| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.39% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1386 | | 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) | |
| 67.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1386 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "efficient" | | 1 | "glint" | | 2 | "familiar" | | 3 | "weight" | | 4 | "flickered" | | 5 | "etched" | | 6 | "depths" | | 7 | "resonated" | | 8 | "whisper" |
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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 | 0 | | narrationSentences | 107 | | matches | (empty) | |
| 76.10% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 107 | | 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 | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1366 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1356 | | uniqueNames | 19 | | maxNameDensity | 0.96 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 13 | | Tomás | 2 | | Herrera | 11 | | Soho | 1 | | London | 2 | | Raven | 1 | | Nest | 1 | | Saint | 1 | | Christopher | 1 | | Underground | 1 | | Sig | 1 | | Sauer | 1 | | Morris | 4 | | East | 1 | | End | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Underground" | | 8 | "Sauer" | | 9 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "East" | | 3 | "End" | | 4 | "Market" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 1 | | matches | | 0 | "light that seemed to come from within the stone itself" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.732 | | wordCount | 1366 | | matches | | 0 | "not the usual London Underground damp of mildew and diesel, but something older" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 108 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 29.7 | | std | 23.93 | | cv | 0.806 | | sampleLengths | | 0 | 46 | | 1 | 10 | | 2 | 48 | | 3 | 59 | | 4 | 14 | | 5 | 35 | | 6 | 62 | | 7 | 18 | | 8 | 2 | | 9 | 71 | | 10 | 7 | | 11 | 16 | | 12 | 69 | | 13 | 6 | | 14 | 65 | | 15 | 4 | | 16 | 79 | | 17 | 38 | | 18 | 29 | | 19 | 4 | | 20 | 6 | | 21 | 45 | | 22 | 2 | | 23 | 1 | | 24 | 1 | | 25 | 38 | | 26 | 1 | | 27 | 69 | | 28 | 20 | | 29 | 6 | | 30 | 69 | | 31 | 3 | | 32 | 48 | | 33 | 23 | | 34 | 48 | | 35 | 55 | | 36 | 16 | | 37 | 4 | | 38 | 26 | | 39 | 28 | | 40 | 48 | | 41 | 31 | | 42 | 6 | | 43 | 52 | | 44 | 31 | | 45 | 7 |
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| 72.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 10 | | totalSentences | 107 | | matches | | 0 | "was gone" | | 1 | "was built" | | 2 | "were fogged" | | 3 | "been opened" | | 4 | "were lined" | | 5 | "been gutted" | | 6 | "was lined" | | 7 | "was marked" | | 8 | "been filed" | | 9 | "being made" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 11 | | totalVerbs | 227 | | matches | | 0 | "was already failing" | | 1 | "was letting" | | 2 | "was settling" | | 3 | "was leading" | | 4 | "was watching" | | 5 | "was walking" | | 6 | "was still coming" | | 7 | "was still thrumming" | | 8 | "was crossing" | | 9 | "was disappearing" | | 10 | "was finally looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 20 | | semicolonCount | 0 | | flaggedSentences | 16 | | totalSentences | 108 | | ratio | 0.148 | | matches | | 0 | "Detective Harlow Quinn's boots hit the wet ground in a rhythm she'd perfected across eighteen years of foot pursuits—controlled, efficient, each stride eating distance without sacrificing awareness of her surroundings." | | 1 | "Herrera had made her for surveillance outside an address in Soho—a narrow townhouse she'd been watching for three weeks, one she suspected was a front for the clique's operations." | | 2 | "Herrera was built like a paramedic who'd traded hospital corridors for worse places—lean, functional, with a body that remembered how to sprint through chaos." | | 3 | "She caught a glimpse of Herrera's silhouette against the glow of a green neon sign up ahead—The Raven's Nest." | | 4 | "He glanced back, and in that instant, Quinn saw the scar running along his left forearm—a knife wound, poorly healed—and the glint of his Saint Christopher medallion swinging against his chest." | | 5 | "The air coming up from below smelled different—not the usual London Underground damp of mildew and diesel, but something older." | | 6 | "A case that should have been routine—a series of suspicious deaths in the East End, all ruled accidents by the coroner." | | 7 | "The steps were uneven, worn down by decades—centuries—of foot traffic, and she counted them as she descended, marking her progress into the unknown." | | 8 | "The smell of earth and stone gave way to something else—incense, maybe, or the metallic tang of old blood." | | 9 | "He was walking now, not running, and she realized why: the tunnel walls were lined with symbols etched into the brick—spirals and circles and angular characters she didn't recognize." | | 10 | "The tunnel opened into a vast underground space—an abandoned Tube station, she realized, though it had been gutted and transformed beyond recognition." | | 11 | "An underground black market for things that shouldn't exist—enchanted goods, banned alchemical substances, information traded in whispers and paid for with more than money." | | 12 | "He didn't need to—he knew she was still coming." | | 13 | "A vendor nearby—a woman with eyes that reflected the candlelight like an animal's—watched her with open curiosity." | | 14 | "And whatever answers he had—whatever the clique was hiding—would stay buried in places like this, beyond the reach of detectives who followed the rules." | | 15 | "The market's noise swelled around her—the chatter of vendors, the clink of coins, the whisper of spells being cast and bargains being made." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1382 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.023154848046309694 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005065123010130246 | |
| 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 | 12.65 | | std | 8.93 | | cv | 0.706 | | sampleLengths | | 0 | 16 | | 1 | 30 | | 2 | 10 | | 3 | 3 | | 4 | 12 | | 5 | 23 | | 6 | 3 | | 7 | 4 | | 8 | 3 | | 9 | 29 | | 10 | 30 | | 11 | 5 | | 12 | 9 | | 13 | 24 | | 14 | 11 | | 15 | 9 | | 16 | 11 | | 17 | 19 | | 18 | 23 | | 19 | 4 | | 20 | 14 | | 21 | 2 | | 22 | 14 | | 23 | 26 | | 24 | 31 | | 25 | 7 | | 26 | 16 | | 27 | 11 | | 28 | 24 | | 29 | 20 | | 30 | 1 | | 31 | 13 | | 32 | 4 | | 33 | 2 | | 34 | 19 | | 35 | 20 | | 36 | 10 | | 37 | 7 | | 38 | 9 | | 39 | 4 | | 40 | 3 | | 41 | 21 | | 42 | 20 | | 43 | 35 | | 44 | 6 | | 45 | 7 | | 46 | 25 | | 47 | 8 | | 48 | 4 | | 49 | 17 |
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| 36.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.32407407407407407 | | totalSentences | 108 | | uniqueOpeners | 35 | |
| 68.03% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 98 | | matches | | 0 | "Then she started down the" | | 1 | "Then Quinn took a step" |
| | ratio | 0.02 | |
| 77.14% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 98 | | matches | | 0 | "Her leather watch strap creaked" | | 1 | "She didn't care." | | 2 | "She'd been wet before." | | 3 | "She'd been worse." | | 4 | "He'd spotted her sedan, held" | | 5 | "She'd known he might bolt." | | 6 | "She caught a glimpse of" | | 7 | "He didn't go inside." | | 8 | "He cut left, down a" | | 9 | "He glanced back, and in" | | 10 | "He knew she was still" | | 11 | "He kicked open a rusted" | | 12 | "She checked her watch." | | 13 | "Her Sig Sauer sat heavy" | | 14 | "She didn't know what was" | | 15 | "She didn't know what Herrera" | | 16 | "She thought about Morris." | | 17 | "He'd followed a lead into" | | 18 | "She took a breath." | | 19 | "She kept one hand on" |
| | ratio | 0.357 | |
| 51.84% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 98 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn's boots hit" | | 2 | "Quinn pushed harder." | | 3 | "Her leather watch strap creaked" | | 4 | "The cheap waterproof jacket she'd" | | 5 | "She didn't care." | | 6 | "She'd been wet before." | | 7 | "She'd been worse." | | 8 | "Herrera had made her for" | | 9 | "He'd spotted her sedan, held" | | 10 | "She'd known he might bolt." | | 11 | "Herrera was built like a" | | 12 | "Quinn's lungs burned, but she" | | 13 | "A taxi blared its horn" | | 14 | "She caught a glimpse of" | | 15 | "The bar's windows were fogged" | | 16 | "He didn't go inside." | | 17 | "He cut left, down a" | | 18 | "The passage opened onto a" | | 19 | "Herrera was already at the" |
| | ratio | 0.816 | |
| 51.02% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 98 | | matches | | | ratio | 0.01 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 8 | | matches | | 0 | "He'd spotted her sedan, held her gaze for a split second too long, and then he was gone, vanishing into the maze of backstreets that threaded through London lik…" | | 1 | "Herrera was built like a paramedic who'd traded hospital corridors for worse places—lean, functional, with a body that remembered how to sprint through chaos." | | 2 | "Morris had been her partner for six years, her anchor in a job that tried its best to drown her." | | 3 | "Vendors hawked wares from behind makeshift counters: glass vials filled with glowing liquids, animal skulls with gemstones set in their eye sockets, books bound…" | | 4 | "An underground black market for things that shouldn't exist—enchanted goods, banned alchemical substances, information traded in whispers and paid for with more…" | | 5 | "The kind of place that operated on the edges of her reality, where the rules she knew didn't apply." | | 6 | "A vendor nearby—a woman with eyes that reflected the candlelight like an animal's—watched her with open curiosity." | | 7 | "The bones of the archway seemed to hum as she passed beneath them, a vibration that resonated in her teeth and her skull and the marrow of her bones." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |