| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.24 | | leniency | 0.48 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1435 | | 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) | |
| 37.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1435 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "pulse" | | 1 | "shattered" | | 2 | "stomach" | | 3 | "porcelain" | | 4 | "traced" | | 5 | "echoed" | | 6 | "standard" | | 7 | "glint" | | 8 | "echoing" | | 9 | "whisper" | | 10 | "silk" | | 11 | "flickered" | | 12 | "trembled" | | 13 | "pulsed" | | 14 | "flicked" | | 15 | "fluttered" | | 16 | "chill" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 116 | | matches | (empty) | |
| 93.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 116 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1415 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 1178 | | uniqueNames | 21 | | maxNameDensity | 1.61 | | worstName | "Shaw" | | maxWindowNameDensity | 3 | | worstWindowName | "Shaw" | | discoveredNames | | Thames | 1 | | Detective | 2 | | Harlow | 15 | | Quinn | 1 | | Nathan | 1 | | Shaw | 19 | | Maglite | 1 | | Dior | 1 | | Three | 2 | | Uniform | 1 | | Tube | 2 | | Camden | 2 | | King | 1 | | Cross | 1 | | Adam | 1 | | Victorian | 1 | | Whitechapel | 1 | | Morris | 2 | | Rawlins | 2 | | Eva | 4 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Nathan" | | 3 | "Shaw" | | 4 | "Dior" | | 5 | "Uniform" | | 6 | "King" | | 7 | "Cross" | | 8 | "Adam" | | 9 | "Morris" | | 10 | "Eva" | | 11 | "Kowalski" |
| | places | | | globalScore | 0.694 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like dried blood" |
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| 58.66% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.413 | | wordCount | 1415 | | matches | | 0 | "not the antiseptic leftover from a late-night shift, but powdered cologne layered over something metallic" | | 1 | "not Adam’s apple, but something grafted" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 135 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 24.4 | | std | 24.33 | | cv | 0.997 | | sampleLengths | | 0 | 126 | | 1 | 39 | | 2 | 3 | | 3 | 71 | | 4 | 34 | | 5 | 8 | | 6 | 74 | | 7 | 3 | | 8 | 12 | | 9 | 12 | | 10 | 44 | | 11 | 13 | | 12 | 47 | | 13 | 27 | | 14 | 31 | | 15 | 2 | | 16 | 25 | | 17 | 36 | | 18 | 51 | | 19 | 23 | | 20 | 17 | | 21 | 11 | | 22 | 73 | | 23 | 39 | | 24 | 13 | | 25 | 34 | | 26 | 7 | | 27 | 14 | | 28 | 85 | | 29 | 26 | | 30 | 29 | | 31 | 8 | | 32 | 5 | | 33 | 28 | | 34 | 7 | | 35 | 16 | | 36 | 31 | | 37 | 37 | | 38 | 7 | | 39 | 9 | | 40 | 3 | | 41 | 24 | | 42 | 3 | | 43 | 1 | | 44 | 68 | | 45 | 6 | | 46 | 12 | | 47 | 11 | | 48 | 5 | | 49 | 10 |
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| 84.09% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 116 | | matches | | 0 | "been replaced" | | 1 | "was sewn" | | 2 | "been disturbed" | | 3 | "been lifted" | | 4 | "been severed" | | 5 | "was unzipped" | | 6 | "been hollowed" | | 7 | "was waxed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 206 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 19 | | semicolonCount | 1 | | flaggedSentences | 17 | | totalSentences | 135 | | ratio | 0.126 | | matches | | 0 | "That came first—no polite preamble, no cautious knock on the locked door of a Thames warehouse." | | 1 | "Internal light slashed across her field boots—combat, broken-in, no scuff marks she couldn’t account for—and she stepped inside without waiting for the shout that never came." | | 2 | "The shout was part of the script; the absence of it left the neon sign above the racks of unsold couture flickering like a dying pulse." | | 3 | "Detective DS Nathan Shaw— arm still braced for a burst of noise that wouldn’t arrive—lowered the Maglite he’d been swinging like a billy club." | | 4 | "Boots thudded past a half-dismantled runway, past mannequins dressed in last season’s mourning chic—pleated skirts the shade of old bruises." | | 5 | "Its porcelain face had been replaced by a clay mask—runes carved deep, filled with what looked like dried blood." | | 6 | "Harlow stepped forward, keeping her boots in the salt ring—standard but pointless if the killer had left a spirit behind." | | 7 | "A single footprint—bare, female, size six." | | 8 | "Her watch gave a single, subtle vibration—alert set for every lunar event." | | 9 | "Not gouged out—evaporated." | | 10 | "The optic nerves hadn’t been severed—they had never existed." | | 11 | "Industrial freezer, industrial racks—designer silk gowns rotated like museum specimens." | | 12 | "The beam trembled on the man’s throat— not Adam’s apple, but something grafted." | | 13 | "Harlow’s watch buzzed again—no longer lunar." | | 14 | "Shaw’s torchlight crawled the basement’s brickwork, landing on a fresh gouge: initials scratched deep, over and over—A.R." | | 15 | "He’d logged a witness statement from a girl named Eva Kowalski—curly red hair, round glasses—claimed she’d seen Rawlins arguing with someone in the abandoned portion of the Camden Tube tunnels the night he disappeared." | | 16 | "From the darkest corner, a whisper—Eva’s voice, but layered, distorted, like a recording run through water." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1203 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.02493765586034913 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005818786367414797 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 10.48 | | std | 7.65 | | cv | 0.729 | | sampleLengths | | 0 | 9 | | 1 | 16 | | 2 | 16 | | 3 | 33 | | 4 | 26 | | 5 | 26 | | 6 | 24 | | 7 | 15 | | 8 | 3 | | 9 | 3 | | 10 | 20 | | 11 | 23 | | 12 | 7 | | 13 | 10 | | 14 | 8 | | 15 | 16 | | 16 | 18 | | 17 | 4 | | 18 | 4 | | 19 | 15 | | 20 | 11 | | 21 | 19 | | 22 | 15 | | 23 | 4 | | 24 | 10 | | 25 | 3 | | 26 | 9 | | 27 | 3 | | 28 | 12 | | 29 | 20 | | 30 | 11 | | 31 | 13 | | 32 | 13 | | 33 | 8 | | 34 | 28 | | 35 | 11 | | 36 | 3 | | 37 | 22 | | 38 | 2 | | 39 | 9 | | 40 | 6 | | 41 | 3 | | 42 | 13 | | 43 | 2 | | 44 | 5 | | 45 | 20 | | 46 | 9 | | 47 | 9 | | 48 | 18 | | 49 | 25 |
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| 67.41% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4222222222222222 | | totalSentences | 135 | | uniqueOpeners | 57 | |
| 30.86% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 108 | | matches | | 0 | "Just the heel of Detective" |
| | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 108 | | matches | | 0 | "His cap tilted back, a" | | 1 | "They weren’t here for a" | | 2 | "She didn’t need to." | | 3 | "Its porcelain face had been" | | 4 | "She’d traced it on a" | | 5 | "She exhaled through her nose," | | 6 | "He fumbled a notebook" | | 7 | "She crouched, extending a gloved" | | 8 | "She stood, letting the torchlight" | | 9 | "Her watch gave a single," | | 10 | "She pried the clay lid" | | 11 | "Its eyes were clay too," | | 12 | "It was waxed hemp, the" | | 13 | "Her mind pulled the date" | | 14 | "She reached down." | | 15 | "He’d logged a witness statement" | | 16 | "She’d bought the heads." | | 17 | "She’d signed the receipt." | | 18 | "She reached for her radio." |
| | ratio | 0.176 | |
| 57.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 108 | | matches | | 0 | "The bones in the mannequin’s" | | 1 | "That came first—no polite preamble," | | 2 | "The hinges themselves were the" | | 3 | "The shout was part of" | | 4 | "Detective DS Nathan Shaw— arm" | | 5 | "His cap tilted back, a" | | 6 | "Harlow didn’t slow." | | 7 | "Boots thudded past a half-dismantled" | | 8 | "The scent was wrong too:" | | 9 | "They weren’t here for a" | | 10 | "This had been a setup" | | 11 | "Shaw jogged to catch up," | | 12 | "Harlow didn’t touch anything." | | 13 | "She didn’t need to." | | 14 | "The centrepiece was a shattered" | | 15 | "Its porcelain face had been" | | 16 | "The hair was real, dark" | | 17 | "Harlow knew that hairline." | | 18 | "She’d traced it on a" | | 19 | "Shaw’s answer was a swallow" |
| | ratio | 0.806 | |
| 46.30% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 108 | | matches | | 0 | "Either the token was inactive" |
| | ratio | 0.009 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 6 | | matches | | 0 | "The hinges themselves were the second thing to notice: they were brand-new brass, still bright, bolted directly into century-old oak as though someone had wante…" | | 1 | "Detective DS Nathan Shaw— arm still braced for a burst of noise that wouldn’t arrive—lowered the Maglite he’d been swinging like a billy club." | | 2 | "The hair was real, dark brown, caught as though caught mid-swirl by some sudden wind." | | 3 | "Harlow was already moving, Shaw’s torch jolting behind like a strobe." | | 4 | "Harlow knelt, avoiding the drips of something that could be candle wax or fast-decaying flesh." | | 5 | "Its eyes were clay too, but the sockets had been hollowed then refilled with black liquid that dripped in slow motion, pooling on the freezer’s plastic tray." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 25 | | tagDensity | 0.04 | | leniency | 0.08 | | rawRatio | 1 | | effectiveRatio | 0.08 | |