| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.26% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1337 | | 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) | |
| 25.21% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1337 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "measured" | | 1 | "familiar" | | 2 | "electric" | | 3 | "flicker" | | 4 | "tension" | | 5 | "tracing" | | 6 | "efficient" | | 7 | "silence" | | 8 | "footsteps" | | 9 | "predator" | | 10 | "maw" | | 11 | "streaming" | | 12 | "etched" | | 13 | "silk" | | 14 | "pulsed" | | 15 | "scanning" | | 16 | "weight" | | 17 | "porcelain" | | 18 | "chill" | | 19 | "lilt" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "clenched jaw/fists" | | count | 1 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "clenched his fist" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 81 | | matches | | |
| 54.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 81 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 6 | | totalWords | 1325 | | ratio | 0.005 | | matches | | 0 | "Camden Lock Depot - Closed 1952" |
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| 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 | 58 | | wordCount | 1280 | | uniqueNames | 31 | | maxNameDensity | 0.94 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Harlow | 1 | | Quinn | 12 | | Tomás | 1 | | Herrera | 7 | | Raven | 1 | | Nest | 1 | | Wardour | 1 | | Street | 1 | | Saint | 2 | | Christopher | 2 | | Glock | 2 | | Morris | 2 | | Metropolitan | 1 | | Police | 1 | | Camden | 4 | | Soho | 1 | | Tube | 1 | | Road | 1 | | Underground | 1 | | Transport | 1 | | Lock | 1 | | Depot | 1 | | Closed | 1 | | King | 1 | | Cross | 1 | | Brixton | 1 | | Veil | 2 | | Market | 2 | | Latin | 1 | | Seville | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Raven" | | 5 | "Nest" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Morris" | | 9 | "Underground" | | 10 | "King" | | 11 | "Cross" | | 12 | "Market" |
| | places | | 0 | "London" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Camden" | | 4 | "Soho" | | 5 | "Road" | | 6 | "Brixton" | | 7 | "Seville" |
| | globalScore | 1 | | windowScore | 1 | |
| 39.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 3 | | matches | | 0 | "as if praying for safe passage" | | 1 | "quite human, vials of blood that steamed in the cold air" | | 2 | "seemed very far away" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 2.264 | | wordCount | 1325 | | matches | | 0 | "Not the functioning one with its bright ticket halls and commuters, but the abandoned husk two streets east, a relic of the Undergro" | | 1 | "not in surrender, but to brush rain from his eyes" | | 2 | "not with fear, but with recognition" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 82.73% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 57.61 | | std | 25.32 | | cv | 0.44 | | sampleLengths | | 0 | 90 | | 1 | 105 | | 2 | 79 | | 3 | 88 | | 4 | 81 | | 5 | 42 | | 6 | 74 | | 7 | 65 | | 8 | 60 | | 9 | 66 | | 10 | 56 | | 11 | 10 | | 12 | 90 | | 13 | 74 | | 14 | 43 | | 15 | 51 | | 16 | 39 | | 17 | 23 | | 18 | 31 | | 19 | 40 | | 20 | 55 | | 21 | 55 | | 22 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 81 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 201 | | matches | | 0 | "was leading" | | 1 | "wasn't letting" | | 2 | "was pretending" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 13 | | totalSentences | 83 | | ratio | 0.157 | | matches | | 0 | "Detective Harlow Quinn kept her elbows tucked, her gait measured—military precision ingrained from eighteen years on the force, though the wet pavement under her boots tried to break her rhythm." | | 1 | "She'd seen the scar tracing his left forearm last week during a stakeout—a jagged white line against the bronze of his skin, a souvenir from some knife fight that predated his exile from the NHS." | | 2 | "Morris had died on a case with supernatural tattoos—whispers of things that didn't belong in the Metropolitan Police files." | | 3 | "The alternative—letting him disappear into the night with whatever intelligence he carried about the clique's operations—was a luxury she couldn't afford." | | 4 | "Above it, a faded sign read *Camden Lock Depot - Closed 1952*." | | 5 | "Quinn saw the medallion swing against his chest, saw the scar on his forearm as he raised his hand—not in surrender, but to brush rain from his eyes." | | 6 | "To enter properly, one needed a bone token—a bleached knucklebone etched with sigils that hummed with residual magic." | | 7 | "The air grew colder, tasting of rust and stagnant water and something else—ozone and old stone." | | 8 | "Vendors hawked their wares in languages that predated Latin—bottles of liquid shadow, grimoires bound in skin that wasn't quite human, vials of blood that steamed in the cold air." | | 9 | "She was an anomaly here—a human detective in a place that catered to the supernatural, the criminal, the things that slipped between the cracks of the law." | | 10 | "The troll sniffed the air, its eyes—milky and cataract-blind—tracking her movement with unsettling accuracy." | | 11 | "His eyes widened—not with fear, but with recognition." | | 12 | "Behind him, the darkness of the tunnel deepened, and Quinn heard the sound of something large shifting in the black—wings, perhaps, or heavy feet on stone." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1302 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.020737327188940093 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.009216589861751152 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 15.96 | | std | 9.04 | | cv | 0.566 | | sampleLengths | | 0 | 15 | | 1 | 30 | | 2 | 24 | | 3 | 21 | | 4 | 20 | | 5 | 32 | | 6 | 18 | | 7 | 35 | | 8 | 6 | | 9 | 35 | | 10 | 16 | | 11 | 22 | | 12 | 10 | | 13 | 47 | | 14 | 19 | | 15 | 7 | | 16 | 5 | | 17 | 5 | | 18 | 21 | | 19 | 22 | | 20 | 18 | | 21 | 15 | | 22 | 5 | | 23 | 16 | | 24 | 21 | | 25 | 8 | | 26 | 34 | | 27 | 20 | | 28 | 12 | | 29 | 5 | | 30 | 13 | | 31 | 28 | | 32 | 19 | | 33 | 19 | | 34 | 5 | | 35 | 29 | | 36 | 7 | | 37 | 3 | | 38 | 25 | | 39 | 18 | | 40 | 4 | | 41 | 16 | | 42 | 10 | | 43 | 16 | | 44 | 16 | | 45 | 14 | | 46 | 10 | | 47 | 22 | | 48 | 29 | | 49 | 11 |
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| 45.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3253012048192771 | | totalSentences | 83 | | uniqueOpeners | 27 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 78 | | matches | | 0 | "Then he descended the concrete" |
| | ratio | 0.013 | |
| 45.64% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 78 | | matches | | 0 | "She wore the rain like" | | 1 | "He was twenty-nine, five-foot-ten of" | | 2 | "She'd seen the scar tracing" | | 3 | "He knew he was being" | | 4 | "She'd seen it in the" | | 5 | "His hand kept drifting to" | | 6 | "She wouldn't accept it now." | | 7 | "He was leading her somewhere." | | 8 | "She knew it with the" | | 9 | "They reached the Tube station" | | 10 | "He turned, and for a" | | 11 | "Her watch read 2:17 AM." | | 12 | "She'd lost him in the" | | 13 | "She knew the name from" | | 14 | "She didn't have one." | | 15 | "She'd crossed into worse places" | | 16 | "She descended the steps, her" | | 17 | "She kept her head down," | | 18 | "She was an anomaly here—a" | | 19 | "She felt the weight of" |
| | ratio | 0.436 | |
| 30.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 78 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn kept her" | | 2 | "She wore the rain like" | | 3 | "Quinn had been tracking him" | | 4 | "He was twenty-nine, five-foot-ten of" | | 5 | "She'd seen the scar tracing" | | 6 | "He knew he was being" | | 7 | "She'd seen it in the" | | 8 | "A former paramedic, Herrera moved" | | 9 | "His hand kept drifting to" | | 10 | "Quinn adjusted her grip on" | | 11 | "Morris had died on a" | | 12 | "Quinn had never accepted the" | | 13 | "She wouldn't accept it now." | | 14 | "Herrera turned north, toward Camden." | | 15 | "The crowds thinned as they" | | 16 | "Quinn maintained visual contact, keeping" | | 17 | "The rain intensified, drumming against" | | 18 | "He was leading her somewhere." | | 19 | "She knew it with the" |
| | ratio | 0.859 | |
| 64.10% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 78 | | matches | | 0 | "To enter properly, one needed" |
| | ratio | 0.013 | |
| 17.54% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 10 | | matches | | 0 | "Three blocks ahead, Tomás Herrera moved through the downpour like a man who knew the choreography of the city's underbelly." | | 1 | "Quinn had been tracking him since the Raven's Nest, past the green neon sign that hummed its electric lullaby over the pavement, watching his olive-skinned prof…" | | 2 | "A former paramedic, Herrera moved with the efficient economy of someone who'd carried bodies through chaos." | | 3 | "His hand kept drifting to the Saint Christopher medallion at his throat, fingers worrying the silver as if praying for safe passage." | | 4 | "Quinn maintained visual contact, keeping to the shadows, her boots splashing through gutters that ran red with autumn leaves and urban detritus." | | 5 | "The rain intensified, drumming against the corrugated iron of shuttered shops, creating a white noise that swallowed footsteps." | | 6 | "Quinn stood at the top of the stairs, rain streaming down her face, her breath coming in sharp bursts." | | 7 | "She knew the name from wiretaps, from informants who spoke in whispers about a black market that moved with the moon, currently operating beneath Camden." | | 8 | "Vendors hawked their wares in languages that predated Latin—bottles of liquid shadow, grimoires bound in skin that wasn't quite human, vials of blood that steam…" | | 9 | "Above, the curved ceiling of the old station disappeared into darkness, but here, in the market's heart, light pulsed in rhythms that had nothing to do with ele…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 2 | | matches | | 0 | "he called out, his voice carrying over the market's hum" | | 1 | "he said, the lilt of Seville thick in his accent" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "he called out (call out)" |
| | dialogueSentences | 5 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0.333 | | effectiveRatio | 0.333 | |