| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.615 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1072 | | 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) | |
| 30.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1072 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "glinting" | | 1 | "flicker" | | 2 | "weight" | | 3 | "reverberated" | | 4 | "pulse" | | 5 | "quickened" | | 6 | "footsteps" | | 7 | "glint" | | 8 | "silence" | | 9 | "etched" | | 10 | "could feel" | | 11 | "charm" | | 12 | "trembled" |
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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 | 96 | | matches | (empty) | |
| 98.21% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 12 | | markdownWords | 39 | | totalWords | 1062 | | ratio | 0.037 | | matches | | 0 | "3:17 a.m." | | 1 | "The Raven’s Nest" | | 2 | "Unlicensed" | | 3 | "Untraceable" | | 4 | "Unexplained circumstances. Supernatural origins." | | 5 | "Go back." | | 6 | "Let it be someone else’s problem." | | 7 | "This isn’t the world you know" | | 8 | "Morris" | | 9 | "Who are you treating?" | | 10 | "This is what you want" | | 11 | "Answers, or the truth?" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 91.23% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1021 | | uniqueNames | 22 | | maxNameDensity | 1.18 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 12 | | Herrera | 11 | | Metropolitan | 1 | | Police | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Raven | 1 | | Glock | 1 | | Whitechapel | 1 | | London | 1 | | Veil | 5 | | Market | 5 | | Tube | 2 | | Morris | 3 | | Spanish | 1 | | Seville | 1 | | English | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Raven" | | 4 | "Market" | | 5 | "Morris" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Soho" | | 1 | "Charing" | | 2 | "Cross" | | 3 | "Road" | | 4 | "London" | | 5 | "Spanish" | | 6 | "Seville" | | 7 | "English" |
| | globalScore | 0.912 | | windowScore | 1 | |
| 76.47% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 2 | | matches | | 0 | "as though steadying himself" | | 1 | "felt like urban legend" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1062 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 31.24 | | std | 26.26 | | cv | 0.841 | | sampleLengths | | 0 | 69 | | 1 | 101 | | 2 | 60 | | 3 | 44 | | 4 | 12 | | 5 | 3 | | 6 | 76 | | 7 | 4 | | 8 | 9 | | 9 | 82 | | 10 | 39 | | 11 | 20 | | 12 | 50 | | 13 | 6 | | 14 | 44 | | 15 | 17 | | 16 | 13 | | 17 | 63 | | 18 | 50 | | 19 | 6 | | 20 | 38 | | 21 | 33 | | 22 | 5 | | 23 | 7 | | 24 | 42 | | 25 | 24 | | 26 | 4 | | 27 | 30 | | 28 | 19 | | 29 | 59 | | 30 | 11 | | 31 | 6 | | 32 | 5 | | 33 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 96 | | matches | | |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 173 | | matches | | 0 | "wasn’t just running" | | 1 | "was leading" | | 2 | "wasn’t fleeing" | | 3 | "was fleeing" | | 4 | "was waiting" | | 5 | "was only beginning" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 1 | | flaggedSentences | 9 | | totalSentences | 101 | | ratio | 0.089 | | matches | | 0 | "The chase had started three blocks back, a shadow slipping from alley to alley, but now her instincts sharpened—the figure ahead wasn’t just running." | | 1 | "*3:17 a.m.* Quinn didn’t need the time; the adrenaline in her veins kept better tempo." | | 2 | "The suspect—a man with dark curls plastered to his forehead, a silver medallion glinting under his collar—had the gait of someone who’d lived through more than his twenty-odd years." | | 3 | "A flicker of movement—his fingers brushing the scar along his left forearm, a crescent wound that had once slit his throat." | | 4 | "A distant clang reverberated—metal on metal." | | 5 | "The rumors—hidden beneath derelict Tube stations, shifting locales with the moon—had always felt like urban legend." | | 6 | "Herrera had reached the threshold—a grating that had been pried open, leading to a cavernous tunnel." | | 7 | "*Morris*—" | | 8 | "The vendor—small, with a face like a falcon—began muttering in a language that made the air hum." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1034 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Ex-paramedic, unauthorized treatments," |
| | adverbCount | 26 | | adverbRatio | 0.025145067698259187 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004835589941972921 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 10.51 | | std | 6.52 | | cv | 0.62 | | sampleLengths | | 0 | 18 | | 1 | 22 | | 2 | 24 | | 3 | 5 | | 4 | 20 | | 5 | 15 | | 6 | 29 | | 7 | 2 | | 8 | 22 | | 9 | 7 | | 10 | 6 | | 11 | 20 | | 12 | 16 | | 13 | 12 | | 14 | 9 | | 15 | 3 | | 16 | 3 | | 17 | 21 | | 18 | 4 | | 19 | 10 | | 20 | 5 | | 21 | 1 | | 22 | 12 | | 23 | 3 | | 24 | 17 | | 25 | 15 | | 26 | 18 | | 27 | 12 | | 28 | 14 | | 29 | 4 | | 30 | 6 | | 31 | 3 | | 32 | 26 | | 33 | 13 | | 34 | 3 | | 35 | 3 | | 36 | 4 | | 37 | 16 | | 38 | 7 | | 39 | 5 | | 40 | 5 | | 41 | 12 | | 42 | 2 | | 43 | 8 | | 44 | 17 | | 45 | 14 | | 46 | 6 | | 47 | 10 | | 48 | 12 | | 49 | 15 |
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| 61.72% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.37623762376237624 | | totalSentences | 101 | | uniqueOpeners | 38 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 90 | | matches | | 0 | "He was leading her somewhere." | | 1 | "Her left wrist felt the" | | 2 | "He paused at the corner" | | 3 | "she called, her voice cutting" | | 4 | "He didn’t turn." | | 5 | "She’d read the file." | | 6 | "He weaved through the narrow" | | 7 | "Her military-trained stride kept her" | | 8 | "He vaulted a low fence," | | 9 | "She reached the mouth of" | | 10 | "Her pulse quickened." | | 11 | "It had to be." | | 12 | "He wasn’t fleeing the law." | | 13 | "He was fleeing into it." | | 14 | "She pulled the flashlight from" | | 15 | "Her flashlight caught the glint" | | 16 | "He paused, hand on the" | | 17 | "he said in Spanish, his" | | 18 | "She’d learned long ago that" | | 19 | "He slipped through the gap" |
| | ratio | 0.333 | |
| 48.89% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 90 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "The chase had started three" | | 3 | "He was leading her somewhere." | | 4 | "Her left wrist felt the" | | 5 | "The suspect—a man with dark" | | 6 | "Name didn’t mean much in" | | 7 | "Quinn had waited for this" | | 8 | "He paused at the corner" | | 9 | "The green neon of *The" | | 10 | "Quinn edged closer, her hand" | | 11 | "she called, her voice cutting" | | 12 | "He didn’t turn." | | 13 | "A flicker of movement—his fingers" | | 14 | "She’d read the file." | | 15 | "A knife attack in Whitechapel," | | 16 | "Herrera exhaled, a slow, deliberate" | | 17 | "Quinn gave chase." | | 18 | "He weaved through the narrow" | | 19 | "Her military-trained stride kept her" |
| | ratio | 0.822 | |
| 55.56% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 90 | | matches | | 0 | "Now, chasing a paramedic who’d" |
| | ratio | 0.011 | |
| 72.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 5 | | matches | | 0 | "The suspect—a man with dark curls plastered to his forehead, a silver medallion glinting under his collar—had the gait of someone who’d lived through more than …" | | 1 | "He paused at the corner of Charing Cross Road, pressing his back to a brick wall as though steadying himself." | | 2 | "A flicker of movement—his fingers brushing the scar along his left forearm, a crescent wound that had once slit his throat." | | 3 | "Herrera had reached the threshold—a grating that had been pried open, leading to a cavernous tunnel." | | 4 | "A man had collapsed near a stall selling dried herbs, his body convulsing." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 4 | | matches | | 0 | "she called, her voice cutting through the rain" | | 1 | "he said, his accent soft as a summer wind over Seville" | | 2 | "he snapped, his hands already at work" | | 3 | "he said, starting to walk away" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "he snapped (snap)" |
| | dialogueSentences | 13 | | tagDensity | 0.462 | | leniency | 0.923 | | rawRatio | 0.333 | | effectiveRatio | 0.308 | |