| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 152 | | tagDensity | 0.112 | | leniency | 0.224 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2399 | | totalAiIsmAdverbs | 2 | | 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) | |
| 91.66% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2399 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "intricate" | | 2 | "silence" | | 3 | "trembled" |
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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 | 209 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 209 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 344 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2399 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 34 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 87 | | wordCount | 1586 | | uniqueNames | 8 | | maxNameDensity | 2.4 | | worstName | "Quinn" | | maxWindowNameDensity | 6 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 2 | | Quinn | 38 | | Bell | 20 | | Vale | 5 | | Eva | 16 | | Morris | 2 | | Blood | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Bell" | | 3 | "Vale" | | 4 | "Eva" | | 5 | "Morris" | | 6 | "Blood" |
| | places | (empty) | | globalScore | 0.302 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 122 | | glossingSentenceCount | 1 | | matches | | 0 | "something like that once before" |
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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 | 2399 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 344 | | matches | | 0 | "let that sit" | | 1 | "back that he" | | 2 | "did that much" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 243 | | mean | 9.87 | | std | 10.33 | | cv | 1.046 | | sampleLengths | | 0 | 11 | | 1 | 43 | | 2 | 8 | | 3 | 7 | | 4 | 28 | | 5 | 5 | | 6 | 41 | | 7 | 11 | | 8 | 5 | | 9 | 9 | | 10 | 6 | | 11 | 8 | | 12 | 33 | | 13 | 6 | | 14 | 74 | | 15 | 3 | | 16 | 11 | | 17 | 1 | | 18 | 2 | | 19 | 3 | | 20 | 6 | | 21 | 4 | | 22 | 4 | | 23 | 6 | | 24 | 47 | | 25 | 42 | | 26 | 8 | | 27 | 4 | | 28 | 11 | | 29 | 1 | | 30 | 11 | | 31 | 5 | | 32 | 4 | | 33 | 1 | | 34 | 20 | | 35 | 2 | | 36 | 31 | | 37 | 25 | | 38 | 6 | | 39 | 2 | | 40 | 7 | | 41 | 10 | | 42 | 4 | | 43 | 22 | | 44 | 34 | | 45 | 3 | | 46 | 5 | | 47 | 3 | | 48 | 9 | | 49 | 37 |
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| 95.19% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 209 | | matches | | 0 | "was furred" | | 1 | "was tucked" | | 2 | "been made" | | 3 | "was tiled" | | 4 | "were scuffed" | | 5 | "were curled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 292 | | matches | | 0 | "was packing" | | 1 | "were disappearing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 344 | | ratio | 0.006 | | matches | | 0 | "Her left wrist brushed the handrail; she pulled it away, protecting the worn leather strap of her watch from a smear of something sticky and black." | | 1 | "A partition enclosed it from the platform; a narrow door stood open, its split frame exposing fresh wood." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1591 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 43 | | adverbRatio | 0.02702702702702703 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.006285355122564425 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 344 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 344 | | mean | 6.97 | | std | 5.05 | | cv | 0.725 | | sampleLengths | | 0 | 11 | | 1 | 6 | | 2 | 27 | | 3 | 10 | | 4 | 8 | | 5 | 7 | | 6 | 14 | | 7 | 14 | | 8 | 5 | | 9 | 11 | | 10 | 7 | | 11 | 23 | | 12 | 1 | | 13 | 10 | | 14 | 5 | | 15 | 5 | | 16 | 4 | | 17 | 6 | | 18 | 8 | | 19 | 7 | | 20 | 26 | | 21 | 6 | | 22 | 17 | | 23 | 9 | | 24 | 13 | | 25 | 16 | | 26 | 6 | | 27 | 10 | | 28 | 3 | | 29 | 3 | | 30 | 11 | | 31 | 1 | | 32 | 2 | | 33 | 3 | | 34 | 6 | | 35 | 4 | | 36 | 4 | | 37 | 6 | | 38 | 16 | | 39 | 18 | | 40 | 13 | | 41 | 18 | | 42 | 14 | | 43 | 4 | | 44 | 6 | | 45 | 8 | | 46 | 4 | | 47 | 11 | | 48 | 1 | | 49 | 11 |
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| 62.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3808139534883721 | | totalSentences | 344 | | uniqueOpeners | 131 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 183 | | matches | | 0 | "Then the gate had opened." | | 1 | "Immediately behind Vale, however, a" | | 2 | "Simply free of the grime" | | 3 | "Afterwards, the caretaker had changed" | | 4 | "Then she stood at the" | | 5 | "Then came a thin metallic" |
| | ratio | 0.033 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 183 | | matches | | 0 | "She stopped at the foot" | | 1 | "She had felt the grain" | | 2 | "Her left wrist brushed the" | | 3 | "It still steamed." | | 4 | "He handed her overshoes and" | | 5 | "He was in his late" | | 6 | "She moved the torch beam" | | 7 | "Its handle was ivory-coloured, perhaps" | | 8 | "She stepped carefully around the" | | 9 | "Its lower edge stopped six" | | 10 | "They had flattened there in" | | 11 | "She lowered the torch." | | 12 | "He came to the threshold." | | 13 | "She would also want an" | | 14 | "It was no larger than" | | 15 | "She was small, with curly" | | 16 | "she said before Quinn asked" | | 17 | "She ignored him." | | 18 | "She kept her voice level." | | 19 | "She glanced at Eva’s satchel." |
| | ratio | 0.175 | |
| 80.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 139 | | totalSentences | 183 | | matches | | 0 | "The dead man lay beneath" | | 1 | "Someone had polished the sign" | | 2 | "Everything else in the abandoned" | | 3 | "She stopped at the foot" | | 4 | "DS Bell stood beside a" | | 5 | "Halfway down the stairs, a" | | 6 | "The constable waiting there had" | | 7 | "She had felt the grain" | | 8 | "Bell glanced towards the stairs." | | 9 | "Quinn went down the last" | | 10 | "Her left wrist brushed the" | | 11 | "The platform had become a" | | 12 | "Stalls crowded both edges, built" | | 13 | "Canvas roofs sagged beneath the" | | 14 | "A butcher’s scale hung above" | | 15 | "Curtains closed most of the" | | 16 | "Someone had left a cup" | | 17 | "It still steamed." | | 18 | "Quinn looked at him." | | 19 | "He handed her overshoes and" |
| | ratio | 0.76 | |
| 54.64% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 183 | | matches | | 0 | "Because it was the only" | | 1 | "Now the needle in Bell’s" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 152 | | tagDensity | 0.112 | | leniency | 0.224 | | rawRatio | 0 | | effectiveRatio | 0 | |