| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.545 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1189 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 87.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1189 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "flicker" | | 1 | "warmth" | | 2 | "flickered" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "the air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 66 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 66 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | 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 | 5 | | markdownWords | 19 | | totalWords | 1204 | | ratio | 0.016 | | matches | | 0 | "police" | | 1 | "alive" | | 2 | "closing it" | | 3 | "there are things down there, Harlow, things that don't want to be found" | | 4 | "down there." |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1118 | | uniqueNames | 14 | | maxNameDensity | 0.63 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 2 | | Quinn | 7 | | Herrera | 7 | | Brewer | 1 | | Street | 1 | | Raven | 2 | | Nest | 2 | | Morris | 4 | | Tube | 1 | | London | 1 | | Seville | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Brewer" | | 1 | "Street" | | 2 | "London" | | 3 | "Seville" |
| | globalScore | 1 | | windowScore | 1 | |
| 33.72% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 2 | | matches | | 0 | "something like ozone, like the air before a" | | 1 | "something like burnt sugar, something like c" | | 2 | "something like copper, something like the in" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.831 | | wordCount | 1204 | | matches | | 0 | "not with the fear of a man about to be arrested but with something worse" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 71 | | matches | | 0 | "understand that desperation" |
| |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 40.13 | | std | 27.2 | | cv | 0.678 | | sampleLengths | | 0 | 23 | | 1 | 67 | | 2 | 53 | | 3 | 30 | | 4 | 4 | | 5 | 78 | | 6 | 65 | | 7 | 2 | | 8 | 49 | | 9 | 16 | | 10 | 58 | | 11 | 19 | | 12 | 8 | | 13 | 88 | | 14 | 87 | | 15 | 43 | | 16 | 49 | | 17 | 30 | | 18 | 40 | | 19 | 41 | | 20 | 106 | | 21 | 6 | | 22 | 21 | | 23 | 54 | | 24 | 36 | | 25 | 5 | | 26 | 16 | | 27 | 16 | | 28 | 61 | | 29 | 33 |
| |
| 94.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 66 | | matches | | 0 | "was flattened" | | 1 | "been allowed" |
| |
| 51.02% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 179 | | matches | | 0 | "was beginning" | | 1 | "was running" | | 2 | "was looking" | | 3 | "was not going" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 71 | | ratio | 0.127 | | matches | | 0 | "But something about the way he'd looked over his shoulder — twice, in ten seconds — had her out of the car before she'd finished the thought." | | 1 | "She was fit — she made sure of that, ran four miles most mornings before the sun was up — but Herrera had youth on his side, and desperation, and she was beginning to understand that desperation counted for more than fitness ever did." | | 2 | "In eighteen years, nobody had ever stopped just because she'd said the word *police*, not the ones with something to hide, and Herrera had something to hide, she was sure of it now, had been sure of it since Morris —" | | 3 | "Then, ahead, light — a low golden flicker, like lanterns, like a hundred candles guttering in a draft." | | 4 | "An old Tube platform, disused, the kind sealed off decades ago and forgotten by everyone except transit historians and urban explorers — except this one was not forgotten." | | 5 | "Quinn's eighteen years of training told her to catalogue, to observe, to build a picture she could take back and put into a report — but her mind kept sliding off the details like water off glass." | | 6 | "A child — surely a child — with eyes the flat silver of old coins." | | 7 | "\"You shouldn't have followed me down here, Detective,\" he said, and his voice didn't carry the accent she'd expected from his file — it was flattened by years in London, but underneath it she could still hear Seville, could still hear the warmth." | | 8 | "Morris's face, after, gone the color of old paper, and the words he'd said to her before the ambulance took him — *there are things down there, Harlow, things that don't want to be found* — words she'd told herself for three years were the ravings of a dying man's oxygen-starved brain." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1110 | | adjectiveStacks | 1 | | stackExamples | | 0 | "ordinary rain-soaked world" |
| | adverbCount | 30 | | adverbRatio | 0.02702702702702703 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002702702702702703 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 16.96 | | std | 14.92 | | cv | 0.88 | | sampleLengths | | 0 | 23 | | 1 | 35 | | 2 | 32 | | 3 | 4 | | 4 | 22 | | 5 | 27 | | 6 | 30 | | 7 | 4 | | 8 | 31 | | 9 | 3 | | 10 | 44 | | 11 | 16 | | 12 | 49 | | 13 | 2 | | 14 | 3 | | 15 | 5 | | 16 | 41 | | 17 | 9 | | 18 | 2 | | 19 | 5 | | 20 | 33 | | 21 | 25 | | 22 | 1 | | 23 | 18 | | 24 | 8 | | 25 | 8 | | 26 | 28 | | 27 | 4 | | 28 | 48 | | 29 | 5 | | 30 | 4 | | 31 | 37 | | 32 | 16 | | 33 | 10 | | 34 | 15 | | 35 | 4 | | 36 | 35 | | 37 | 2 | | 38 | 2 | | 39 | 43 | | 40 | 6 | | 41 | 14 | | 42 | 6 | | 43 | 4 | | 44 | 6 | | 45 | 21 | | 46 | 19 | | 47 | 3 | | 48 | 25 | | 49 | 3 |
| |
| 84.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5492957746478874 | | totalSentences | 71 | | uniqueOpeners | 39 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 60 | | matches | | 0 | "Almost told herself it was" | | 1 | "Of course he didn't stop." | | 2 | "Then, ahead, light — a" | | 3 | "Somewhere close, a bell rang," |
| | ratio | 0.067 | |
| 80.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 60 | | matches | | 0 | "She almost hadn't followed." | | 1 | "she called, and her voice" | | 2 | "She went after him." | | 3 | "She was fit — she" | | 4 | "He cut down an alley" | | 5 | "She followed anyway, her hand" | | 6 | "He didn't stop." | | 7 | "She shoved the thought down" | | 8 | "She followed him down, her" | | 9 | "She stopped at the bottom" | | 10 | "He stood twenty feet ahead" | | 11 | "he said, and his voice" | | 12 | "Her voice came out steadier" | | 13 | "Her hand was on her" | | 14 | "He glanced past her shoulder," | | 15 | "She thought of Morris." | | 16 | "He'd said *down there.*" | | 17 | "She thought of her warrant" | | 18 | "She thought of Morris's face." | | 19 | "she said, and her voice" |
| | ratio | 0.35 | |
| 68.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 60 | | matches | | 0 | "Rain had been coming down" | | 1 | "Tomás Herrera moved fast for" | | 2 | "Quinn had been watching the" | | 3 | "She almost hadn't followed." | | 4 | "she called, and her voice" | | 5 | "She went after him." | | 6 | "Soho at night was a" | | 7 | "Quinn's lungs burned." | | 8 | "She was fit — she" | | 9 | "He cut down an alley" | | 10 | "She followed anyway, her hand" | | 11 | "He didn't stop." | | 12 | "She shoved the thought down" | | 13 | "Herrera ducked down a set" | | 14 | "She followed him down, her" | | 15 | "She stopped at the bottom" | | 16 | "An old Tube platform, disused," | | 17 | "This one was *alive*." | | 18 | "Stalls lined the platform where" | | 19 | "People moved between the stalls." |
| | ratio | 0.783 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 60 | | matches | | 0 | "Before her stretched something that" | | 1 | "If they were people." |
| | ratio | 0.033 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 8 | | matches | | 0 | "She followed anyway, her hand brushing the outline of her badge in her coat pocket like a talisman, and came out the other side into a narrower street that smel…" | | 1 | "Herrera ducked down a set of stairs that shouldn't have been there, stairs that led below street level into what she'd have sworn, walking past in daylight, was…" | | 2 | "She followed him down, her hand finding the cold iron railing, and the sound of the rain dropped away behind her like a curtain falling." | | 3 | "He stood twenty feet ahead of her, chest heaving, rainwater dripping off his curls, and he was looking at her not with the fear of a man about to be arrested bu…" | | 4 | "Three years gone, and the file still open, still unexplained, a case she'd never been allowed to close because *closing it* would have meant writing down things…" | | 5 | "Morris's face, after, gone the color of old paper, and the words he'd said to her before the ambulance took him — *there are things down there, Harlow, things t…" | | 6 | "Herrera closed his eyes for one long moment, as if she'd confirmed some private, terrible prophecy." | | 7 | "Quinn followed him onto the platform, into the light, into the market that moved with the moon, and behind her the stairs to the street swallowed the last gray …" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0 | | effectiveRatio | 0 | |