| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.225 | | leniency | 0.45 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.12% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1269 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 88.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1269 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "weight" | | 1 | "stomach" | | 2 | "silence" |
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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 | 84 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 84 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1256 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 55.66% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 954 | | uniqueNames | 12 | | maxNameDensity | 1.89 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 18 | | Camden | 1 | | Town | 1 | | Okonkwo | 8 | | Sunday | 1 | | Morris | 1 | | Eva | 3 | | Kowalski | 1 | | Yard | 1 | | Simon | 1 | | Larch | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Okonkwo" | | 3 | "Morris" | | 4 | "Eva" | | 5 | "Kowalski" | | 6 | "Simon" | | 7 | "Larch" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "Sunday" | | 3 | "Yard" |
| | globalScore | 0.557 | | windowScore | 0.667 | |
| 71.88% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 2 | | matches | | 0 | "quite any alphabet she'd seen in eighteen years on the force" | | 1 | "looked like the work of a scalpel in an o" |
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| 40.76% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.592 | | wordCount | 1256 | | matches | | 0 | "not just tox, but tissue analysis, stomach contents, bone marrow if she can ma" | | 1 | "not the missing woman but the description Eva had given of the man who'd been helping" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 22.43 | | std | 19.41 | | cv | 0.865 | | sampleLengths | | 0 | 52 | | 1 | 36 | | 2 | 1 | | 3 | 5 | | 4 | 51 | | 5 | 64 | | 6 | 47 | | 7 | 22 | | 8 | 54 | | 9 | 3 | | 10 | 51 | | 11 | 5 | | 12 | 15 | | 13 | 2 | | 14 | 6 | | 15 | 60 | | 16 | 26 | | 17 | 9 | | 18 | 48 | | 19 | 3 | | 20 | 30 | | 21 | 12 | | 22 | 3 | | 23 | 34 | | 24 | 4 | | 25 | 32 | | 26 | 3 | | 27 | 18 | | 28 | 11 | | 29 | 22 | | 30 | 24 | | 31 | 19 | | 32 | 9 | | 33 | 7 | | 34 | 60 | | 35 | 6 | | 36 | 1 | | 37 | 14 | | 38 | 4 | | 39 | 63 | | 40 | 26 | | 41 | 4 | | 42 | 4 | | 43 | 32 | | 44 | 51 | | 45 | 42 | | 46 | 4 | | 47 | 20 | | 48 | 49 | | 49 | 8 |
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| 84.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 84 | | matches | | 0 | "been sealed" | | 1 | "been gutted" | | 2 | "been drawn" | | 3 | "been made" | | 4 | "been closed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 158 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 115 | | ratio | 0.087 | | matches | | 0 | "Her left hand brushed the worn leather band of her watch—an old habit, checking the time when she already knew it was half past two in the morning." | | 1 | "But beneath the modern tags, older markings crawled across the ceramic—sigils she didn't recognize, circles within circles, script that wasn't quite any alphabet she'd seen in eighteen years on the force." | | 2 | "From the platform edge, Quinn could see the wound—a single clean incision across the throat, surgical precision." | | 3 | "Up close, the victim looked young—younger than twenty-seven." | | 4 | "Okonkwo—still on the platform—leaned over." | | 5 | "She'd seen throats cut before—it was always a mess, ragged and violent." | | 6 | "Something about the positioning nagged at her—the arms spread, the palms up." | | 7 | "It was the same feeling she'd had when Morris disappeared—when the evidence had told a story that made no sense, when the official report had been closed too quickly, when she'd started noticing things that weren't supposed to exist." | | 8 | "She'd pressed a photograph into Quinn's hand—a man in his late twenties, dark hair, pale skin, standing in front of a bookshop." | | 9 | "But she'd taken the photograph, and she'd kept it, and now she was standing over a body that matched not the missing woman but the description Eva had given of the man who'd been helping her—a dealer in rare artifacts named Simon Larch." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 968 | | adjectiveStacks | 1 | | stackExamples | | 0 | "damp, mineral-scented air." |
| | adverbCount | 28 | | adverbRatio | 0.028925619834710745 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004132231404958678 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 10.92 | | std | 7.65 | | cv | 0.701 | | sampleLengths | | 0 | 19 | | 1 | 20 | | 2 | 13 | | 3 | 15 | | 4 | 21 | | 5 | 1 | | 6 | 5 | | 7 | 16 | | 8 | 7 | | 9 | 28 | | 10 | 14 | | 11 | 31 | | 12 | 19 | | 13 | 18 | | 14 | 12 | | 15 | 17 | | 16 | 14 | | 17 | 8 | | 18 | 3 | | 19 | 12 | | 20 | 8 | | 21 | 15 | | 22 | 16 | | 23 | 3 | | 24 | 14 | | 25 | 18 | | 26 | 6 | | 27 | 13 | | 28 | 3 | | 29 | 2 | | 30 | 5 | | 31 | 10 | | 32 | 2 | | 33 | 6 | | 34 | 4 | | 35 | 13 | | 36 | 13 | | 37 | 12 | | 38 | 12 | | 39 | 6 | | 40 | 12 | | 41 | 10 | | 42 | 4 | | 43 | 9 | | 44 | 8 | | 45 | 6 | | 46 | 17 | | 47 | 7 | | 48 | 10 | | 49 | 3 |
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| 68.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.4434782608695652 | | totalSentences | 115 | | uniqueOpeners | 51 | |
| 45.05% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 74 | | matches | | 0 | "Instead, the gravel between the" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 74 | | matches | | 0 | "Her left hand brushed the" | | 1 | "She descended the short ladder" | | 2 | "His clothes were ordinary: dark" | | 3 | "She pulled a penlight from" | | 4 | "She'd seen throats cut before—it" | | 5 | "she said, lifting each hand" | | 6 | "She climbed back up and" | | 7 | "She walked the length of" | | 8 | "It was small, no larger" | | 9 | "She pocketed the evidence bag" | | 10 | "She knelt beside the body" | | 11 | "She pointed the penlight" | | 12 | "She'd learned, over three years," | | 13 | "It was the same feeling" | | 14 | "She'd pressed a photograph into" | | 15 | "She'd tucked her hair behind" | | 16 | "It rang four times before" |
| | ratio | 0.23 | |
| 68.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 74 | | matches | | 0 | "Detective Harlow Quinn ducked under" | | 1 | "The disused tube stop had" | | 2 | "The escalator had been gutted" | | 3 | "Quinn's boots rang against the" | | 4 | "Her left hand brushed the" | | 5 | "The platform stretched before her," | | 6 | "The air down here pressed" | | 7 | "A forensic team worked the" | | 8 | "The body lay in the" | | 9 | "Okonkwo said, scrolling again" | | 10 | "Quinn's eyes narrowed." | | 11 | "She descended the short ladder" | | 12 | "His clothes were ordinary: dark" | | 13 | "A body drained of blood" | | 14 | "Quinn crouched, her sharp jaw" | | 15 | "She pulled a penlight from" | | 16 | "Okonkwo—still on the platform—leaned over." | | 17 | "Quinn examined the wound." | | 18 | "The cut was so clean" | | 19 | "The edges of the skin" |
| | ratio | 0.784 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 4 | | matches | | 0 | "The escalator had been gutted years ago, leaving a steep staircase that descended into the dark." | | 1 | "But beneath the modern tags, older markings crawled across the ceramic—sigils she didn't recognize, circles within circles, script that wasn't quite any alphabe…" | | 2 | "It was the same feeling she'd had when Morris disappeared—when the evidence had told a story that made no sense, when the official report had been closed too qu…" | | 3 | "But she'd taken the photograph, and she'd kept it, and now she was standing over a body that matched not the missing woman but the description Eva had given of …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0 | | effectiveRatio | 0 | |