| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1236 | | 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) | |
| 59.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1236 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "flicker" | | 1 | "glint" | | 2 | "efficient" | | 3 | "could feel" | | 4 | "navigating" | | 5 | "trembled" | | 6 | "familiar" | | 7 | "weight" | | 8 | "echoed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 84 | | matches | (empty) | |
| 23.81% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 2 | | narrationSentences | 84 | | filterMatches | | 0 | "watch" | | 1 | "watch—watch, think" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 85 | | 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 | 1227 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1218 | | uniqueNames | 22 | | maxNameDensity | 0.99 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 1 | | Quinn | 12 | | Raven | 1 | | Nest | 1 | | Tomás | 1 | | Herrera | 8 | | Saint | 1 | | Christopher | 1 | | Wardour | 1 | | Street | 1 | | Camden | 1 | | Regent | 1 | | Canal | 1 | | Morris | 3 | | Victorian | 1 | | Tube | 1 | | Blitz | 1 | | Veil | 1 | | Market | 1 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Regent" | | 8 | "Morris" | | 9 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Victorian" | | 4 | "Metropolitan" |
| | globalScore | 1 | | windowScore | 1 | |
| 74.24% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 2 | | matches | | 0 | "as if offering protection" | | 1 | "looked like a ventilation shaft set into" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 4 | | per1kWords | 3.26 | | wordCount | 1227 | | matches | | 0 | "Not drawn, but ready" | | 1 | "not metal, but organic, curved like a crescent moon" | | 2 | "neither surprised nor" | | 3 | "not aimed at Herrera, but ready" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 45.44 | | std | 30.99 | | cv | 0.682 | | sampleLengths | | 0 | 87 | | 1 | 78 | | 2 | 69 | | 3 | 21 | | 4 | 3 | | 5 | 97 | | 6 | 7 | | 7 | 82 | | 8 | 78 | | 9 | 10 | | 10 | 60 | | 11 | 39 | | 12 | 11 | | 13 | 57 | | 14 | 4 | | 15 | 96 | | 16 | 8 | | 17 | 68 | | 18 | 52 | | 19 | 50 | | 20 | 18 | | 21 | 64 | | 22 | 62 | | 23 | 54 | | 24 | 6 | | 25 | 40 | | 26 | 6 |
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| 96.91% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 84 | | matches | | 0 | "been frozen" | | 1 | "was cracked" |
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| 34.98% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 202 | | matches | | 0 | "was coming" | | 1 | "wasn’t just fleeing" | | 2 | "was navigating" | | 3 | "was climbing" | | 4 | "was already vanishing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 85 | | ratio | 0.094 | | matches | | 0 | "He moved differently than he had in the surveillance photos—lower center of gravity, head tucked, the short curly dark brown hair plastered flat by the downpour." | | 1 | "Herrera was fast for a former paramedic—faster than his file suggested, certainly faster than a man with a knife scar should be." | | 2 | "Then he reached into his pocket and withdrew something that gleamed dully in the darkness—not metal, but organic, curved like a crescent moon." | | 3 | "Somewhere below, voices murmured in commerce—a market, but one that whispered in registers that made her teeth ache." | | 4 | "She checked her watch—Morris’s watch, though she never let herself think of it that way." | | 5 | "She looked back at the city above—the familiar topography of streetlamps and surveillance cameras, of protocols and backup calls and the eighteen years of procedure that had defined her existence." | | 6 | "Inside, wrapped in plastic, lay a fragment she’d found clutched in Morris’s hand three years ago—a small, curved piece of ivory that the lab couldn’t identify." | | 7 | "But as Quinn’s eyes adjusted, she saw light flickering from farther down—bioluminescent or chemical, she couldn’t tell—and smelled the distinct scent of alchemical smoke, sweet and acrid." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1231 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.022745735174654752 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005686433793663688 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 85 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 85 | | mean | 14.44 | | std | 9.36 | | cv | 0.648 | | sampleLengths | | 0 | 26 | | 1 | 33 | | 2 | 28 | | 3 | 8 | | 4 | 26 | | 5 | 28 | | 6 | 16 | | 7 | 5 | | 8 | 3 | | 9 | 23 | | 10 | 38 | | 11 | 6 | | 12 | 12 | | 13 | 3 | | 14 | 3 | | 15 | 9 | | 16 | 22 | | 17 | 17 | | 18 | 20 | | 19 | 29 | | 20 | 4 | | 21 | 3 | | 22 | 15 | | 23 | 27 | | 24 | 20 | | 25 | 11 | | 26 | 9 | | 27 | 13 | | 28 | 23 | | 29 | 19 | | 30 | 19 | | 31 | 4 | | 32 | 7 | | 33 | 3 | | 34 | 16 | | 35 | 19 | | 36 | 23 | | 37 | 2 | | 38 | 11 | | 39 | 21 | | 40 | 7 | | 41 | 11 | | 42 | 11 | | 43 | 28 | | 44 | 18 | | 45 | 4 | | 46 | 6 | | 47 | 10 | | 48 | 15 | | 49 | 25 |
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| 52.55% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.36470588235294116 | | totalSentences | 85 | | uniqueOpeners | 31 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 80 | | matches | | 0 | "Then he ran." | | 1 | "Then he reached into his" | | 2 | "Somewhere below, voices murmured in" | | 3 | "Then she looked down, into" |
| | ratio | 0.05 | |
| 65.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 80 | | matches | | 0 | "She’d been there three hours," | | 1 | "He moved differently than he" | | 2 | "She didn’t shout." | | 3 | "She closed the distance with" | | 4 | "He saw her at fifteen" | | 5 | "He cut west, dodging between" | | 6 | "She was forty-one, fit, but" | | 7 | "He wasn’t just fleeing." | | 8 | "He was navigating." | | 9 | "They left Soho behind, the" | | 10 | "He ducked beneath railway arches" | | 11 | "She caught the flash of" | | 12 | "Her voice cut through the" | | 13 | "He paused at the mouth" | | 14 | "He pressed it against the" | | 15 | "She reached the entrance as" | | 16 | "Her chest heaved, her uniform" | | 17 | "She checked her watch—Morris’s watch," | | 18 | "They’d found him without a" | | 19 | "She’d kept the watch." |
| | ratio | 0.388 | |
| 78.75% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 80 | | matches | | 0 | "Rain was coming down in" | | 1 | "Detective Harlow Quinn stood in" | | 2 | "She’d been there three hours," | | 3 | "He moved differently than he" | | 4 | "The scar running along his" | | 5 | "Quinn pushed off the wall." | | 6 | "She didn’t shout." | | 7 | "She closed the distance with" | | 8 | "He saw her at fifteen" | | 9 | "Those warm brown eyes widened," | | 10 | "Quinn gave chase." | | 11 | "The rain lashed sideways as" | | 12 | "Herrera was fast for a" | | 13 | "He cut west, dodging between" | | 14 | "Quinn kept pace, her salt-and-pepper" | | 15 | "She was forty-one, fit, but" | | 16 | "He wasn’t just fleeing." | | 17 | "He was navigating." | | 18 | "They left Soho behind, the" | | 19 | "Herrera led her north toward" |
| | ratio | 0.763 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 12 | | matches | | 0 | "Rain was coming down in ropes, turning Soho’s pavement into a black mirror that reflected the city’s neon guts in smeared streaks of red and green." | | 1 | "She’d been there three hours, ignoring the ache in her knees and the steady seep of cold through her coat, waiting for one of the clique to surface." | | 2 | "She closed the distance with the efficient stride that had earned her a reputation for military precision, her boots striking the pavement in a rhythm that matc…" | | 3 | "He cut west, dodging between midnight revelers huddled under awnings, splashing through gutters that ran like rivers." | | 4 | "She caught the flash of his medallion as he hauled himself over, the saint’s face turned toward the storm as if offering protection." | | 5 | "Through the gap, she saw stone steps descending into a breath of air that carried the scent of copper and myrrh, of ozone and vegetation growing without light." | | 6 | "Somewhere below, voices murmured in commerce—a market, but one that whispered in registers that made her teeth ache." | | 7 | "She looked back at the city above—the familiar topography of streetlamps and surveillance cameras, of protocols and backup calls and the eighteen years of proce…" | | 8 | "It offered no resistance, as if it had been expecting her all along." | | 9 | "The platform opened into a cavernous space where stalls huddled beneath the curve of tiled walls, vendors hooded in shadows exchanging currency that glowed and …" | | 10 | "And there, at the edge of the crowd, stood Herrera, watching her with those warm brown eyes, neither surprised nor afraid, as if he’d known she would follow." | | 11 | "She was underground now, in unfamiliar territory, with no backup and no warrant and no understanding of the laws that governed this place." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |