| 46.15% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "she shouted again [again]" |
| | dialogueSentences | 13 | | tagDensity | 0.462 | | leniency | 0.923 | | rawRatio | 0.167 | | effectiveRatio | 0.154 | |
| 89.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1372 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "suddenly" | | 1 | "quickly" | | 2 | "slightly" |
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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) | |
| 63.56% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1372 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "database" | | 1 | "pulse" | | 2 | "streaming" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "pulsed" | | 6 | "velvet" | | 7 | "scanned" | | 8 | "glint" | | 9 | "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 | 96 | | matches | (empty) | |
| 83.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 103 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1393 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1255 | | uniqueNames | 16 | | maxNameDensity | 0.72 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 9 | | Raven | 1 | | Nest | 1 | | Herrera | 8 | | Brewer | 1 | | Street | 2 | | Berwick | 1 | | Morris | 5 | | Camden | 1 | | Tube | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 2 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Brewer" | | 3 | "Street" | | 4 | "Berwick" | | 5 | "Camden" | | 6 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 74.24% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like the same thing" | | 1 | "velvet that seemed to drink the light" |
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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 | 1393 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 103 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 37.65 | | std | 25.39 | | cv | 0.674 | | sampleLengths | | 0 | 31 | | 1 | 89 | | 2 | 4 | | 3 | 49 | | 4 | 29 | | 5 | 7 | | 6 | 67 | | 7 | 57 | | 8 | 10 | | 9 | 63 | | 10 | 70 | | 11 | 14 | | 12 | 60 | | 13 | 16 | | 14 | 32 | | 15 | 34 | | 16 | 5 | | 17 | 76 | | 18 | 16 | | 19 | 10 | | 20 | 104 | | 21 | 28 | | 22 | 39 | | 23 | 39 | | 24 | 33 | | 25 | 58 | | 26 | 19 | | 27 | 32 | | 28 | 37 | | 29 | 21 | | 30 | 68 | | 31 | 42 | | 32 | 59 | | 33 | 49 | | 34 | 4 | | 35 | 1 | | 36 | 21 |
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| 97.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 96 | | matches | | 0 | "were shuttered" | | 1 | "been opened" |
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| 17.35% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 219 | | matches | | 0 | "was gaining" | | 1 | "were sweating" | | 2 | "was haggling" | | 3 | "was talking" | | 4 | "was watching" | | 5 | "was watching" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 19 | | semicolonCount | 0 | | flaggedSentences | 13 | | totalSentences | 103 | | ratio | 0.126 | | matches | | 0 | "Tomás Herrera — she had his file memorized." | | 1 | "Just a glance held a half-second too long as he passed her car, his warm brown eyes finding hers through the rain-streaked windshield, and then he was moving — fast, bag clutched to his chest, cutting left down an alley off Brewer Street." | | 2 | "Her worn leather watch — Morris's watch, the one she'd never taken off — ticked against her wrist like a second heartbeat." | | 3 | "He veered north, toward Camden, and she lost him for thirty seconds at the junction — long enough for her pulse to spike, her hand going to her radio before she thought better of it." | | 4 | "Except the fence had a gap in it tonight — a gap she was suddenly certain had been opened from the inside — and Herrera slipped through it like water through a crack." | | 5 | "Morris was dead, and the file on his death had so many redactions it read like a crossword puzzle, and nobody — nobody — would tell her why." | | 6 | "The tiled walls were sweating, ancient adverts peeling away in damp curls, and somewhere below she could hear Herrera's footsteps echoing — and something else." | | 7 | "She drew her sidearm — off the books, like everything else tonight — and kept going." | | 8 | "A woman with no irises — white from lid to lid — turned her head as Quinn passed, and smiled." | | 9 | "The people here — if they were people — hadn't reacted to her." | | 10 | "She was out of her jurisdiction in every sense — geographic, procedural, and something deeper she didn't have a word for." | | 11 | "He closed his bag, said something to the gray figure, and started walking toward her — hands raised slightly, palms open, the way a man approaches a frightened animal." | | 12 | "\"This place is the least of your problems.\" His eyes flicked past her, toward the stairs, and something in his expression shifted — the particular stillness of a paramedic reading a scene." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1241 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.029814665592264304 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.009669621273166801 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 103 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 103 | | mean | 13.52 | | std | 10.89 | | cv | 0.806 | | sampleLengths | | 0 | 31 | | 1 | 27 | | 2 | 8 | | 3 | 17 | | 4 | 14 | | 5 | 9 | | 6 | 14 | | 7 | 4 | | 8 | 6 | | 9 | 43 | | 10 | 24 | | 11 | 5 | | 12 | 3 | | 13 | 4 | | 14 | 27 | | 15 | 25 | | 16 | 15 | | 17 | 3 | | 18 | 4 | | 19 | 25 | | 20 | 3 | | 21 | 22 | | 22 | 5 | | 23 | 5 | | 24 | 3 | | 25 | 3 | | 26 | 9 | | 27 | 18 | | 28 | 30 | | 29 | 35 | | 30 | 5 | | 31 | 30 | | 32 | 14 | | 33 | 7 | | 34 | 9 | | 35 | 11 | | 36 | 33 | | 37 | 16 | | 38 | 7 | | 39 | 15 | | 40 | 9 | | 41 | 1 | | 42 | 6 | | 43 | 28 | | 44 | 5 | | 45 | 9 | | 46 | 17 | | 47 | 8 | | 48 | 25 | | 49 | 1 |
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| 58.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.44660194174757284 | | totalSentences | 103 | | uniqueOpeners | 46 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 88 | | matches | | 0 | "Just a glance held a" | | 1 | "Of course he didn't." | | 2 | "Somewhere a man was haggling" | | 3 | "Then the figure in gray" |
| | ratio | 0.045 | |
| 56.36% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 88 | | matches | | 0 | "She'd been sitting in her" | | 1 | "She'd watched him go into" | | 2 | "She hadn't even done anything" | | 3 | "He didn't stop." | | 4 | "She scrambled over them, kept" | | 5 | "He was fast." | | 6 | "She'd give him that." | | 7 | "Her lungs burned." | | 8 | "Her worn leather watch —" | | 9 | "she shouted again" | | 10 | "She wanted answers." | | 11 | "She wanted to know what" | | 12 | "She wanted to know why" | | 13 | "She wanted to know what" | | 14 | "He veered north, toward Camden," | | 15 | "She caught sight of him" | | 16 | "She'd walked past it a" | | 17 | "She went through the fence." | | 18 | "She knew that immediately, with" | | 19 | "She drew her sidearm —" |
| | ratio | 0.409 | |
| 73.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 88 | | matches | | 0 | "The rain came down in" | | 1 | "She'd been sitting in her" | | 2 | "Tomás Herrera — she had" | | 3 | "She'd watched him go into" | | 4 | "Tonight, he'd made her." | | 5 | "She hadn't even done anything" | | 6 | "Quinn was out of the" | | 7 | "He didn't stop." | | 8 | "The alley was narrow, stinking" | | 9 | "She scrambled over them, kept" | | 10 | "The market stalls were shuttered," | | 11 | "He was fast." | | 12 | "She'd give him that." | | 13 | "Her lungs burned." | | 14 | "Her worn leather watch —" | | 15 | "she shouted again" | | 16 | "A lie, mostly." | | 17 | "She wanted answers." | | 18 | "She wanted to know what" | | 19 | "She wanted to know why" |
| | ratio | 0.773 | |
| 56.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 88 | | matches | | 0 | "If she vanished down here," |
| | ratio | 0.011 | |
| 47.62% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 6 | | matches | | 0 | "Quinn was out of the car before she made a conscious decision, her boots hitting the pavement, eighteen years of training overriding everything else." | | 1 | "Explain she was off-shift, running surveillance on a case that wasn't hers, chasing a man into the rain on a hunch her superintendent had already told her to dr…" | | 2 | "Quinn slowed at the fence line, rain streaming off her closely cropped hair, her chest heaving." | | 3 | "She knew that immediately, with the cold animal certainty that lived at the base of her spine." | | 4 | "The tiled walls were sweating, ancient adverts peeling away in damp curls, and somewhere below she could hear Herrera's footsteps echoing — and something else." | | 5 | "She looked past him at the impossible market, the pulsing jars, the stalls that shouldn't exist beneath a city she'd policed her entire adult life." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 73.08% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "she shouted again (shout)" |
| | dialogueSentences | 13 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0.25 | | effectiveRatio | 0.154 | |