| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.538 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1621 | | totalAiIsmAdverbs | 3 | | 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) | |
| 56.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1621 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "electric" | | 1 | "measured" | | 2 | "warmth" | | 3 | "weight" | | 4 | "rhythmic" | | 5 | "flickered" | | 6 | "tracing" | | 7 | "shimmered" | | 8 | "velvet" | | 9 | "echoing" | | 10 | "depths" | | 11 | "absolutely" |
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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 | 106 | | matches | | |
| 8.09% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 7 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | | 0 | "begin to" | | 1 | "began to" | | 2 | "seem to" | | 3 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1613 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 1522 | | uniqueNames | 19 | | maxNameDensity | 1.25 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Harlow | 1 | | Quinn | 19 | | Raven | 2 | | Nest | 2 | | Soho | 1 | | Morris | 4 | | Herrera | 13 | | Saint | 2 | | Christopher | 2 | | Camden | 2 | | Underground | 1 | | Veil | 2 | | Market | 3 | | Met | 1 | | Queen | 1 | | Northern | 1 | | Line | 1 | | Tomás | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Morris" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Queen" | | 9 | "Tomás" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Underground" | | 3 | "Line" |
| | globalScore | 0.876 | | windowScore | 0.833 | |
| 96.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 94 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like heaps of walking refuse" | | 1 | "sounded like a thousand clockwork gears gr" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.62 | | wordCount | 1613 | | matches | | 0 | "not the yellow hum of sodium bulbs, but the flickering glow of lanterns filled with bioluminescent m" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 43.59 | | std | 23.19 | | cv | 0.532 | | sampleLengths | | 0 | 90 | | 1 | 80 | | 2 | 87 | | 3 | 20 | | 4 | 29 | | 5 | 80 | | 6 | 72 | | 7 | 50 | | 8 | 33 | | 9 | 16 | | 10 | 56 | | 11 | 9 | | 12 | 52 | | 13 | 40 | | 14 | 58 | | 15 | 34 | | 16 | 23 | | 17 | 73 | | 18 | 42 | | 19 | 20 | | 20 | 49 | | 21 | 17 | | 22 | 16 | | 23 | 59 | | 24 | 20 | | 25 | 36 | | 26 | 22 | | 27 | 54 | | 28 | 28 | | 29 | 43 | | 30 | 35 | | 31 | 8 | | 32 | 32 | | 33 | 53 | | 34 | 52 | | 35 | 34 | | 36 | 91 |
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| 75.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 9 | | totalSentences | 106 | | matches | | 0 | "was beaded" | | 1 | "been peeled" | | 2 | "was withered" | | 3 | "were built" | | 4 | "were draped" | | 5 | "were bundled" | | 6 | "being played" | | 7 | "was gone" | | 8 | "were written" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 237 | | matches | | 0 | "was wrapping" | | 1 | "was heading" | | 2 | "were spinning" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 4 | | flaggedSentences | 10 | | totalSentences | 112 | | ratio | 0.089 | | matches | | 0 | "He wasn’t a criminal in the traditional sense—at least not the kind Quinn usually hunted." | | 1 | "She kept her distance, using the clutter of the city—abandoned skips, stacks of wooden pallets, the skeletal remains of a scaffolding rig—to mask her approach." | | 2 | "Most men in his position carried a knife; Herrera carried a Saint Christopher medallion and a head full of secrets." | | 3 | "The smell hit her first—a cloying mixture of ozone, rotting lilies, and something metallic, like blood on a hot stove." | | 4 | "Lights flickered to life—not the yellow hum of sodium bulbs, but the flickering glow of lanterns filled with bioluminescent moss and jars of trapped, sparking vapor." | | 5 | "She knew the lore of the street; the entrance to the Market required a bone token." | | 6 | "Brass scales weighed out powders made of crushed iridescent wings; jars of preserved organs shook with rhythmic, phantom pulses." | | 7 | "Some were tall and reed-thin, their movements too fluid to be human; others were bundled in so many rags they looked like heaps of walking refuse." | | 8 | "He didn't flee—not at first." | | 9 | "She caught a glimpse of Herrera’s scar—the jagged line along his left forearm—as he vaulted over a low table covered in silver trinkets." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1540 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.02142857142857143 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.007142857142857143 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 14.4 | | std | 7.65 | | cv | 0.531 | | sampleLengths | | 0 | 19 | | 1 | 22 | | 2 | 30 | | 3 | 19 | | 4 | 10 | | 5 | 5 | | 6 | 20 | | 7 | 23 | | 8 | 22 | | 9 | 8 | | 10 | 15 | | 11 | 8 | | 12 | 2 | | 13 | 14 | | 14 | 15 | | 15 | 25 | | 16 | 6 | | 17 | 14 | | 18 | 22 | | 19 | 3 | | 20 | 4 | | 21 | 13 | | 22 | 25 | | 23 | 8 | | 24 | 14 | | 25 | 20 | | 26 | 11 | | 27 | 18 | | 28 | 13 | | 29 | 17 | | 30 | 13 | | 31 | 17 | | 32 | 12 | | 33 | 5 | | 34 | 16 | | 35 | 19 | | 36 | 14 | | 37 | 10 | | 38 | 6 | | 39 | 20 | | 40 | 8 | | 41 | 28 | | 42 | 9 | | 43 | 7 | | 44 | 19 | | 45 | 26 | | 46 | 13 | | 47 | 21 | | 48 | 6 | | 49 | 2 |
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| 42.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.32142857142857145 | | totalSentences | 112 | | uniqueOpeners | 36 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 104 | | matches | (empty) | | ratio | 0 | |
| 43.08% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 104 | | matches | | 0 | "She stood in the shadow" | | 1 | "She checked the worn leather" | | 2 | "Her salt-and-pepper hair, cropped close" | | 3 | "It felt heavy with the" | | 4 | "He moved with a practiced," | | 5 | "He wasn’t a criminal in" | | 6 | "He was a ghost-doctor, a" | | 7 | "He turned toward the mouth" | | 8 | "She stepped out of the" | | 9 | "She kept her distance, using" | | 10 | "He kept clutching at something" | | 11 | "They crossed into Camden, where" | | 12 | "Her lungs burned with the" | | 13 | "She reached the fence and" | | 14 | "she muttered to the empty" | | 15 | "It was the scent of" | | 16 | "She’d heard ripples of it" | | 17 | "He was withered, his skin" | | 18 | "He held out a gnarled" | | 19 | "She didn't have what he" |
| | ratio | 0.442 | |
| 12.88% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 93 | | totalSentences | 104 | | matches | | 0 | "The rain in London didn’t" | | 1 | "Detective Harlow Quinn adjusted the" | | 2 | "She stood in the shadow" | | 3 | "The light cast a sickly" | | 4 | "She checked the worn leather" | | 5 | "Midnight had come and gone." | | 6 | "Her salt-and-pepper hair, cropped close" | | 7 | "It felt heavy with the" | | 8 | "The door of The Raven’s" | | 9 | "A man stepped out, his" | | 10 | "He moved with a practiced," | | 11 | "He wasn’t a criminal in" | | 12 | "He was a ghost-doctor, a" | | 13 | "Herrera didn't look left or" | | 14 | "He turned toward the mouth" | | 15 | "Quinn whispered into the collar" | | 16 | "This was off-the-books." | | 17 | "This was for Morris." | | 18 | "She stepped out of the" | | 19 | "She kept her distance, using" |
| | ratio | 0.894 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 104 | | matches | | 0 | "Even from fifty yards, Quinn" | | 1 | "Now, those stalls were draped" | | 2 | "If anything, they closed ranks," | | 3 | "By the time the stars" |
| | ratio | 0.038 | |
| 57.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 67 | | technicalSentenceCount | 8 | | matches | | 0 | "She stood in the shadow of a brick alcove across from The Raven’s Nest, her eyes fixed on the distinctive green neon sign that hummed with a low, electric vibra…" | | 1 | "Her salt-and-pepper hair, cropped close to her skull in a style that favored utility over vanity, was beaded with moisture." | | 2 | "It felt heavy with the same inexplicable statics that had preceded the night DS Morris vanished into the dark three years ago." | | 3 | "He was a ghost-doctor, a former paramedic who had traded his NHS badge for a life in the shadows, stitching up wounds that shouldn't exist." | | 4 | "She’d heard ripples of it in the interrogation rooms of the Met, whispers from frantic junkies and broken informants who claimed there was a city beneath the ci…" | | 5 | "He was withered, his skin like parchment paper, and his eyes were milky cataracts that didn't seem to track her movement." | | 6 | "He squeezed it, his thumb tracing the Queen’s profile, and then he leaned back, kicking a heavy iron bolt aside." | | 7 | "She spotted Herrera’s dark, curly hair weaving through a crowd of figures that defied easy categorization." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 3 | | matches | | 0 | "Herrera gasped, the word lost in the din" | | 1 | "he yelled, his voice echoing off the curved walls" | | 2 | "Quinn said, her voice steady despite the hammer of her heart" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 5 | | fancyTags | | 0 | "Quinn whispered (whisper)" | | 1 | "she muttered (mutter)" | | 2 | "she shouted (shout)" | | 3 | "Herrera gasped (gasp)" | | 4 | "he yelled (yell)" |
| | dialogueSentences | 13 | | tagDensity | 0.462 | | leniency | 0.923 | | rawRatio | 0.833 | | effectiveRatio | 0.769 | |