| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "a man said quietly [quietly]" |
| | dialogueSentences | 23 | | tagDensity | 0.391 | | leniency | 0.783 | | rawRatio | 0.111 | | effectiveRatio | 0.087 | |
| 83.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1224 | | totalAiIsmAdverbs | 4 | | 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) | |
| 22.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1224 | | totalAiIsms | 19 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | word | "the last thing" | | count | 1 |
| | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "scanning" | | 1 | "measured" | | 2 | "reminder" | | 3 | "flicker" | | 4 | "pulse" | | 5 | "jaw clenched" | | 6 | "familiar" | | 7 | "calculated" | | 8 | "rhythmic" | | 9 | "glint" | | 10 | "the last thing" | | 11 | "weight" | | 12 | "gloom" | | 13 | "electric" | | 14 | "pulsed" | | 15 | "whisper" | | 16 | "echoed" | | 17 | "flickered" |
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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 | 123 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 123 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 137 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 14 | | markdownWords | 30 | | totalWords | 1243 | | ratio | 0.024 | | matches | | 0 | "There." | | 1 | "knew" | | 2 | "drip-drip" | | 3 | "“They’re not human. And they’re beneath the city.”" | | 4 | "click" | | 5 | "other" | | 6 | "legal" | | 7 | "him" | | 8 | "Herrera — knows about bleeders. Treats them. Ex-NHS. Watch him." | | 9 | "were" | | 10 | "them" | | 11 | "I’ve" | | 12 | "you" | | 13 | "human." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 986 | | uniqueNames | 16 | | maxNameDensity | 0.81 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Tomás" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 8 | | Berwick | 1 | | Street | 1 | | Oxfords | 1 | | Raven | 2 | | Nest | 2 | | Morris | 4 | | Veil | 2 | | Market | 2 | | Saint | 1 | | Christopher | 1 | | Herrera | 2 | | Rain | 1 | | Tomás | 6 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Oxfords" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Herrera" | | 9 | "Rain" | | 10 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "Berwick" | | 2 | "Street" |
| | globalScore | 1 | | windowScore | 0.667 | |
| 71.88% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like bone" | | 1 | "something like burnt sugar" |
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| 39.10% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.609 | | wordCount | 1243 | | matches | | 0 | "not electric, not flame, but flickering with a pale blue light" | | 1 | "not flame, but flickering with a pale blue light" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 137 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 25.9 | | std | 20.5 | | cv | 0.792 | | sampleLengths | | 0 | 82 | | 1 | 20 | | 2 | 68 | | 3 | 60 | | 4 | 4 | | 5 | 55 | | 6 | 3 | | 7 | 57 | | 8 | 42 | | 9 | 38 | | 10 | 16 | | 11 | 29 | | 12 | 2 | | 13 | 41 | | 14 | 17 | | 15 | 37 | | 16 | 3 | | 17 | 12 | | 18 | 68 | | 19 | 5 | | 20 | 71 | | 21 | 12 | | 22 | 9 | | 23 | 21 | | 24 | 43 | | 25 | 2 | | 26 | 23 | | 27 | 17 | | 28 | 5 | | 29 | 17 | | 30 | 4 | | 31 | 14 | | 32 | 17 | | 33 | 5 | | 34 | 32 | | 35 | 50 | | 36 | 17 | | 37 | 19 | | 38 | 21 | | 39 | 36 | | 40 | 10 | | 41 | 24 | | 42 | 29 | | 43 | 24 | | 44 | 17 | | 45 | 9 | | 46 | 22 | | 47 | 14 |
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| 96.71% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 123 | | matches | | 0 | "been waterlogged" | | 1 | "was burned" | | 2 | "was obscured" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 171 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 18 | | semicolonCount | 0 | | flaggedSentences | 16 | | totalSentences | 137 | | ratio | 0.117 | | matches | | 0 | "At her left wrist, the worn leather watch ticked quietly beneath the cuff of her coat — a reminder of time slipping away." | | 1 | "The figure ahead was lean, fast — familiar." | | 2 | "Just a dead end, a dumpster, and a faint glint on the ground — something small and pale." | | 3 | "She’d seen that symbol before — in the margins of Morris’s final notebook, the one she’d recovered from his flat after the coroner released the body." | | 4 | "A gust of wind stirred the alley, carrying the scent of damp earth and something sharper — ozone, maybe." | | 5 | "Behind the dumpster, a section of the pavement — too clean, too uniform." | | 6 | "Just the faint hum of electricity — and the unmistakable scent of something *other*." | | 7 | "But the face of DS Morris — hollow-eyed, skin waxy, veins dark as ink beneath the surface — flashed behind her eyes." | | 8 | "Lanterns hung from rusted beams — not electric, not flame, but flickering with a pale blue light that cast no shadows." | | 9 | "And then she saw *him* — the man she’d chased." | | 10 | "The hum of the market pressed in — low chatter in languages she didn’t recognize, the clink of vials, the whisper of wings from somewhere above." | | 11 | "Then — a hand on her arm." | | 12 | "A silver medallion — Saint Christopher — glinted at his throat." | | 13 | "But she’d seen his name in Morris’s notes: *Herrera — knows about bleeders." | | 14 | "Somewhere in the market, a bell chimed — three slow, resonant tones." | | 15 | "Of the dreams she still had — him calling her name from beneath the city." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 971 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.037075180226570546 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.009268795056642637 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 137 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 137 | | mean | 9.07 | | std | 7.39 | | cv | 0.815 | | sampleLengths | | 0 | 17 | | 1 | 18 | | 2 | 14 | | 3 | 10 | | 4 | 23 | | 5 | 5 | | 6 | 11 | | 7 | 3 | | 8 | 1 | | 9 | 12 | | 10 | 16 | | 11 | 8 | | 12 | 19 | | 13 | 8 | | 14 | 3 | | 15 | 2 | | 16 | 15 | | 17 | 12 | | 18 | 15 | | 19 | 18 | | 20 | 4 | | 21 | 3 | | 22 | 2 | | 23 | 4 | | 24 | 18 | | 25 | 6 | | 26 | 2 | | 27 | 6 | | 28 | 2 | | 29 | 12 | | 30 | 3 | | 31 | 26 | | 32 | 8 | | 33 | 8 | | 34 | 15 | | 35 | 19 | | 36 | 2 | | 37 | 9 | | 38 | 12 | | 39 | 4 | | 40 | 13 | | 41 | 2 | | 42 | 2 | | 43 | 17 | | 44 | 5 | | 45 | 2 | | 46 | 3 | | 47 | 6 | | 48 | 7 | | 49 | 2 |
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| 52.80% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.36496350364963503 | | totalSentences | 137 | | uniqueOpeners | 50 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 9 | | totalSentences | 92 | | matches | | 0 | "Then the figure vanished." | | 1 | "Just a dead end, a" | | 2 | "Then she saw it." | | 3 | "Just the faint hum of" | | 4 | "Then the tunnel leveled out," | | 5 | "Then — a hand on" | | 6 | "Somewhere in the market, a" | | 7 | "Then up, past Tomás, into" | | 8 | "Then sighed, reaching into his" |
| | ratio | 0.098 | |
| 93.91% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 92 | | matches | | 0 | "She moved with a brisk," | | 1 | "Her breath came in measured" | | 2 | "She broke into a run," | | 3 | "Her sharp jaw clenched as" | | 4 | "She’d seen that build before," | | 5 | "She crouched, gloved fingers retrieving" | | 6 | "Her breath hitched." | | 7 | "She’d seen that symbol before" | | 8 | "Her fingers brushed the grip" | | 9 | "She knelt again, fingers probing." | | 10 | "She had no warrant." | | 11 | "She flipped on her torch," | | 12 | "She was in the underground." | | 13 | "He stood at a stall," | | 14 | "Her watch ticked once, twice." | | 15 | "She moved closer, staying low." | | 16 | "She whirled, torch raised, hand" | | 17 | "He stepped into the light." | | 18 | "She knew him." | | 19 | "he said, voice calm, eyes" |
| | ratio | 0.315 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 92 | | matches | | 0 | "The rain fell in sheets" | | 1 | "Detective Harlow Quinn pulled her" | | 2 | "She moved with a brisk," | | 3 | "Her breath came in measured" | | 4 | "A silhouette darted between two" | | 5 | "Quinn’s pulse surged." | | 6 | "She broke into a run," | | 7 | "Her sharp jaw clenched as" | | 8 | "The figure ahead was lean," | | 9 | "She’d seen that build before," | | 10 | "The suspect ducked into a" | | 11 | "Quinn followed without hesitation, her" | | 12 | "The alley narrowed, walls closing" | | 13 | "The sound of the city" | | 14 | "Quinn slowed, scanning." | | 15 | "She crouched, gloved fingers retrieving" | | 16 | "Her breath hitched." | | 17 | "She’d seen that symbol before" | | 18 | "The pages had been waterlogged," | | 19 | "The last thing he’d written" |
| | ratio | 0.696 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, voice calm, eyes warm brown but wary" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | 0 | "he corrected (correct)" |
| | dialogueSentences | 23 | | tagDensity | 0.348 | | leniency | 0.696 | | rawRatio | 0.125 | | effectiveRatio | 0.087 | |