| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1604 | | totalAiIsmAdverbs | 2 | | 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) | |
| 87.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1604 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "shattered" | | 1 | "methodical" | | 2 | "streaming" | | 3 | "glint" |
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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 | 94 | | matches | (empty) | |
| 82.07% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "happened to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 76 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 16 | | totalWords | 1616 | | ratio | 0.01 | | matches | | 0 | "The market moves with the moon. Camden. I'm going to see what they're selling." | | 1 | "protocol" | | 2 | "Morris." |
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| 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 | 61 | | wordCount | 1504 | | uniqueNames | 25 | | maxNameDensity | 0.93 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Harlow | 2 | | Quinn | 14 | | Tomás | 2 | | Herrera | 8 | | Tottenham | 1 | | Court | 1 | | Road | 3 | | Euston | 2 | | Camden | 4 | | Street | 2 | | High | 1 | | Metropolitan | 1 | | Police | 1 | | Transport | 1 | | London | 1 | | Met | 1 | | Morris | 4 | | Saint | 1 | | Christopher | 1 | | Detective | 2 | | Veil | 1 | | Market | 1 | | Protocol | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Police" | | 7 | "Morris" | | 8 | "Saint" | | 9 | "Christopher" | | 10 | "Market" | | 11 | "Protocol" |
| | places | | 0 | "Tottenham" | | 1 | "Court" | | 2 | "Road" | | 3 | "Euston" | | 4 | "Camden" | | 5 | "Street" | | 6 | "High" | | 7 | "London" | | 8 | "Veil" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | glossingSentenceCount | 1 | | matches | | 0 | "spiral that seemed to turn when she wasn't looking directly at it" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.619 | | wordCount | 1616 | | matches | | 0 | "no evidence of anything but" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 41.44 | | std | 28.86 | | cv | 0.697 | | sampleLengths | | 0 | 73 | | 1 | 48 | | 2 | 79 | | 3 | 11 | | 4 | 57 | | 5 | 64 | | 6 | 3 | | 7 | 41 | | 8 | 114 | | 9 | 17 | | 10 | 73 | | 11 | 10 | | 12 | 56 | | 13 | 79 | | 14 | 3 | | 15 | 54 | | 16 | 20 | | 17 | 23 | | 18 | 48 | | 19 | 15 | | 20 | 93 | | 21 | 24 | | 22 | 41 | | 23 | 2 | | 24 | 65 | | 25 | 10 | | 26 | 20 | | 27 | 14 | | 28 | 17 | | 29 | 67 | | 30 | 7 | | 31 | 37 | | 32 | 77 | | 33 | 16 | | 34 | 51 | | 35 | 14 | | 36 | 33 | | 37 | 75 | | 38 | 65 |
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| 86.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 94 | | matches | | 0 | "been written" | | 1 | "being followed " | | 2 | "been taught" | | 3 | "was occupied" | | 4 | "were stained" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 245 | | matches | | 0 | "was already moving" | | 1 | "was thinking" | | 2 | "wasn't looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 100 | | ratio | 0.1 | | matches | | 0 | "Herrera walked like a man with somewhere to be and no interest in being followed — steady pace, no phone out, no glancing over his shoulder." | | 1 | "Quinn was already moving, sliding behind a parked van, but she caught the moment his eyes tracked along the reflection — patient, methodical, reading the street like a page." | | 2 | "He was fast — faster than a paramedic had any right to be — and he knew the ground." | | 3 | "From below came a sound she couldn't immediately place — a murmur, layered, like a crowd heard through a wall, punctuated by the chime of small bells and something lower, a thrum she felt in her back teeth." | | 4 | "At the gate, a shape stirred — a booth of corrugated tin that hadn't been there when the Metropolitan Police surveyed this site fourteen months ago, Quinn was certain of it." | | 5 | "Light spilled up the steps, amber and unsteady, and Herrera glanced back — once, over his shoulder, straight up the stairwell." | | 6 | "They'd recovered it from his lodgings a week after he vanished — the file said vanished, because there was no body, no scene, no evidence of anything but absence — and the final entry, in his cramped left-slanted hand, read: *The market moves with the moon." | | 7 | "It had been ticking against her skin the night she'd identified what was left of — no." | | 8 | "She pulled the gate open and stepped through into amber light and a rising wall of sound — voices stacked in a dozen languages and a few that were no language she had words for, the smell of hot metal and ambergris and something burnt-sweet, and below the platform where a train should have been, a market spreading away into the dark of the tunnel like a lantern-lit city at the bottom of the sea." | | 9 | "Ahead, between stalls of hanging charms and cages of moving shadow, she caught a flash of a leather satchel and the glint of a Saint Christopher medallion, and Detective Harlow Quinn went down the steps into the Veil Market to bring Tomás Herrera home — or to learn, at last, what had happened to the only partner she'd ever trusted." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1200 | | adjectiveStacks | 1 | | stackExamples | | 0 | "cramped left-slanted hand," |
| | adverbCount | 35 | | adverbRatio | 0.029166666666666667 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.006666666666666667 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 16.16 | | std | 14.29 | | cv | 0.885 | | sampleLengths | | 0 | 18 | | 1 | 44 | | 2 | 7 | | 3 | 4 | | 4 | 6 | | 5 | 12 | | 6 | 30 | | 7 | 9 | | 8 | 20 | | 9 | 24 | | 10 | 26 | | 11 | 3 | | 12 | 8 | | 13 | 14 | | 14 | 26 | | 15 | 8 | | 16 | 9 | | 17 | 18 | | 18 | 17 | | 19 | 29 | | 20 | 3 | | 21 | 4 | | 22 | 21 | | 23 | 10 | | 24 | 6 | | 25 | 27 | | 26 | 19 | | 27 | 43 | | 28 | 7 | | 29 | 18 | | 30 | 17 | | 31 | 37 | | 32 | 17 | | 33 | 7 | | 34 | 12 | | 35 | 10 | | 36 | 5 | | 37 | 13 | | 38 | 38 | | 39 | 4 | | 40 | 31 | | 41 | 17 | | 42 | 2 | | 43 | 4 | | 44 | 21 | | 45 | 3 | | 46 | 37 | | 47 | 2 | | 48 | 9 | | 49 | 6 |
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| 59.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.41 | | totalSentences | 100 | | uniqueOpeners | 41 | |
| 75.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 88 | | matches | | 0 | "Then he ran." | | 1 | "Then he went down into" |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 88 | | matches | | 0 | "Her coffee had gone cold" | | 1 | "She'd drunk it anyway." | | 2 | "He was also, per three" | | 3 | "He turned north." | | 4 | "She gave him half a" | | 5 | "He checked the window of" | | 6 | "He simply went, satchel banging" | | 7 | "He was fast — faster" | | 8 | "He took a shortcut through" | | 9 | "It was a gap between" | | 10 | "She pressed herself against the" | | 11 | "He'd known since Euston Road," | | 12 | "He'd let her see him" | | 13 | "They'd recovered it from his" | | 14 | "I'm going to see what" | | 15 | "He'd suggested Morris had been" | | 16 | "She'd suggested a lot of" | | 17 | "She hadn't thought about that" | | 18 | "She was thinking about it" | | 19 | "She'd worn it through every" |
| | ratio | 0.284 | |
| 67.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 88 | | matches | | 0 | "The green neon sign of" | | 1 | "Detective Harlow Quinn had been" | | 2 | "Her coffee had gone cold" | | 3 | "She'd drunk it anyway." | | 4 | "The bar had closed at" | | 5 | "The clientele trickled out in" | | 6 | "Quinn straightened slowly, keeping her" | | 7 | "Herrera was twenty-nine, ex-NHS, struck" | | 8 | "He was also, per three" | | 9 | "He turned north." | | 10 | "She gave him half a" | | 11 | "Herrera walked like a man" | | 12 | "That was the first thing" | | 13 | "Nobody walked that clean unless" | | 14 | "He checked the window of" | | 15 | "Quinn was already moving, sliding" | | 16 | "He simply went, satchel banging" | | 17 | "A black cab braked hard" | | 18 | "Quinn swore and ran after" | | 19 | "The chase tore north up" |
| | ratio | 0.784 | |
| 56.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 88 | | matches | | 0 | "Even at forty feet, in" |
| | ratio | 0.011 | |
| 55.39% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 6 | | matches | | 0 | "He was also, per three separate informants, the man who patched up the clique's people when they got hurt in ways hospitals didn't have forms for." | | 1 | "The chase tore north up Camden Street, past shuttered shops and the orange smear of sodium lamps, rain coming sideways now, her breath tearing in her chest." | | 2 | "It was a gap between a boarded bakery and a construction hoarding, a flight of concrete steps descending below street level, and at the bottom, a rusted gate se…" | | 3 | "Even at forty feet, in the dark, in the rain, she saw his face clearly: olive skin gone pale, dark curls plastered to his forehead, warm brown eyes that held he…" | | 4 | "The moon was three nights off full, a hard white edge showing through a tear in the clouds, and the tin booth at the bottom of the steps was occupied by someone…" | | 5 | "The keeper weighed it without a scale, whatever that meant, and pressed something into her hand in return: a token of pale bone the size of the last joint of he…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0 | | effectiveRatio | 0 | |