| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 22 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said sharply [sharply]" |
| | dialogueSentences | 140 | | tagDensity | 0.157 | | leniency | 0.314 | | rawRatio | 0.045 | | effectiveRatio | 0.014 | |
| 88.02% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2505 | | totalAiIsmAdverbs | 6 | | found | | 0 | | | 1 | | | 2 | | adverb | "reluctantly" | | count | 1 |
| | 3 | | | 4 | | | 5 | |
| | highlights | | 0 | "gently" | | 1 | "really" | | 2 | "reluctantly" | | 3 | "carefully" | | 4 | "slowly" | | 5 | "sharply" |
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| 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) | |
| 88.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2505 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "velvet" | | 1 | "familiar" | | 2 | "silence" | | 3 | "weight" | | 4 | "flicked" |
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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 | 245 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 245 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 363 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2505 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 30 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 95 | | wordCount | 1798 | | uniqueNames | 9 | | maxNameDensity | 2.06 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Camden | 1 | | Sergeant | 1 | | Bell | 19 | | Quinn | 37 | | Venn | 4 | | Eva | 15 | | Morris | 6 | | Limehouse | 1 | | Rusk | 11 |
| | persons | | 0 | "Sergeant" | | 1 | "Bell" | | 2 | "Quinn" | | 3 | "Eva" | | 4 | "Morris" | | 5 | "Rusk" |
| | places | | | globalScore | 0.471 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 136 | | glossingSentenceCount | 2 | | matches | | 0 | "uare of glass, apparently intended to keep th" | | 1 | "seemed solid" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.798 | | wordCount | 2505 | | matches | | 0 | "not a confession, not the whole of it, but the shape of the thing" | | 1 | "not the whole of it, but the shape of the thing" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 363 | | matches | | 0 | "removed that phrase" | | 1 | "knew that shape" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 255 | | mean | 9.82 | | std | 9.98 | | cv | 1.016 | | sampleLengths | | 0 | 6 | | 1 | 39 | | 2 | 7 | | 3 | 15 | | 4 | 5 | | 5 | 3 | | 6 | 30 | | 7 | 4 | | 8 | 16 | | 9 | 1 | | 10 | 6 | | 11 | 34 | | 12 | 5 | | 13 | 11 | | 14 | 1 | | 15 | 10 | | 16 | 3 | | 17 | 7 | | 18 | 48 | | 19 | 27 | | 20 | 6 | | 21 | 4 | | 22 | 1 | | 23 | 5 | | 24 | 6 | | 25 | 23 | | 26 | 48 | | 27 | 3 | | 28 | 6 | | 29 | 14 | | 30 | 5 | | 31 | 54 | | 32 | 8 | | 33 | 2 | | 34 | 1 | | 35 | 12 | | 36 | 1 | | 37 | 26 | | 38 | 18 | | 39 | 3 | | 40 | 8 | | 41 | 5 | | 42 | 17 | | 43 | 4 | | 44 | 18 | | 45 | 39 | | 46 | 7 | | 47 | 4 | | 48 | 41 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 245 | | matches | | 0 | "been drilled" | | 1 | "were polished" | | 2 | "was tiled" | | 3 | "been scuffed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 310 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 363 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1802 | | adjectiveStacks | 2 | | stackExamples | | 0 | "small red-haired woman" | | 1 | "lay slick against his" |
| | adverbCount | 49 | | adverbRatio | 0.02719200887902331 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.007214206437291898 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 363 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 363 | | mean | 6.9 | | std | 4.96 | | cv | 0.719 | | sampleLengths | | 0 | 6 | | 1 | 8 | | 2 | 9 | | 3 | 22 | | 4 | 7 | | 5 | 15 | | 6 | 5 | | 7 | 3 | | 8 | 4 | | 9 | 15 | | 10 | 3 | | 11 | 8 | | 12 | 4 | | 13 | 16 | | 14 | 1 | | 15 | 6 | | 16 | 11 | | 17 | 16 | | 18 | 7 | | 19 | 5 | | 20 | 9 | | 21 | 2 | | 22 | 1 | | 23 | 10 | | 24 | 3 | | 25 | 7 | | 26 | 6 | | 27 | 6 | | 28 | 17 | | 29 | 8 | | 30 | 11 | | 31 | 4 | | 32 | 5 | | 33 | 18 | | 34 | 6 | | 35 | 4 | | 36 | 1 | | 37 | 5 | | 38 | 6 | | 39 | 5 | | 40 | 9 | | 41 | 9 | | 42 | 15 | | 43 | 14 | | 44 | 7 | | 45 | 12 | | 46 | 3 | | 47 | 4 | | 48 | 2 | | 49 | 5 |
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| 53.72% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.33884297520661155 | | totalSentences | 363 | | uniqueOpeners | 123 | |
| 95.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 210 | | matches | | 0 | "Usually it made the scene" | | 1 | "Then she turned back to" | | 2 | "Then his shoulders dropped." | | 3 | "Then air moved across her" | | 4 | "Then she shifted her hand." | | 5 | "Then stepped through a wall" |
| | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 55 | | totalSentences | 210 | | matches | | 0 | "he said, holding up a" | | 1 | "She had seen enough bone" | | 2 | "Her bare feet didn’t reach" | | 3 | "They watched her with the" | | 4 | "He had been working with" | | 5 | "He knew when a question" | | 6 | "Her glasses had slipped down" | | 7 | "She pushed them up, then" | | 8 | "Its wired-glass pane was painted" | | 9 | "His right hand rested on" | | 10 | "His left lay palm-up on" | | 11 | "She put on gloves." | | 12 | "She would leave conclusions to" | | 13 | "His collar was damp." | | 14 | "She moved her torch along" | | 15 | "It ran down from the" | | 16 | "He stepped out." | | 17 | "They all ran towards the" | | 18 | "She looked under the desk." | | 19 | "She stood and examined the" |
| | ratio | 0.262 | |
| 88.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 156 | | totalSentences | 210 | | matches | | 0 | "The station wasn’t on the" | | 1 | "Quinn checked twice before putting" | | 2 | "Detective Sergeant Bell waited at" | | 3 | "he said, holding up a" | | 4 | "Quinn took the disc." | | 5 | "A hole had been drilled" | | 6 | "She had seen enough bone" | | 7 | "A woman in a stained" | | 8 | "Her bare feet didn’t reach" | | 9 | "Quinn showed her the token." | | 10 | "The woman looked at it," | | 11 | "the woman repeated, and moved" | | 12 | "Quinn went through." | | 13 | "The abandoned platform had become" | | 14 | "Canvas awnings hung from rusted" | | 15 | "Lamps burned blue inside glass" | | 16 | "Something under a tarpaulin knocked" | | 17 | "A good many of them." | | 18 | "They watched her with the" | | 19 | "Bell gave her a look." |
| | ratio | 0.743 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 210 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 61 | | technicalSentenceCount | 1 | | matches | | 0 | "Below, a flight of tiled steps descended into warm air that smelled of wet wool, hot copper, and something sweet going rotten." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 22 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 22 | | fancyCount | 1 | | fancyTags | | 0 | "the woman repeated (repeat)" |
| | dialogueSentences | 140 | | tagDensity | 0.157 | | leniency | 0.314 | | rawRatio | 0.045 | | effectiveRatio | 0.014 | |