| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 21 | | tagDensity | 0.571 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 80.30% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1015 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "carefully" | | 2 | "lightly" |
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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) | |
| 35.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1015 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "churn" | | 1 | "chaotic" | | 2 | "tapestry" | | 3 | "complex" | | 4 | "tracing" | | 5 | "intricate" | | 6 | "flicker" | | 7 | "pristine" | | 8 | "etched" | | 9 | "chill" | | 10 | "maw" | | 11 | "oppressive" | | 12 | "profound" |
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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 | 99 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 99 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 109 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1006 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 868 | | uniqueNames | 9 | | maxNameDensity | 0.92 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 8 | | Constable | 1 | | Ayres | 6 | | London | 1 | | Thorne | 4 | | Morris | 2 | | Tube | 1 | | Superintendent | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Constable" | | 3 | "Ayres" | | 4 | "Thorne" | | 5 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | 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 | 1006 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 109 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 34.69 | | std | 22.89 | | cv | 0.66 | | sampleLengths | | 0 | 47 | | 1 | 37 | | 2 | 51 | | 3 | 34 | | 4 | 62 | | 5 | 9 | | 6 | 14 | | 7 | 72 | | 8 | 45 | | 9 | 4 | | 10 | 15 | | 11 | 41 | | 12 | 86 | | 13 | 11 | | 14 | 7 | | 15 | 59 | | 16 | 58 | | 17 | 40 | | 18 | 59 | | 19 | 34 | | 20 | 7 | | 21 | 2 | | 22 | 14 | | 23 | 62 | | 24 | 12 | | 25 | 7 | | 26 | 39 | | 27 | 41 | | 28 | 37 |
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| 94.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 99 | | matches | | 0 | "were positioned" | | 1 | "were layered" | | 2 | "were scuffed" |
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| 10.87% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 141 | | matches | | 0 | "were looking" | | 1 | "was reading" | | 2 | "were blinking" | | 3 | "were getting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 109 | | ratio | 0.009 | | matches | | 0 | "The scrubbed symbol wasn't graffiti; it was a signpost, hastily erased." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 530 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.013207547169811321 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.005660377358490566 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 109 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 109 | | mean | 9.23 | | std | 4.99 | | cv | 0.541 | | sampleLengths | | 0 | 16 | | 1 | 12 | | 2 | 19 | | 3 | 12 | | 4 | 15 | | 5 | 10 | | 6 | 5 | | 7 | 18 | | 8 | 4 | | 9 | 12 | | 10 | 12 | | 11 | 13 | | 12 | 21 | | 13 | 14 | | 14 | 17 | | 15 | 5 | | 16 | 15 | | 17 | 9 | | 18 | 1 | | 19 | 1 | | 20 | 9 | | 21 | 6 | | 22 | 8 | | 23 | 3 | | 24 | 5 | | 25 | 4 | | 26 | 2 | | 27 | 12 | | 28 | 10 | | 29 | 9 | | 30 | 11 | | 31 | 10 | | 32 | 6 | | 33 | 16 | | 34 | 9 | | 35 | 15 | | 36 | 5 | | 37 | 4 | | 38 | 5 | | 39 | 10 | | 40 | 4 | | 41 | 4 | | 42 | 16 | | 43 | 14 | | 44 | 2 | | 45 | 1 | | 46 | 10 | | 47 | 10 | | 48 | 9 | | 49 | 15 |
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| 48.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3577981651376147 | | totalSentences | 109 | | uniqueOpeners | 39 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 49.21% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 89 | | matches | | 0 | "She ignored the uniformed constable" | | 1 | "His eagerness was a sharp," | | 2 | "His bespoke suit was immaculate," | | 3 | "It was more than odd." | | 4 | "It was a mistake." | | 5 | "You don’t leave a twenty-thousand-pound" | | 6 | "You leave it if you" | | 7 | "She ran her gloved finger" | | 8 | "It was a cloying, heavy" | | 9 | "It tickled the back of" | | 10 | "They were clean, manicured." | | 11 | "They were positioned with an" | | 12 | "She shifted her attention from" | | 13 | "Her gaze lingered on a" | | 14 | "It was not a gang" | | 15 | "she asked, pointing her torch" | | 16 | "She moved closer, tracing the" | | 17 | "He called them" | | 18 | "It was clean, simple, and" | | 19 | "They were paragraphs in a" |
| | ratio | 0.427 | |
| 10.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 89 | | matches | | 0 | "The generator’s churn did little" | | 1 | "Detective Harlow Quinn’s boots crunched" | | 2 | "She ignored the uniformed constable" | | 3 | "Detective Constable Ayres gestured with" | | 4 | "His eagerness was a sharp," | | 5 | "Quinn’s gaze swept the scene." | | 6 | "The body lay near the" | | 7 | "Pockets turned inside out." | | 8 | "A missing wallet and phone," | | 9 | "The textbook image of a" | | 10 | "Ayres continued, reading from his" | | 11 | "Quinn knelt beside the body," | | 12 | "Thorne’s face was placid, his" | | 13 | "His bespoke suit was immaculate," | | 14 | "A stiletto, the ME had" | | 15 | "Quinn said, her voice flat" | | 16 | "Quinn’s eyes narrowed." | | 17 | "It was more than odd." | | 18 | "It was a mistake." | | 19 | "You don’t leave a twenty-thousand-pound" |
| | ratio | 0.899 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 1 | | matches | | 0 | "The bone was cold, a deep, unnatural chill that had nothing to do with the station’s ambient temperature." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 3 | | matches | | 0 | "Quinn said, her voice flat" | | 1 | "Ayres leaned, curious" | | 2 | "she said, her voice low" |
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| 54.76% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "Ayres continued (continue)" | | 1 | "He chuckled (chuckle)" |
| | dialogueSentences | 21 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.333 | | effectiveRatio | 0.19 | |