| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 83.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 912 | | totalAiIsmAdverbs | 3 | | 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) | |
| 23.25% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 912 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "flickered" | | 1 | "footsteps" | | 2 | "pulse" | | 3 | "jaw clenched" | | 4 | "flicker" | | 5 | "rhythmic" | | 6 | "stomach" | | 7 | "wavering" | | 8 | "gloom" | | 9 | "trembled" | | 10 | "whisper" |
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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 | 102 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 102 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 9 | | markdownWords | 16 | | totalWords | 900 | | ratio | 0.018 | | matches | | 0 | "The Raven’s Nest" | | 1 | "Twenty-three forty-seven." | | 2 | "clink" | | 3 | "Trust your gut." | | 4 | "clink" | | 5 | "gate" | | 6 | "presence" | | 7 | "challenge" | | 8 | "Trust your gut." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.44% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 900 | | uniqueNames | 10 | | maxNameDensity | 1.11 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 1 | | Harlow | 1 | | Quinn | 10 | | Tomás | 6 | | Herrera | 1 | | Morris | 3 | | Veil | 2 | | Market | 2 | | Camden | 1 | | Tube | 1 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Morris" | | 6 | "Camden" |
| | places | (empty) | | globalScore | 0.944 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 1 | | matches | | 0 | "maps that seemed to shift when she wasn’t looking" |
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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 | 900 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 102 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 24.32 | | std | 21.18 | | cv | 0.871 | | sampleLengths | | 0 | 75 | | 1 | 8 | | 2 | 78 | | 3 | 19 | | 4 | 75 | | 5 | 35 | | 6 | 7 | | 7 | 5 | | 8 | 15 | | 9 | 3 | | 10 | 38 | | 11 | 36 | | 12 | 60 | | 13 | 15 | | 14 | 41 | | 15 | 32 | | 16 | 2 | | 17 | 44 | | 18 | 8 | | 19 | 11 | | 20 | 27 | | 21 | 34 | | 22 | 5 | | 23 | 19 | | 24 | 10 | | 25 | 6 | | 26 | 5 | | 27 | 18 | | 28 | 33 | | 29 | 4 | | 30 | 5 | | 31 | 4 | | 32 | 36 | | 33 | 5 | | 34 | 31 | | 35 | 38 | | 36 | 13 |
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| 98.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 102 | | matches | | 0 | "been solved" | | 1 | "was made" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 148 | | matches | | 0 | "was going" | | 1 | "was, chasing" | | 2 | "was selling" | | 3 | "was selling" | | 4 | "were screaming" | | 5 | "wasn’t looking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 102 | | ratio | 0.088 | | matches | | 0 | "The rain lashed her face, turning the air thick with the scent of wet asphalt and something older—something metallic, like old blood." | | 1 | "Her watch—worn leather, the kind that had seen too many nights—ticked." | | 2 | "Then she heard it—a low, rhythmic *clink*." | | 3 | "The alley was narrow, the air thick with the scent of damp earth and something else—something sweet, like burnt sugar." | | 4 | "Then she saw it—a set of rusted metal stairs, half-buried in the dirt." | | 5 | "The stairs led her down into darkness, the air thick with the scent of damp stone and something else—something ancient." | | 6 | "Then she heard it—a low, rhythmic *clink* again." | | 7 | "Then she saw it—a door." | | 8 | "But then she heard it—a whisper." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 912 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.03289473684210526 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005482456140350877 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 8.82 | | std | 6.21 | | cv | 0.704 | | sampleLengths | | 0 | 23 | | 1 | 30 | | 2 | 22 | | 3 | 8 | | 4 | 18 | | 5 | 9 | | 6 | 16 | | 7 | 21 | | 8 | 2 | | 9 | 12 | | 10 | 11 | | 11 | 6 | | 12 | 2 | | 13 | 7 | | 14 | 18 | | 15 | 15 | | 16 | 6 | | 17 | 20 | | 18 | 9 | | 19 | 12 | | 20 | 11 | | 21 | 6 | | 22 | 6 | | 23 | 7 | | 24 | 5 | | 25 | 8 | | 26 | 4 | | 27 | 3 | | 28 | 3 | | 29 | 4 | | 30 | 4 | | 31 | 11 | | 32 | 19 | | 33 | 3 | | 34 | 4 | | 35 | 12 | | 36 | 5 | | 37 | 12 | | 38 | 15 | | 39 | 11 | | 40 | 20 | | 41 | 3 | | 42 | 11 | | 43 | 11 | | 44 | 2 | | 45 | 2 | | 46 | 11 | | 47 | 10 | | 48 | 7 | | 49 | 13 |
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| 38.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.21568627450980393 | | totalSentences | 102 | | uniqueOpeners | 22 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 92 | | matches | | 0 | "Then she heard it—a low," | | 1 | "Then she saw it—a set" | | 2 | "Then she heard it—a low," | | 3 | "Then she saw it—a door." |
| | ratio | 0.043 | |
| 93.91% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 92 | | matches | | 0 | "She had been chasing Tomás" | | 1 | "Her watch—worn leather, the kind" | | 2 | "He was a man who" | | 3 | "She exhaled sharply, her fingers" | | 4 | "She should have known." | | 5 | "Her breath hitched." | | 6 | "She could go back." | | 7 | "She could press charges, get" | | 8 | "She wiped it away, her" | | 9 | "She didn’t like the way" | | 10 | "She took a step forward," | | 11 | "She could turn back." | | 12 | "She could call for backup." | | 13 | "Her flashlight flickered, casting long," | | 14 | "It was a chain." | | 15 | "She had to be careful." | | 16 | "It was a world." | | 17 | "She pressed on, her flashlight" | | 18 | "It was made of blackened" | | 19 | "She had no key." |
| | ratio | 0.315 | |
| 90.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 92 | | matches | | 0 | "The neon glow of *The" | | 1 | "Detective Harlow Quinn’s breath came" | | 2 | "The rain lashed her face," | | 3 | "She had been chasing Tomás" | | 4 | "The paramedic’s footsteps had been" | | 5 | "Quinn’s pulse hammered in her" | | 6 | "The word slithered through her" | | 7 | "Her watch—worn leather, the kind" | | 8 | "*Twenty-three forty-seven.* She needed to" | | 9 | "A flicker of movement caught" | | 10 | "A figure stood at the" | | 11 | "Quinn’s instincts screamed at her" | | 12 | "Tomás wasn’t just some random" | | 13 | "He was a man who" | | 14 | "She exhaled sharply, her fingers" | | 15 | "The rain had turned the" | | 16 | "Every shadow could be a" | | 17 | "Every sound could be a" | | 18 | "Quinn’s blood turned to ice." | | 19 | "The sound came from the" |
| | ratio | 0.739 | |
| 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 | 39 | | technicalSentenceCount | 2 | | matches | | 0 | "The paramedic’s footsteps had been too light, too sure, as if he knew exactly where he was going." | | 1 | "Three years ago, she’d lost her partner, DS Morris, in a case that hadn’t been solved." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |