| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.245 | | leniency | 0.49 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1417 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 82.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1417 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "fluttered" | | 1 | "pristine" | | 2 | "furrowed" | | 3 | "pulse" | | 4 | "crystal" |
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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 | 97 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 97 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1430 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 62.66% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 916 | | uniqueNames | 10 | | maxNameDensity | 1.75 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 16 | | London | 1 | | Tube | 1 | | Camden | 1 | | City | 1 | | Eva | 8 | | Morris | 2 | | Hargrove | 8 | | Oxford | 1 | | Sap | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Camden" | | 2 | "Eva" | | 3 | "Morris" | | 4 | "Hargrove" |
| | places | | | globalScore | 0.627 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 1 | | matches | | 0 | "as though guarding its contents" |
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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 | 1430 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 25.54 | | std | 21.74 | | cv | 0.851 | | sampleLengths | | 0 | 67 | | 1 | 45 | | 2 | 14 | | 3 | 9 | | 4 | 4 | | 5 | 16 | | 6 | 46 | | 7 | 70 | | 8 | 3 | | 9 | 56 | | 10 | 3 | | 11 | 23 | | 12 | 5 | | 13 | 29 | | 14 | 46 | | 15 | 3 | | 16 | 52 | | 17 | 12 | | 18 | 45 | | 19 | 5 | | 20 | 15 | | 21 | 50 | | 22 | 73 | | 23 | 4 | | 24 | 16 | | 25 | 57 | | 26 | 4 | | 27 | 5 | | 28 | 55 | | 29 | 3 | | 30 | 68 | | 31 | 4 | | 32 | 5 | | 33 | 36 | | 34 | 26 | | 35 | 11 | | 36 | 55 | | 37 | 9 | | 38 | 20 | | 39 | 18 | | 40 | 4 | | 41 | 60 | | 42 | 10 | | 43 | 12 | | 44 | 30 | | 45 | 8 | | 46 | 54 | | 47 | 11 | | 48 | 11 | | 49 | 30 |
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| 79.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 97 | | matches | | 0 | "been closed" | | 1 | "been pulled" | | 2 | "been found" | | 3 | "been disturbed " | | 4 | "been dragged" | | 5 | "been barricaded" | | 6 | "been pried" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 156 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 134 | | ratio | 0.052 | | matches | | 0 | "The air tasted of rust and something else — something acrid that coated the back of her throat." | | 1 | "Her boots splashed through shallow water that shouldn't have been there — the pumps had failed decades ago, but this much moisture suggested recent flooding." | | 2 | "But his expression — his mouth hung open in a silent scream, eyes fixed wide, pupils dilated to the point where no iris remained visible." | | 3 | "The tiles bore markings she hadn't noticed on her way in — scratches that formed patterns." | | 4 | "His shoes — polished Oxford brogues — had no scuff marks." | | 5 | "The standing water had been disturbed — boot prints from the forensics team, Hargrove's heavier impressions." | | 6 | "Beneath the surface, something moved — a faint tracery of black lines spreading from the centre of his chest outward, following the paths of his veins." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 874 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.013729977116704805 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006864988558352402 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 10.67 | | std | 8.53 | | cv | 0.8 | | sampleLengths | | 0 | 16 | | 1 | 16 | | 2 | 18 | | 3 | 17 | | 4 | 20 | | 5 | 10 | | 6 | 15 | | 7 | 5 | | 8 | 9 | | 9 | 9 | | 10 | 4 | | 11 | 16 | | 12 | 3 | | 13 | 15 | | 14 | 25 | | 15 | 3 | | 16 | 31 | | 17 | 9 | | 18 | 3 | | 19 | 2 | | 20 | 25 | | 21 | 3 | | 22 | 18 | | 23 | 9 | | 24 | 9 | | 25 | 20 | | 26 | 3 | | 27 | 14 | | 28 | 9 | | 29 | 5 | | 30 | 8 | | 31 | 21 | | 32 | 4 | | 33 | 16 | | 34 | 1 | | 35 | 16 | | 36 | 9 | | 37 | 3 | | 38 | 14 | | 39 | 19 | | 40 | 7 | | 41 | 12 | | 42 | 6 | | 43 | 6 | | 44 | 45 | | 45 | 5 | | 46 | 7 | | 47 | 8 | | 48 | 12 | | 49 | 20 |
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| 69.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.44029850746268656 | | totalSentences | 134 | | uniqueOpeners | 59 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 85 | | matches | | 0 | "Her torch cut through the" | | 1 | "She adjusted the beam toward" | | 2 | "She strode past him toward" | | 3 | "Her boots splashed through shallow" | | 4 | "His skin had taken on" | | 5 | "She held a worn leather" | | 6 | "She hopped down from the" | | 7 | "She'd seen similar markings three" | | 8 | "Its casing had gone green" | | 9 | "He trudged through the shallow" | | 10 | "He stopped beside the body" | | 11 | "His fingernails showed none of" | | 12 | "His shoes — polished Oxford" | | 13 | "She turned one foot slightly" | | 14 | "She followed them with her" | | 15 | "She held up her hand" | | 16 | "Her compass needle had steadied," | | 17 | "She looked at Quinn with" | | 18 | "She waited until Hargrove turned" | | 19 | "His hand went to his" |
| | ratio | 0.294 | |
| 71.76% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 85 | | matches | | 0 | "The rain hammered the cement" | | 1 | "Her torch cut through the" | | 2 | "The air tasted of rust" | | 3 | "She adjusted the beam toward" | | 4 | "Quinn ducked under the tape." | | 5 | "A uniformed officer she didn't" | | 6 | "Quinn's jaw tightened." | | 7 | "She strode past him toward" | | 8 | "Her boots splashed through shallow" | | 9 | "The sheet had been pulled" | | 10 | "His skin had taken on" | | 11 | "Quinn turned to find a" | | 12 | "She held a worn leather" | | 13 | "She hopped down from the" | | 14 | "Eva tucked a curl behind" | | 15 | "Quinn raised her torch." | | 16 | "The tiles bore markings she" | | 17 | "She'd seen similar markings three" | | 18 | "The memory lodged in her" | | 19 | "Eva reached into her satchel" |
| | ratio | 0.776 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 4 | | matches | | 0 | "Her boots splashed through shallow water that shouldn't have been there — the pumps had failed decades ago, but this much moisture suggested recent flooding." | | 1 | "The sheet had been pulled back to reveal a man in his mid-thirties, dressed in a tailored charcoal suit that belonged on a City banker rather than an abandoned …" | | 2 | "She held a worn leather satchel across her lap, one hand resting on its flap as though guarding its contents." | | 3 | "A barrel of a man with a florid complexion and the kind of moustache that belonged in a gentlemen's club." |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva said, her voice steady" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 49 | | tagDensity | 0.041 | | leniency | 0.082 | | rawRatio | 0.5 | | effectiveRatio | 0.041 | |