 | | 25 | 11 | | 26 | 11 | | 27 | 25 | | 28 | 4 | | 29 | 25 | | 30 | 11 | | 31 | 9 | | 32 | 10 | | 33 | 8 | | 34 | 5 | | 35 | 14 | | 36 | 11 | | 37 | 3 | | 38 | 45 | | 39 | 12 | | 40 | 38 | | 41 | 9 | | 42 | 5 | | 43 | 17 | | 44 | 9 | | 45 | 15 | | 46 | 11 | | 47 | 12 | | 48 | 16 | | 49 | 4 |
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| 84.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5230769230769231 | | totalSentences | 65 | | uniqueOpeners | 34 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 52 | | matches | | 0 | "Just a man in an" | | 1 | "Just like she'd noticed how" |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 7 | | totalSentences | 52 | | matches | | 0 | "She gestured to the far" | | 1 | "She fumbled in her satchel," | | 2 | "Its face was covered in" | | 3 | "She pointed to what Quinn" | | 4 | "She fixed the researcher with" | | 5 | "She paused, looking back" | | 6 | "She just wasn't sure she" |
| | ratio | 0.135 | |
| 56.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 42 | | totalSentences | 52 | | matches | | 0 | "Detective Harlow Quinn crouched beside" | | 1 | "The abandoned Tube platform hadn't" | | 2 | "The victim lay spread-eagled on" | | 3 | "Quinn checked her worn leather" | | 4 | "The fluorescent work lights the" | | 5 | "Chen, the ME, snapping off" | | 6 | "Quinn finished, her jaw tightening" | | 7 | "The words were becoming too" | | 8 | "Chen gathered her equipment, clearly" | | 9 | "Quinn stood, her knees protesting," | | 10 | "Something about this place needled" | | 11 | "The platform showed signs of" | | 12 | "The access tunnel they'd used" | | 13 | "A familiar voice echoed across" | | 14 | "Eva Kowalski hurried toward her," | | 15 | "The British Museum researcher had" | | 16 | "Quinn's tone carried a warning," | | 17 | "Quinn hadn't called anyone, but" | | 18 | "She gestured to the far" | | 19 | "Eva's green eyes widened." |
| | ratio | 0.808 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 52 | | matches | (empty) | | ratio | 0 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 2 | | matches | | 0 | "Just a man in an expensive suit who looked like he'd decided to take a nap in a forgotten corner of London's underground." | | 1 | "She fumbled in her satchel, producing a small brass compass that looked antique but somehow wrong - too bright in places, too tarnished in others." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn finished, her jaw tightening" | | 1 | "Eva continued, her freckled face intense" |
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| 30.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 3 | | fancyTags | | 0 | "Eva murmured (murmur)" | | 1 | "Eva continued (continue)" | | 2 | "Quinn repeated flatly (repeat)" |
| | dialogueSentences | 25 | | tagDensity | 0.24 | | leniency | 0.48 | | rawRatio | 0.5 | | effectiveRatio | 0.24 | |