| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.29 | | leniency | 0.581 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1318 | | 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) | |
| 16.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1318 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "pumping" | | 1 | "loomed" | | 2 | "sentinels" | | 3 | "raced" | | 4 | "weight" | | 5 | "echoed" | | 6 | "footsteps" | | 7 | "vibrated" | | 8 | "velvet" | | 9 | "hulking" | | 10 | "etched" | | 11 | "scanned" | | 12 | "grave" | | 13 | "glinting" | | 14 | "jaw clenched" | | 15 | "pulsed" | | 16 | "echo" | | 17 | "crystal" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 133 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 133 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 152 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1290 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 1129 | | uniqueNames | 28 | | maxNameDensity | 1.33 | | worstName | "Herrera" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 11 | | Raven | 1 | | Nest | 2 | | Herrera | 15 | | Saint | 1 | | Christopher | 1 | | Wardour | 1 | | Street | 3 | | Shaftesbury | 1 | | Avenue | 1 | | Oxford | 1 | | Regent | 1 | | Camden | 2 | | Morris | 2 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Tube | 2 | | Blitz | 1 | | Veil | 1 | | Market | 1 | | Old | 1 | | Off-books | 1 | | Tomás | 2 | | Sevillian | 1 | | Voices | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Tube" | | 9 | "Tomás" | | 10 | "Voices" |
| | places | | 0 | "Soho" | | 1 | "Wardour" | | 2 | "Street" | | 3 | "Shaftesbury" | | 4 | "Avenue" | | 5 | "Oxford" | | 6 | "Regent" | | 7 | "Camden" | | 8 | "Charing" | | 9 | "Cross" | | 10 | "Road" |
| | globalScore | 0.836 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1290 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 152 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 26.33 | | std | 19.9 | | cv | 0.756 | | sampleLengths | | 0 | 72 | | 1 | 4 | | 2 | 60 | | 3 | 34 | | 4 | 6 | | 5 | 11 | | 6 | 67 | | 7 | 6 | | 8 | 20 | | 9 | 68 | | 10 | 58 | | 11 | 23 | | 12 | 2 | | 13 | 60 | | 14 | 41 | | 15 | 39 | | 16 | 6 | | 17 | 7 | | 18 | 43 | | 19 | 24 | | 20 | 9 | | 21 | 1 | | 22 | 41 | | 23 | 48 | | 24 | 23 | | 25 | 65 | | 26 | 46 | | 27 | 17 | | 28 | 31 | | 29 | 23 | | 30 | 9 | | 31 | 14 | | 32 | 25 | | 33 | 37 | | 34 | 15 | | 35 | 34 | | 36 | 37 | | 37 | 9 | | 38 | 23 | | 39 | 11 | | 40 | 6 | | 41 | 24 | | 42 | 20 | | 43 | 23 | | 44 | 14 | | 45 | 10 | | 46 | 7 | | 47 | 9 | | 48 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 249 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 20 | | semicolonCount | 1 | | flaggedSentences | 17 | | totalSentences | 152 | | ratio | 0.112 | | matches | | 0 | "She locked eyes on the figure ahead—olive skin slick under streetlamps, short curly dark hair plastered flat." | | 1 | "Military precision kept her form tight—arms pumping, worn leather watch ticking against the downpour." | | 2 | "Rain blurred her brown eyes, but she tracked the scar on his left forearm—white lightning against olive—as he shoved through a crowd outside a kebab shop." | | 3 | "No time for backup chatter—radio stayed silent in her pocket." | | 4 | "He kicked a bin lid; it flew, clanging into her shin." | | 5 | "Her mind raced the map—abandoned lines under there, whispers from informants about ghost markets." | | 6 | "Alley dead-ended at iron stairs plunging down—service access to the Tube, chained but pried open." | | 7 | "Rusty links parted under pressure—recent work." | | 8 | "Air thickened, stale with mould and something sharper—ozone, incense." | | 9 | "Voices swelled—hawkers, barters in clipped accents." | | 10 | "Crowds milled—pale faces with slit pupils, hulking figures furred at the knuckles." | | 11 | "He nodded to a vendor, slipped a bone token—carved femur, yellowed—into a palm." | | 12 | "Military bearing held her straight—5'9\" of coiled steel. But this... enchanted baubles pulsed light, air hummed with power she'd dismissed as junkie ravings. Morris's file flashed: claw marks unexplained, blood alchemical. Clique covered it." | | 13 | "She stepped forward. Crowd parted grudging, murmurs peaking. Bone token? None. Gun her only key. Stall left: amulets promising sight beyond sight. She snatched one—cold jade, thrumming—and palmed it. Vendor snarled but backed off." | | 14 | "Curtain loomed, heavy brocade embroidered with ravens—Nest echo? She parted it, gun first. Narrow passage, walls veined crystal pulsing blue. Voices sharpened ahead." | | 15 | "Quinn halted. Two silhouettes against glow—Herrera facing a mirror of himself? No—same build, but this one scarred forearm glinting as he rolled a sleeve." | | 16 | "Passage dripped. Jade amulet warmed her palm, visions flickering—raven wings, lost partner screaming. No." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 950 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.010526315789473684 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002105263157894737 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 152 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 152 | | mean | 8.49 | | std | 5.91 | | cv | 0.696 | | sampleLengths | | 0 | 11 | | 1 | 16 | | 2 | 13 | | 3 | 17 | | 4 | 2 | | 5 | 13 | | 6 | 4 | | 7 | 4 | | 8 | 11 | | 9 | 10 | | 10 | 14 | | 11 | 10 | | 12 | 7 | | 13 | 4 | | 14 | 11 | | 15 | 8 | | 16 | 5 | | 17 | 10 | | 18 | 6 | | 19 | 6 | | 20 | 5 | | 21 | 6 | | 22 | 13 | | 23 | 6 | | 24 | 6 | | 25 | 26 | | 26 | 10 | | 27 | 6 | | 28 | 10 | | 29 | 7 | | 30 | 3 | | 31 | 11 | | 32 | 5 | | 33 | 9 | | 34 | 6 | | 35 | 10 | | 36 | 12 | | 37 | 10 | | 38 | 5 | | 39 | 14 | | 40 | 9 | | 41 | 13 | | 42 | 11 | | 43 | 11 | | 44 | 2 | | 45 | 10 | | 46 | 11 | | 47 | 2 | | 48 | 13 | | 49 | 12 |
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| 100.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.6052631578947368 | | totalSentences | 152 | | uniqueOpeners | 92 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 118 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 118 | | matches | | 0 | "She locked eyes on the" | | 1 | "He vaulted a low railing," | | 2 | "He didn't break stride." | | 3 | "She flashed her badge without" | | 4 | "They clattered, sparking chaos behind" | | 5 | "She hurdled them clean, landing" | | 6 | "He glanced over his shoulder," | | 7 | "She shouldered him aside, suit" | | 8 | "He veered into a service" | | 9 | "He kicked a bin lid;" | | 10 | "He twisted free, scar twisting" | | 11 | "They emerged onto Regent Street," | | 12 | "Her mind raced the map—abandoned" | | 13 | "He slipped on a grate," | | 14 | "He yanked it free, bolted" | | 15 | "She rounded after, boots hydroplaning." | | 16 | "Her gut twisted." | | 17 | "She gripped the chain, rattled" | | 18 | "She holstered it, descended." | | 19 | "Her watch hand swept past" |
| | ratio | 0.263 | |
| 57.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 118 | | matches | | 0 | "Detective Harlow Quinn burst from" | | 1 | "The green neon sign buzzed" | | 2 | "She locked eyes on the" | | 3 | "He vaulted a low railing," | | 4 | "He didn't break stride." | | 5 | "Quinn followed, breath steady despite" | | 6 | "Military precision kept her form" | | 7 | "Herrera cut left, splashing through" | | 8 | "Horns blared as he darted" | | 9 | "Taxis fishtailed on the gloss." | | 10 | "Quinn weaved between them, a" | | 11 | "She flashed her badge without" | | 12 | "Traffic parted like a reluctant" | | 13 | "They clattered, sparking chaos behind" | | 14 | "She hurdled them clean, landing" | | 15 | "Rain blurred her brown eyes," | | 16 | "Steam from sizzling grills mixed" | | 17 | "He glanced over his shoulder," | | 18 | "She shouldered him aside, suit" | | 19 | "Herrera bolted north, towards Oxford" |
| | ratio | 0.805 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 118 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 1 | | matches | | 0 | "Herrera cut left, splashing through a puddle that sprayed her trousers." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |