| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.481 | | leniency | 0.963 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1485 | | 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) | |
| 42.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1485 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "unravel" | | 2 | "tapestry" | | 3 | "silence" | | 4 | "calculated" | | 5 | "maw" | | 6 | "familiar" | | 7 | "flicked" | | 8 | "gloom" | | 9 | "echoing" | | 10 | "scanning" | | 11 | "velvet" | | 12 | "pulse" | | 13 | "vibrated" | | 14 | "weight" |
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| 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" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 101 | | matches | (empty) | |
| 72.14% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 101 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1478 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 60 | | wordCount | 1284 | | uniqueNames | 17 | | maxNameDensity | 1.79 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Harlow | 1 | | Quinn | 23 | | Herrera | 17 | | Seville | 1 | | Morris | 4 | | Camden | 1 | | Tube | 1 | | London | 1 | | Underground | 1 | | Met | 1 | | Veil | 1 | | Market | 1 | | Saint | 2 | | Christopher | 2 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Underground" | | 6 | "Market" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Seville" | | 2 | "London" | | 3 | "Veil" |
| | globalScore | 0.604 | | windowScore | 0.5 | |
| 58.54% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | glossingSentenceCount | 3 | | matches | | 0 | "energy that seemed to ignore the slickness of the cobblestones" | | 1 | "felt like a transition" | | 2 | "liquid that seemed to pulse with its own heartbeat" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.677 | | wordCount | 1478 | | matches | | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 36.05 | | std | 22.82 | | cv | 0.633 | | sampleLengths | | 0 | 68 | | 1 | 84 | | 2 | 32 | | 3 | 99 | | 4 | 6 | | 5 | 16 | | 6 | 79 | | 7 | 36 | | 8 | 45 | | 9 | 53 | | 10 | 46 | | 11 | 15 | | 12 | 39 | | 13 | 43 | | 14 | 15 | | 15 | 14 | | 16 | 28 | | 17 | 18 | | 18 | 66 | | 19 | 21 | | 20 | 15 | | 21 | 7 | | 22 | 60 | | 23 | 65 | | 24 | 68 | | 25 | 34 | | 26 | 1 | | 27 | 26 | | 28 | 39 | | 29 | 18 | | 30 | 31 | | 31 | 54 | | 32 | 15 | | 33 | 23 | | 34 | 25 | | 35 | 35 | | 36 | 41 | | 37 | 25 | | 38 | 42 | | 39 | 15 | | 40 | 16 |
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| 98.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 101 | | matches | | 0 | "been transformed" | | 1 | "was pushed" |
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| 40.51% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 209 | | matches | | 0 | "was looking" | | 1 | "was sorting" | | 2 | "was haggling" | | 3 | "was speaking" | | 4 | "was clutching" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 2 | | flaggedSentences | 8 | | totalSentences | 116 | | ratio | 0.069 | | matches | | 0 | "She didn't run; she flowed with military precision, her boots barely splashing in the oily puddles." | | 1 | "She knew Herrera’s pedigree—a disgraced paramedic from Seville who had traded his NHS pension for a life tending to wounds that shouldn't exist." | | 2 | "It carried the smell of ozone, dried herbs, and something metallic—not iron, but copper." | | 3 | "The ambient noise of the city—the distant hum of tires on wet asphalt, the drone of a far-off siren—faded into a heavy, expectant silence." | | 4 | "Quinn remembered a smudge of evidence from a previous crime scene—a small, polished piece of antler carved with strange runes." | | 5 | "A merchant with fingers too long for his hands was sorting through a pile of iridescent feathers; a woman in a velvet cloak was haggling over a jar of liquid that seemed to pulse with its own heartbeat." | | 6 | "Herrera’s sleeve was pushed up, revealing the long scar on his left forearm—the mark of a knife attack that had ended his career and started his descent into this underworld." | | 7 | "From the shadows of the stalls, more figures emerged—warped, elongated, and hungry." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 840 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.017857142857142856 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007142857142857143 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 12.74 | | std | 7.32 | | cv | 0.575 | | sampleLengths | | 0 | 33 | | 1 | 18 | | 2 | 13 | | 3 | 4 | | 4 | 12 | | 5 | 21 | | 6 | 18 | | 7 | 11 | | 8 | 22 | | 9 | 5 | | 10 | 27 | | 11 | 2 | | 12 | 16 | | 13 | 23 | | 14 | 16 | | 15 | 20 | | 16 | 22 | | 17 | 6 | | 18 | 9 | | 19 | 5 | | 20 | 2 | | 21 | 11 | | 22 | 23 | | 23 | 17 | | 24 | 28 | | 25 | 19 | | 26 | 17 | | 27 | 11 | | 28 | 16 | | 29 | 3 | | 30 | 14 | | 31 | 1 | | 32 | 17 | | 33 | 23 | | 34 | 4 | | 35 | 9 | | 36 | 13 | | 37 | 15 | | 38 | 18 | | 39 | 10 | | 40 | 5 | | 41 | 2 | | 42 | 13 | | 43 | 24 | | 44 | 11 | | 45 | 19 | | 46 | 13 | | 47 | 15 | | 48 | 4 | | 49 | 7 |
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| 47.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3275862068965517 | | totalSentences | 116 | | uniqueOpeners | 38 | |
| 34.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 96 | | matches | | 0 | "Instead, he pivoted on his" |
| | ratio | 0.01 | |
| 61.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 96 | | matches | | 0 | "She checked the worn leather" | | 1 | "He glanced over his shoulder," | | 2 | "She’d spent eighteen years on" | | 3 | "She didn't run; she flowed" | | 4 | "She knew Herrera’s pedigree—a disgraced" | | 5 | "He was the bandage-man for" | | 6 | "He took a hard left," | | 7 | "They reached the outskirts of" | | 8 | "She skidded to a halt" | | 9 | "It was thicker." | | 10 | "It carried the smell of" | | 11 | "They’d called her obsessed." | | 12 | "They’d called Morris’s death a" | | 13 | "She pulled a heavy tactical" | | 14 | "He didn't move as Quinn" | | 15 | "He reached out a hand," | | 16 | "She didn't have her badge" | | 17 | "She reached into her pocket," | | 18 | "She didn't know why she’d" | | 19 | "She dropped the token into" |
| | ratio | 0.396 | |
| 17.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 96 | | matches | | 0 | "The rain in Soho didn't" | | 1 | "Detective Harlow Quinn stood in" | | 2 | "She checked the worn leather" | | 3 | "Tomás Herrera looked smaller than" | | 4 | "He glanced over his shoulder," | | 5 | "Quinn pressed her back against" | | 6 | "She’d spent eighteen years on" | | 7 | "Herrera didn't enter the bar." | | 8 | "She didn't run; she flowed" | | 9 | "She knew Herrera’s pedigree—a disgraced" | | 10 | "He was the bandage-man for" | | 11 | "The shout broke the muffled" | | 12 | "Herrera didn't even look back." | | 13 | "The chase shifted from a" | | 14 | "Quinn’s lungs burned with the" | | 15 | "Herrera was fast, driven by" | | 16 | "He took a hard left," | | 17 | "They reached the outskirts of" | | 18 | "Quinn watched as Herrera vaulted" | | 19 | "She skidded to a halt" |
| | ratio | 0.885 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 96 | | matches | | 0 | "If she caught him, she" | | 1 | "If she called for backup," |
| | ratio | 0.021 | |
| 33.90% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 9 | | matches | | 0 | "The rain in Soho didn't fall so much as it dissolved, a grey, greasy mist that turned the neon glow of the Raven’s Nest into a smear of sickly emerald against t…" | | 1 | "Tomás Herrera looked smaller than his file suggested, huddling into the collar of a waxed jacket that had seen better decades." | | 2 | "She knew Herrera’s pedigree—a disgraced paramedic from Seville who had traded his NHS pension for a life tending to wounds that shouldn't exist." | | 3 | "If she caught him, she caught the thread that would finally unravel the tapestry of lies surrounding DS Morris’s death." | | 4 | "Quinn’s lungs burned with the cold, damp air as she followed him through a shortcut that smelled of rotting refuse and expensive gin." | | 5 | "The lighting came from jars of luminous moss and flickering tallow candles that smelled of beeswax and old paper." | | 6 | "A merchant with fingers too long for his hands was sorting through a pile of iridescent feathers; a woman in a velvet cloak was haggling over a jar of liquid th…" | | 7 | "Herrera’s sleeve was pushed up, revealing the long scar on his left forearm—the mark of a knife attack that had ended his career and started his descent into th…" | | 8 | "She was deep underground, surrounded by the impossible, standing in the heart of a bazaar that shouldn't exist." |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn said, her voice steady and lethal" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 5 | | fancyTags | | 0 | "Quinn muttered (mutter)" | | 1 | "the man whispered (whisper)" | | 2 | "Quinn snapped (snap)" | | 3 | "Herrera shouted (shout)" | | 4 | "the creature hissed (hiss)" |
| | dialogueSentences | 27 | | tagDensity | 0.296 | | leniency | 0.593 | | rawRatio | 0.625 | | effectiveRatio | 0.37 | |