| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1285 | | 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) | |
| 64.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1285 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "flickered" | | 1 | "weight" | | 2 | "fragmented" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "measured" | | 6 | "flicker" |
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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 | 120 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 120 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1276 | | ratio | 0 | | matches | (empty) | |
| 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 | 37 | | wordCount | 1126 | | uniqueNames | 20 | | maxNameDensity | 0.89 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 2 | | Guinness | 1 | | Raven | 1 | | Nest | 1 | | Dean | 1 | | Street | 2 | | Shaftesbury | 1 | | Avenue | 1 | | Soho | 1 | | Maglite | 1 | | Old | 1 | | Compton | 1 | | Morris | 3 | | English | 1 | | Harlow | 10 | | Rainwater | 2 | | Tube | 1 | | Hampstead | 1 | | Silas | 4 | | Tommy | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Maglite" | | 3 | "Morris" | | 4 | "Harlow" | | 5 | "Rainwater" | | 6 | "Silas" | | 7 | "Tommy" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Shaftesbury" | | 3 | "Avenue" | | 4 | "Soho" | | 5 | "Old" | | 6 | "Compton" | | 7 | "Hampstead" |
| | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | glossingSentenceCount | 5 | | matches | | 0 | "felt like algae but stank like tinned r" | | 1 | "corridor that seemed to drink light" | | 2 | "looked like a child's molar on a thin sil" | | 3 | "sounded like a bolt firing" | | 4 | "quite human—high cheekbones too sharp, eyes reflecting green neon like a cat's" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.784 | | wordCount | 1276 | | matches | | 0 | "not a plea but a confirmation: \"They're real" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 25.02 | | std | 16.86 | | cv | 0.674 | | sampleLengths | | 0 | 1 | | 1 | 61 | | 2 | 5 | | 3 | 37 | | 4 | 25 | | 5 | 20 | | 6 | 53 | | 7 | 27 | | 8 | 6 | | 9 | 1 | | 10 | 43 | | 11 | 15 | | 12 | 34 | | 13 | 42 | | 14 | 5 | | 15 | 41 | | 16 | 2 | | 17 | 56 | | 18 | 8 | | 19 | 24 | | 20 | 45 | | 21 | 29 | | 22 | 32 | | 23 | 50 | | 24 | 30 | | 25 | 4 | | 26 | 39 | | 27 | 30 | | 28 | 5 | | 29 | 7 | | 30 | 25 | | 31 | 35 | | 32 | 6 | | 33 | 32 | | 34 | 22 | | 35 | 41 | | 36 | 29 | | 37 | 9 | | 38 | 2 | | 39 | 42 | | 40 | 12 | | 41 | 2 | | 42 | 31 | | 43 | 41 | | 44 | 48 | | 45 | 14 | | 46 | 43 | | 47 | 34 | | 48 | 15 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 120 | | matches | | |
| 32.50% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 199 | | matches | | 0 | "were already splashing" | | 1 | "was already moving" | | 2 | "was counting" | | 3 | "wasn't losing" | | 4 | "wasn't breathing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 3 | | flaggedSentences | 9 | | totalSentences | 133 | | ratio | 0.068 | | matches | | 0 | "The clique didn't leave corpses behind—they left messages, offerings, signature stains." | | 1 | "A figure—dark hoodie hugging broad shoulders—darted around the corner of Dean Street heading south towards Shaftesbury Avenue and the warren of Soho backstreets." | | 2 | "She'd been soft back in uniform; wrapped in duty and foreseeable consequences." | | 3 | "Something—not human—scuttled sideways out of the light." | | 4 | "The wound on her partner's thigh came back to her, brief and vivid as a cigarette burn—Morris's voice rasping, \"Christ, they've got teeth now, boss.\"" | | 5 | "The neon spelled \"RAVEN\" backwards—she figured it out in the same instant the door locked with an industrial groan." | | 6 | "Silas tilted his head, just enough to spotlight a face that wasn't quite human—high cheekbones too sharp, eyes reflecting green neon like a cat's." | | 7 | "The walls curved upward into vaulted brick ribs; the ceiling dripped with stalactites of ancient soot." | | 8 | "Turn back to the hapless Silas and demand proof of life for his market; or trust the tunnel rat instincts that had kept her alive so far and follow the hooded figure into the dark where the rules stopped mattering." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1144 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.03671328671328671 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004370629370629371 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 9.59 | | std | 7.15 | | cv | 0.746 | | sampleLengths | | 0 | 9 | | 1 | 15 | | 2 | 17 | | 3 | 20 | | 4 | 5 | | 5 | 14 | | 6 | 12 | | 7 | 11 | | 8 | 7 | | 9 | 12 | | 10 | 6 | | 11 | 12 | | 12 | 8 | | 13 | 12 | | 14 | 16 | | 15 | 9 | | 16 | 11 | | 17 | 5 | | 18 | 14 | | 19 | 13 | | 20 | 2 | | 21 | 2 | | 22 | 2 | | 23 | 1 | | 24 | 12 | | 25 | 8 | | 26 | 23 | | 27 | 7 | | 28 | 8 | | 29 | 8 | | 30 | 17 | | 31 | 4 | | 32 | 5 | | 33 | 19 | | 34 | 15 | | 35 | 8 | | 36 | 5 | | 37 | 2 | | 38 | 12 | | 39 | 12 | | 40 | 15 | | 41 | 2 | | 42 | 8 | | 43 | 24 | | 44 | 10 | | 45 | 14 | | 46 | 8 | | 47 | 15 | | 48 | 9 | | 49 | 5 |
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| 86.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5263157894736842 | | totalSentences | 133 | | uniqueOpeners | 70 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 104 | | matches | | 0 | "Too fast for someone with" | | 1 | "Then the cover vanished beneath" | | 2 | "Too fast for rat." | | 3 | "Just the quiet sound of" | | 4 | "Then the ladder ended." |
| | ratio | 0.048 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 104 | | matches | | 0 | "She tossed a twenty on" | | 1 | "It landed in a puddle" | | 2 | "She'd smoked this particular rat" | | 3 | "She kicked it off its" | | 4 | "He moved too clean for" | | 5 | "She could just make out" | | 6 | "He knew she was there." | | 7 | "His hips swayed like he" | | 8 | "She wasn't losing him again." | | 9 | "Her scalp prickled where salt" | | 10 | "She'd been soft back in" | | 11 | "He'd already seen her face" | | 12 | "His boot struck a manhole" | | 13 | "He dropped through like a" | | 14 | "She pulled the torch tight," | | 15 | "She'd be descending into a" | | 16 | "She holstered the torch and" | | 17 | "Her boots landed on a" | | 18 | "She got three steps before" | | 19 | "She tasted ozone and iron." |
| | ratio | 0.269 | |
| 75.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 104 | | matches | | 0 | "The pub door swung inward," | | 1 | "Harlow Quinn ducked through the" | | 2 | "The barman looked up from" | | 3 | "Recognition flickered in his flat" | | 4 | "She tossed a twenty on" | | 5 | "It landed in a puddle" | | 6 | "The barman exhaled through his" | | 7 | "Harlow was already moving, the" | | 8 | "Each step carried the copper" | | 9 | "She'd smoked this particular rat" | | 10 | "The clique didn't leave corpses" | | 11 | "Morris hadn't been an exception." | | 12 | "The door at the top" | | 13 | "She kicked it off its" | | 14 | "The alley behind the Raven's" | | 15 | "Rain slashed both ways, cutting" | | 16 | "A figure—dark hoodie hugging broad" | | 17 | "He moved too clean for" | | 18 | "Harlow's Maglite carved a tunnel" | | 19 | "She could just make out" |
| | ratio | 0.769 | |
| 48.08% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 104 | | matches | | | ratio | 0.01 | |
| 66.87% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 5 | | matches | | 0 | "Traffic shrieked left on Old Compton Street, tyres hissing through puddles that reflected neon and threat in equal measure." | | 1 | "Her boots landed on a platform slick with something that felt like algae but stank like tinned regret." | | 2 | "Silas tilted his head, just enough to spotlight a face that wasn't quite human—high cheekbones too sharp, eyes reflecting green neon like a cat's." | | 3 | "The double doors groaned open inward this time, revealing a cavernous space that smelled of ozone and fennel." | | 4 | "Turn back to the hapless Silas and demand proof of life for his market; or trust the tunnel rat instincts that had kept her alive so far and follow the hooded f…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "the figure continued (continue)" |
| | dialogueSentences | 18 | | tagDensity | 0.056 | | leniency | 0.111 | | rawRatio | 1 | | effectiveRatio | 0.111 | |