| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.257 | | leniency | 0.514 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2103 | | 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) | |
| 76.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2103 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "weight" | | 1 | "gleaming" | | 2 | "stomach" | | 3 | "footsteps" | | 4 | "quickened" | | 5 | "traced" | | 6 | "trembled" | | 7 | "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 | 1 | | narrationSentences | 248 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 5 | | narrationSentences | 248 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 274 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 60 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2096 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 92 | | wordCount | 1946 | | uniqueNames | 24 | | maxNameDensity | 1.85 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Wardour | 2 | | Street | 2 | | Harlow | 1 | | Quinn | 36 | | Tomás | 1 | | Herrera | 28 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Dalston | 1 | | Bermondsey | 1 | | Brixton | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Cambridge | 1 | | Circus | 1 | | Underground | 1 | | Morris | 4 | | Wapping | 1 | | Harls | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Christopher" | | 5 | "Morris" |
| | places | | 0 | "Wardour" | | 1 | "Street" | | 2 | "Raven" | | 3 | "Soho" | | 4 | "Dalston" | | 5 | "Bermondsey" | | 6 | "Brixton" | | 7 | "Charing" | | 8 | "Cross" | | 9 | "Road" | | 10 | "Cambridge" | | 11 | "Underground" | | 12 | "Wapping" | | 13 | "Market" |
| | globalScore | 0.575 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 147 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.477 | | wordCount | 2096 | | matches | | 0 | "not with fear of arrest, she thought, but fear" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 274 | | matches | | 0 | "knew that scar" | | 1 | "fear that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 148 | | mean | 14.16 | | std | 14.36 | | cv | 1.014 | | sampleLengths | | 0 | 12 | | 1 | 16 | | 2 | 10 | | 3 | 46 | | 4 | 39 | | 5 | 5 | | 6 | 2 | | 7 | 5 | | 8 | 23 | | 9 | 79 | | 10 | 8 | | 11 | 28 | | 12 | 3 | | 13 | 2 | | 14 | 22 | | 15 | 1 | | 16 | 44 | | 17 | 5 | | 18 | 23 | | 19 | 2 | | 20 | 8 | | 21 | 2 | | 22 | 24 | | 23 | 4 | | 24 | 31 | | 25 | 7 | | 26 | 35 | | 27 | 12 | | 28 | 2 | | 29 | 46 | | 30 | 38 | | 31 | 3 | | 32 | 5 | | 33 | 10 | | 34 | 29 | | 35 | 10 | | 36 | 4 | | 37 | 48 | | 38 | 10 | | 39 | 3 | | 40 | 22 | | 41 | 8 | | 42 | 5 | | 43 | 3 | | 44 | 41 | | 45 | 6 | | 46 | 30 | | 47 | 7 | | 48 | 3 | | 49 | 3 |
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| 99.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 248 | | matches | | 0 | "been found" | | 1 | "was gone" | | 2 | "been removed" | | 3 | "been carved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 363 | | matches | | 0 | "was going" | | 1 | "was already moving" |
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| 69.86% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 2 | | flaggedSentences | 7 | | totalSentences | 274 | | ratio | 0.026 | | matches | | 0 | "It had appeared in three lots of CCTV gathered around three separate crime scenes: a bedsit in Dalston where a man had bled enough to die but walked out before officers arrived; a warehouse in Bermondsey where human tissue had been found embedded in the ceiling; and the abandoned surgery in Brixton where six units of stolen blood had vanished." | | 1 | "Quinn heard the subtle change in the rain before she saw him move—the scrape of his sole against wet stone, weight shifting to the ball of his foot." | | 2 | "Warm brown eyes, wide now—not with fear of arrest, she thought, but fear that she might keep up." | | 3 | "The air changed as she descended—warmer, stale with dust and brakes, carrying the iron smell of the tunnels." | | 4 | "Herrera used every obstruction—a suitcase dragged into the aisle, a cluster of tourists, a man with crutches." | | 5 | "The air smelled of wet stone and something sweeter beneath it—incense, perhaps, or spoiled fruit." | | 6 | "People—or shapes wearing people’s outlines—moved between stalls laden with bottles, cages, bundles of herbs, trays of gleaming teeth." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1957 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.017373530914665303 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.0035769034236075624 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 274 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 274 | | mean | 7.65 | | std | 5.83 | | cv | 0.762 | | sampleLengths | | 0 | 12 | | 1 | 16 | | 2 | 5 | | 3 | 5 | | 4 | 6 | | 5 | 22 | | 6 | 5 | | 7 | 13 | | 8 | 8 | | 9 | 3 | | 10 | 5 | | 11 | 14 | | 12 | 9 | | 13 | 5 | | 14 | 2 | | 15 | 5 | | 16 | 8 | | 17 | 15 | | 18 | 4 | | 19 | 10 | | 20 | 5 | | 21 | 60 | | 22 | 5 | | 23 | 2 | | 24 | 1 | | 25 | 28 | | 26 | 3 | | 27 | 2 | | 28 | 9 | | 29 | 13 | | 30 | 1 | | 31 | 12 | | 32 | 4 | | 33 | 16 | | 34 | 12 | | 35 | 5 | | 36 | 23 | | 37 | 2 | | 38 | 6 | | 39 | 2 | | 40 | 2 | | 41 | 5 | | 42 | 11 | | 43 | 8 | | 44 | 4 | | 45 | 8 | | 46 | 6 | | 47 | 6 | | 48 | 11 | | 49 | 7 |
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| 53.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3467153284671533 | | totalSentences | 274 | | uniqueOpeners | 95 | |
| 93.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 214 | | matches | | 0 | "Then the bus cleared and" | | 1 | "Then he pulled the emergency" | | 2 | "Somewhere behind, passengers shouted." | | 3 | "Then she saw Herrera’s dark" | | 4 | "Then her beam caught the" | | 5 | "Then the old tiles shivered." |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 44 | | totalSentences | 214 | | matches | | 0 | "His short curls shone with" | | 1 | "She glanced past him into" | | 2 | "She knew the bag too." | | 3 | "It had appeared in three" | | 4 | "He cut across Wardour Street" | | 5 | "She reached for her radio." | | 6 | "She checked the display without" | | 7 | "She increased her pace." | | 8 | "He glanced over his shoulder." | | 9 | "He shoved through two laughing" | | 10 | "It filled the night with" | | 11 | "Her shoes struck standing water." | | 12 | "He ignored the crossings." | | 13 | "She cleared the barrier and" | | 14 | "He did not." | | 15 | "He looked at her with" | | 16 | "Her mind offered procedure in" | | 17 | "Her right foot landed on" | | 18 | "She pitched forward, one hand" | | 19 | "She pressed the radio close" |
| | ratio | 0.206 | |
| 60.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 171 | | totalSentences | 214 | | matches | | 0 | "Rain silvered Wardour Street and" | | 1 | "Detective Harlow Quinn stood beneath" | | 2 | "His short curls shone with" | | 3 | "Water tracked down his olive" | | 4 | "Saint Christopher, patron of travellers." | | 5 | "She glanced past him into" | | 6 | "The barman polished a glass" | | 7 | "Herrera’s grip tightened on the" | | 8 | "The motion pulled his wet" | | 9 | "Quinn knew that scar." | | 10 | "Knife attack, eighteen months ago," | | 11 | "She knew the bag too." | | 12 | "It had appeared in three" | | 13 | "Herrera looked at the pavement." | | 14 | "Quinn heard the subtle change" | | 15 | "The bag clipped her hip" | | 16 | "Quinn struck the wall, caught" | | 17 | "He cut across Wardour Street" | | 18 | "The taxi’s horn blared." | | 19 | "Quinn vaulted the bonnet, coat" |
| | ratio | 0.799 | |
| 46.73% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 214 | | matches | | 0 | "To the left, blackness." | | 1 | "To the right, a weak" |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 76 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 64.29% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 3 | | fancyTags | | 0 | "she snapped (snap)" | | 1 | "She pressed (press)" | | 2 | "he whispered (whisper)" |
| | dialogueSentences | 35 | | tagDensity | 0.257 | | leniency | 0.514 | | rawRatio | 0.333 | | effectiveRatio | 0.171 | |