| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.615 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1512 | | totalAiIsmAdverbs | 1 | | 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) | |
| 83.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1512 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "footsteps" | | 1 | "gleaming" | | 2 | "searing" | | 3 | "flickered" | | 4 | "echoed" |
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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 | 103 | | matches | | |
| 73.51% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 103 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 108 | | 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 | 0 | | markdownWords | 0 | | totalWords | 1526 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1390 | | uniqueNames | 28 | | maxNameDensity | 0.5 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Road" | | discoveredNames | | Raven | 1 | | Nest | 4 | | Old | 2 | | Compton | 1 | | Street | 3 | | Harlow | 1 | | Quinn | 7 | | Soho | 1 | | Silas | 1 | | Morris | 4 | | Tomás | 1 | | Herrera | 5 | | Saint | 1 | | Christopher | 1 | | Catholic | 1 | | Dean | 1 | | Charing | 1 | | Cross | 1 | | Road | 3 | | Oxford | 1 | | Tottenham | 1 | | Court | 1 | | Euston | 1 | | Parkway | 1 | | Eighteen | 2 | | Bethnal | 2 | | Green | 2 | | Rain | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Silas" | | 5 | "Morris" | | 6 | "Tomás" | | 7 | "Herrera" | | 8 | "Saint" | | 9 | "Christopher" | | 10 | "Rain" |
| | places | | 0 | "Old" | | 1 | "Compton" | | 2 | "Street" | | 3 | "Soho" | | 4 | "Dean" | | 5 | "Charing" | | 6 | "Cross" | | 7 | "Road" | | 8 | "Oxford" | | 9 | "Tottenham" | | 10 | "Court" | | 11 | "Euston" | | 12 | "Bethnal" |
| | globalScore | 1 | | windowScore | 1 | |
| 75.37% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like the bookshelf" | | 1 | "felt like this" |
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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 | 1526 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 108 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 38.15 | | std | 32.19 | | cv | 0.844 | | sampleLengths | | 0 | 74 | | 1 | 25 | | 2 | 92 | | 3 | 24 | | 4 | 17 | | 5 | 70 | | 6 | 29 | | 7 | 10 | | 8 | 93 | | 9 | 72 | | 10 | 31 | | 11 | 5 | | 12 | 2 | | 13 | 112 | | 14 | 34 | | 15 | 54 | | 16 | 9 | | 17 | 93 | | 18 | 12 | | 19 | 71 | | 20 | 3 | | 21 | 90 | | 22 | 53 | | 23 | 8 | | 24 | 30 | | 25 | 13 | | 26 | 10 | | 27 | 48 | | 28 | 5 | | 29 | 75 | | 30 | 15 | | 31 | 80 | | 32 | 10 | | 33 | 10 | | 34 | 16 | | 35 | 60 | | 36 | 10 | | 37 | 14 | | 38 | 44 | | 39 | 3 |
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| 98.45% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 103 | | matches | | |
| 15.67% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 217 | | matches | | 0 | "was already moving" | | 1 | "wasn't strolling" | | 2 | "was running" | | 3 | "wasn't falling" | | 4 | "was breathing" | | 5 | "was listening" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 1 | | flaggedSentences | 12 | | totalSentences | 108 | | ratio | 0.111 | | matches | | 0 | "She was already moving when he slipped into the alley behind the bar — she'd cut across the street at 11:40 on a hunch that smelled like the bookshelf." | | 1 | "At the junction with the Euston Road a night bus slid between them, lit windows ghosting down the wet street — and in those windows she watched his reflection find hers." | | 2 | "He was twenty-nine and paramedic-fit; she was forty-one and felt all of it in her knees by the second minute." | | 3 | "Behind her, somewhere, the city kept doing what it did — a siren, a dog, a shout — and none of it was for her." | | 4 | "Gone — down." | | 5 | "No backup — she'd called nothing, because you don't call a van for a quiet tail." | | 6 | "And the hole itself — the hole was wrong." | | 7 | "She thought of Morris — of a doorway in Bethnal Green three years ago, rain just like this, and the thing she'd never been able to put in a report: that when Morris went through that door, the dark on the other side had felt like this." | | 8 | "The stairwell stank of wet stone and something else — ozone, incense, searing meat, an animal smell underneath." | | 9 | "He turned then, and looked at her — those warm brown eyes gone bright with something she chose to call anger — and for a moment neither of them breathed." | | 10 | "Herrera was the only thread she had — the Nest, the bookshelf, the satchel, three years of a file with Morris's name on the cover and nothing inside." | | 11 | "Quinn looked at the open way down, at the far-off glow of it, at the smell of the whole impossible world rising to meet her — and made the choice she'd already made on the stairs, the one she'd been making since Bethnal Green." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1386 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.02813852813852814 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.002886002886002886 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 108 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 108 | | mean | 14.13 | | std | 11.77 | | cv | 0.833 | | sampleLengths | | 0 | 20 | | 1 | 54 | | 2 | 2 | | 3 | 5 | | 4 | 1 | | 5 | 17 | | 6 | 26 | | 7 | 5 | | 8 | 15 | | 9 | 17 | | 10 | 29 | | 11 | 3 | | 12 | 4 | | 13 | 17 | | 14 | 17 | | 15 | 29 | | 16 | 41 | | 17 | 2 | | 18 | 8 | | 19 | 19 | | 20 | 10 | | 21 | 3 | | 22 | 5 | | 23 | 39 | | 24 | 24 | | 25 | 6 | | 26 | 16 | | 27 | 15 | | 28 | 3 | | 29 | 22 | | 30 | 4 | | 31 | 28 | | 32 | 31 | | 33 | 3 | | 34 | 2 | | 35 | 2 | | 36 | 6 | | 37 | 22 | | 38 | 20 | | 39 | 30 | | 40 | 6 | | 41 | 3 | | 42 | 25 | | 43 | 23 | | 44 | 4 | | 45 | 4 | | 46 | 3 | | 47 | 27 | | 48 | 8 | | 49 | 2 |
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| 68.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4722222222222222 | | totalSentences | 108 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 91 | | matches | | 0 | "Even winded, his accent turned" | | 1 | "Then he stepped through, and" | | 2 | "Then the turnstile arm lifted." |
| | ratio | 0.033 | |
| 74.95% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 91 | | matches | | 0 | "She checked the time anyway." | | 1 | "She'd been inside the Nest" | | 2 | "She noticed things." | | 3 | "It was the job." | | 4 | "It had been the job" | | 5 | "She was already moving when" | | 6 | "She'd grown up enough Catholic" | | 7 | "She'd grown up enough a" | | 8 | "She gave him half a" | | 9 | "He was good." | | 10 | "He checked himself in shop" | | 11 | "He crossed Charing Cross Road" | | 12 | "He wasn't strolling anymore." | | 13 | "He was running an errand" | | 14 | "He was twenty-nine and paramedic-fit;" | | 15 | "He vaulted a barrier by" | | 16 | "Her radio bounced against her" | | 17 | "Her breath sawed." | | 18 | "He turned off the main" | | 19 | "She found the seam where" |
| | ratio | 0.363 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 91 | | matches | | 0 | "The Raven's Nest burned green" | | 1 | "Detective Harlow Quinn had been" | | 2 | "She checked the time anyway." | | 3 | "She'd been inside the Nest" | | 4 | "A bartender called Silas who'd" | | 5 | "She noticed things." | | 6 | "It was the job." | | 7 | "It had been the job" | | 8 | "She was already moving when" | | 9 | "She'd grown up enough Catholic" | | 10 | "She'd grown up enough a" | | 11 | "She gave him half a" | | 12 | "He was good." | | 13 | "He checked himself in shop" | | 14 | "Quinn hung back, folded herself" | | 15 | "Rain was good for this" | | 16 | "Rain ate footsteps and smeared" | | 17 | "He crossed Charing Cross Road" | | 18 | "He wasn't strolling anymore." | | 19 | "He was running an errand" |
| | ratio | 0.648 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 91 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 2 | | matches | | 0 | "Detective Harlow Quinn had been watching that green for two hours, tucked into the dead doorway of a shuttered optician's, and in two hours the rain had worked …" | | 1 | "A bartender called Silas who'd smiled at her the whole interview like a man pricing a horse." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0 | | effectiveRatio | 0 | |