| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.38% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1082 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1082 | | totalAiIsms | 31 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | |
| | highlights | | 0 | "jaw clenched" | | 1 | "raced" | | 2 | "weight" | | 3 | "chill" | | 4 | "maw" | | 5 | "pounding" | | 6 | "echo" | | 7 | "tracing" | | 8 | "cacophony" | | 9 | "echoes" | | 10 | "warmth" | | 11 | "tension" | | 12 | "loomed" | | 13 | "unspoken" | | 14 | "whisper" | | 15 | "trembled" | | 16 | "furrowed" | | 17 | "familiar" | | 18 | "encounter" | | 19 | "racing" | | 20 | "etched" | | 21 | "gloom" | | 22 | "reminder" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
| | 2 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "blood ran cold" | | 1 | "jaw clenched" | | 2 | "a flash of recognition" |
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| 98.12% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 62 | | matches | | 0 | "g with urgency" | | 1 | "d with urgency" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 62 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 72 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1072 | | 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 | 33 | | wordCount | 947 | | uniqueNames | 12 | | maxNameDensity | 0.95 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 9 | | Morris | 3 | | Soho | 2 | | Tomás | 9 | | Herrera | 2 | | Raven | 1 | | Nest | 2 | | Veil | 1 | | Market | 1 | | Camden | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Raven" | | 6 | "Nest" | | 7 | "Market" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 1 | | matches | | 0 | "as if expecting trouble" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1072 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 36.97 | | std | 18.88 | | cv | 0.511 | | sampleLengths | | 0 | 85 | | 1 | 58 | | 2 | 62 | | 3 | 57 | | 4 | 54 | | 5 | 65 | | 6 | 17 | | 7 | 43 | | 8 | 33 | | 9 | 44 | | 10 | 55 | | 11 | 50 | | 12 | 20 | | 13 | 12 | | 14 | 44 | | 15 | 31 | | 16 | 12 | | 17 | 26 | | 18 | 29 | | 19 | 15 | | 20 | 42 | | 21 | 26 | | 22 | 20 | | 23 | 19 | | 24 | 32 | | 25 | 3 | | 26 | 49 | | 27 | 27 | | 28 | 42 |
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| 93.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 62 | | matches | | 0 | "was huddled" | | 1 | "being urged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 152 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 2 | | flaggedSentences | 9 | | totalSentences | 72 | | ratio | 0.125 | | matches | | 0 | "Her sharp jaw clenched as she thought of DS Morris—the lingering pain of his loss bubbling to the surface like a cruel joke." | | 1 | "Each step sent splashes of water up her legs as she raced after the silhouette—long strides propelling her across the slick asphalt." | | 2 | "She rounded the corner, catching a glimpse of the fugitive—a man, tall and agile, darting deeper into the labyrinth of Soho." | | 3 | "He wasn’t just an innocent bystander; he was one of them." | | 4 | "But as she closed in, Herrera turned sharply on his heel and bolted toward an unmarked door beneath a flickering overhead light—The Raven’s Nest." | | 5 | "The Nest had its secrets; it catered to an unsavory clientele, and stepping inside meant crossing a threshold into dangerous territory." | | 6 | "It was evident he was on the brink of fleeing once again, but there was a raw vulnerability about him that stirred something deep in her—an impulse to protect him from whatever lay beyond." | | 7 | "She didn’t know if they were talking about supernatural entities or human threats yet again, but the tension had a familiar taste—one she'd sworn she’d never encounter again after losing Morris." | | 8 | "As they slipped through a hidden doorway in the back, wreathed by the scent of old wood and untold stories, she felt the darkness wrap around her—threatening, yet familiar." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 956 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.03138075313807531 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.012552301255230125 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 14.89 | | std | 7.92 | | cv | 0.532 | | sampleLengths | | 0 | 17 | | 1 | 30 | | 2 | 15 | | 3 | 23 | | 4 | 35 | | 5 | 15 | | 6 | 8 | | 7 | 22 | | 8 | 18 | | 9 | 22 | | 10 | 15 | | 11 | 21 | | 12 | 21 | | 13 | 11 | | 14 | 10 | | 15 | 4 | | 16 | 21 | | 17 | 8 | | 18 | 24 | | 19 | 9 | | 20 | 21 | | 21 | 11 | | 22 | 17 | | 23 | 17 | | 24 | 13 | | 25 | 13 | | 26 | 17 | | 27 | 11 | | 28 | 5 | | 29 | 15 | | 30 | 20 | | 31 | 9 | | 32 | 30 | | 33 | 9 | | 34 | 16 | | 35 | 5 | | 36 | 11 | | 37 | 34 | | 38 | 20 | | 39 | 8 | | 40 | 4 | | 41 | 24 | | 42 | 20 | | 43 | 13 | | 44 | 10 | | 45 | 8 | | 46 | 12 | | 47 | 20 | | 48 | 6 | | 49 | 4 |
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| 81.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5 | | totalSentences | 72 | | uniqueOpeners | 36 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 60 | | matches | (empty) | | ratio | 0 | |
| 73.33% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 60 | | matches | | 0 | "She wiped the rain from" | | 1 | "Her sharp jaw clenched as" | | 2 | "she muttered, giving chase" | | 3 | "She fought against the weight" | | 4 | "She rounded the corner, catching" | | 5 | "He wasn’t just an innocent" | | 6 | "She couldn’t lose him too," | | 7 | "Her instincts screamed at her" | | 8 | "He looked ill at ease," | | 9 | "she replied, her voice steady" | | 10 | "She wove through clusters of" | | 11 | "It took precious seconds, but" | | 12 | "He was huddled over a" | | 13 | "Their eyes locked, and his" | | 14 | "He darted a glance toward" | | 15 | "It was evident he was" | | 16 | "He paused, his dark curls" | | 17 | "Her chest tightened." | | 18 | "She didn’t know if they" | | 19 | "She’d been an investigator long" |
| | ratio | 0.367 | |
| 51.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 60 | | matches | | 0 | "The rain fell in relentless" | | 1 | "Detective Harlow Quinn leaned against" | | 2 | "She wiped the rain from" | | 3 | "Her sharp jaw clenched as" | | 4 | "Adrenaline surged in her veins," | | 5 | "she muttered, giving chase" | | 6 | "Each step sent splashes of" | | 7 | "She fought against the weight" | | 8 | "Quinn picked up speed, her" | | 9 | "She rounded the corner, catching" | | 10 | "He wasn’t just an innocent" | | 11 | "A former paramedic turned off-the-books" | | 12 | "Quinn had built walls tight" | | 13 | "She couldn’t lose him too," | | 14 | "Her instincts screamed at her" | | 15 | "The Nest had its secrets;" | | 16 | "Quinn called, her voice barely" | | 17 | "The low hum of muted" | | 18 | "The atmosphere was thick with" | | 19 | "The bartender, a wiry man" |
| | ratio | 0.817 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 60 | | matches | (empty) | | ratio | 0 | |
| 86.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 3 | | matches | | 0 | "The atmosphere was thick with something shadowy, something that tugged at her instincts." | | 1 | "He looked ill at ease, glancing around as if expecting trouble." | | 2 | "He was huddled over a table in the far corner, his expression a deceptive calm, as if he weren’t the man who had just sprinted through Soho in a panic." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 3 | | matches | | 0 | "she replied, her voice steady despite the rising tide of anxiety" | | 1 | "Tomás hissed, the panic rising again in his feature" | | 2 | "Quinn urged, her mind racing, calculations forming one by one" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 4 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Tomás hissed (hiss)" | | 2 | "Tomás hissed (hiss)" | | 3 | "Quinn urged (urge)" |
| | dialogueSentences | 25 | | tagDensity | 0.32 | | leniency | 0.64 | | rawRatio | 0.5 | | effectiveRatio | 0.32 | |