| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.294 | | leniency | 0.588 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1734 | | totalAiIsmAdverbs | 3 | | 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) | |
| 71.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1734 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "electric" | | 1 | "glint" | | 2 | "weight" | | 3 | "footsteps" | | 4 | "flickered" | | 5 | "familiar" | | 6 | "velvet" | | 7 | "pulsed" | | 8 | "scanned" | | 9 | "measured" |
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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 | 197 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 197 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 208 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 15 | | totalWords | 1734 | | ratio | 0.009 | | matches | | 0 | "Flat 4, Peckham. Unlocks once." | | 1 | "Mother's jewellery box, never opened." | | 2 | "The door you dream about." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1625 | | uniqueNames | 32 | | maxNameDensity | 0.8 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Herrera | 7 | | Camden | 4 | | High | 1 | | Street | 1 | | Electric | 1 | | Ballroom | 1 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Tottenham | 1 | | Court | 1 | | Road | 2 | | Tomás | 3 | | Kentish | 2 | | Town | 2 | | Northern | 1 | | Ten | 1 | | Darkness | 1 | | Met | 2 | | Morris | 4 | | Deptford | 1 | | August | 2 | | Tube | 1 | | Veil | 2 | | Market | 2 | | Barbour | 1 | | Quinn | 13 | | Brixton | 1 | | Peckham | 1 | | Silas | 1 | | Sergeant | 1 | | Daniel | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Nest" | | 2 | "Tomás" | | 3 | "Darkness" | | 4 | "Morris" | | 5 | "Market" | | 6 | "Quinn" | | 7 | "Silas" | | 8 | "Sergeant" | | 9 | "Daniel" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Electric" | | 4 | "Soho" | | 5 | "Raven" | | 6 | "Tottenham" | | 7 | "Court" | | 8 | "Road" | | 9 | "Kentish" | | 10 | "Town" | | 11 | "Deptford" | | 12 | "August" | | 13 | "Barbour" | | 14 | "Brixton" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 110 | | glossingSentenceCount | 2 | | matches | | 0 | "spiral that seemed to turn when she tilted it towards the streetlight" | | 1 | "looked like people" |
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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 | 1734 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 208 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 70 | | mean | 24.77 | | std | 21.75 | | cv | 0.878 | | sampleLengths | | 0 | 11 | | 1 | 65 | | 2 | 51 | | 3 | 3 | | 4 | 5 | | 5 | 31 | | 6 | 76 | | 7 | 3 | | 8 | 25 | | 9 | 3 | | 10 | 52 | | 11 | 8 | | 12 | 4 | | 13 | 32 | | 14 | 4 | | 15 | 35 | | 16 | 11 | | 17 | 21 | | 18 | 16 | | 19 | 5 | | 20 | 14 | | 21 | 23 | | 22 | 7 | | 23 | 1 | | 24 | 55 | | 25 | 3 | | 26 | 61 | | 27 | 13 | | 28 | 11 | | 29 | 57 | | 30 | 7 | | 31 | 8 | | 32 | 45 | | 33 | 48 | | 34 | 40 | | 35 | 54 | | 36 | 11 | | 37 | 19 | | 38 | 29 | | 39 | 29 | | 40 | 5 | | 41 | 3 | | 42 | 75 | | 43 | 8 | | 44 | 29 | | 45 | 9 | | 46 | 3 | | 47 | 7 | | 48 | 36 | | 49 | 86 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 197 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 256 | | matches | | 0 | "was chasing" | | 1 | "was lying" | | 2 | "were reading" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 208 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1633 | | adjectiveStacks | 1 | | stackExamples | | 0 | "tight, used parked cars" |
| | adverbCount | 29 | | adverbRatio | 0.017758726270667484 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.003061849357011635 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 208 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 208 | | mean | 8.34 | | std | 6.87 | | cv | 0.824 | | sampleLengths | | 0 | 11 | | 1 | 8 | | 2 | 4 | | 3 | 26 | | 4 | 2 | | 5 | 2 | | 6 | 23 | | 7 | 6 | | 8 | 22 | | 9 | 2 | | 10 | 21 | | 11 | 3 | | 12 | 2 | | 13 | 3 | | 14 | 13 | | 15 | 7 | | 16 | 7 | | 17 | 4 | | 18 | 8 | | 19 | 28 | | 20 | 5 | | 21 | 5 | | 22 | 13 | | 23 | 3 | | 24 | 6 | | 25 | 8 | | 26 | 3 | | 27 | 7 | | 28 | 2 | | 29 | 6 | | 30 | 3 | | 31 | 6 | | 32 | 1 | | 33 | 3 | | 34 | 22 | | 35 | 3 | | 36 | 6 | | 37 | 4 | | 38 | 4 | | 39 | 13 | | 40 | 8 | | 41 | 4 | | 42 | 5 | | 43 | 8 | | 44 | 19 | | 45 | 4 | | 46 | 2 | | 47 | 7 | | 48 | 4 | | 49 | 22 |
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| 77.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.4975845410628019 | | totalSentences | 207 | | uniqueOpeners | 103 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 167 | | matches | | 0 | "Then he'd spotted her reflection" | | 1 | "Then he stopped." | | 2 | "Then he was gone, footsteps" | | 3 | "Somewhere far down, a faint" | | 4 | "Then the figure stepped aside" | | 5 | "Somewhere down the tracks, far" |
| | ratio | 0.036 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 167 | | matches | | 0 | "He never glanced back." | | 1 | "He cut corners tight, used" | | 2 | "She had followed him from" | | 3 | "They never did." | | 4 | "Her warrant card stayed in" | | 5 | "Her shoes slapped through puddles." | | 6 | "Her lungs burned, and she" | | 7 | "She had chased faster men" | | 8 | "He veered left onto Kentish" | | 9 | "He stood in front of" | | 10 | "She kept her hands loose" | | 11 | "His chest heaved, and his" | | 12 | "She took another step" | | 13 | "He smiled, and it didn't" | | 14 | "She watched his feet, not" | | 15 | "His weight shifted back." | | 16 | "He pulled the door wider" | | 17 | "He ducked inside." | | 18 | "Her fingers caught the sleeve" | | 19 | "His forearm came up to" |
| | ratio | 0.251 | |
| 88.74% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 124 | | totalSentences | 167 | | matches | | 0 | "Tomás Herrera ran like a" | | 1 | "Quinn clocked it within the" | | 2 | "He never glanced back." | | 3 | "He cut corners tight, used" | | 4 | "Paramedics learned to move through" | | 5 | "She had followed him from" | | 6 | "They never did." | | 7 | "A bouncer in a puffer" | | 8 | "Quinn shouldered past him without" | | 9 | "Her warrant card stayed in" | | 10 | "Rain slicked the pavement into" | | 11 | "Every shopfront bled colour across" | | 12 | "Her shoes slapped through puddles." | | 13 | "Her lungs burned, and she" | | 14 | "She had chased faster men" | | 15 | "He veered left onto Kentish" | | 16 | "Quinn pushed harder." | | 17 | "The gap closed to twenty" | | 18 | "He stood in front of" | | 19 | "Quinn knew it." |
| | ratio | 0.743 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 167 | | matches | (empty) | | ratio | 0 | |
| 92.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 4 | | matches | | 0 | "Two hours outside the Raven's Nest, watching that green neon sign buzz above the door, waiting for him to come out alone." | | 1 | "Wait for a dog unit and a search team and a sergeant who would ask why a detective inspector was chasing an unlicensed paramedic through Camden at midnight with…" | | 2 | "The hood tilted, as if something inside it were reading her face and finding it interesting." | | 3 | "Two teenagers in school blazers sniffed a tray of powders that shifted colour under their noses." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.059 | | leniency | 0.118 | | rawRatio | 0 | | effectiveRatio | 0 | |