| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.51% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1113 | | 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) | |
| 37.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1113 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "familiar" | | 1 | "weight" | | 2 | "gloom" | | 3 | "footsteps" | | 4 | "echoed" | | 5 | "measured" | | 6 | "velvet" | | 7 | "tapestry" | | 8 | "glinting" | | 9 | "sense of" | | 10 | "predator" | | 11 | "scanned" | | 12 | "tangible" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 92 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 92 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 4 | | totalWords | 1102 | | ratio | 0.004 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1102 | | uniqueNames | 14 | | maxNameDensity | 0.54 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 6 | | Raven | 1 | | Morris | 5 | | Glock | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Camden | 1 | | Herrera | 6 | | Saint | 1 | | Christopher | 1 | | Tomás | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" |
| | places | | | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 1 | | matches | | 0 | "knives that seemed to drink the light" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.907 | | wordCount | 1102 | | matches | | 0 | "not as he was in life, laughing at his own bad jokes, but as he was in her last memory of him: pale, terrified, whispe" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 92 | | matches | | |
| 80.16% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 47.91 | | std | 20.63 | | cv | 0.431 | | sampleLengths | | 0 | 71 | | 1 | 81 | | 2 | 69 | | 3 | 11 | | 4 | 70 | | 5 | 48 | | 6 | 51 | | 7 | 31 | | 8 | 54 | | 9 | 9 | | 10 | 67 | | 11 | 69 | | 12 | 52 | | 13 | 33 | | 14 | 58 | | 15 | 75 | | 16 | 22 | | 17 | 52 | | 18 | 36 | | 19 | 54 | | 20 | 42 | | 21 | 19 | | 22 | 28 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 92 | | matches | | |
| 88.89% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 180 | | matches | | 0 | "was running" | | 1 | "was listening" | | 2 | "was heading" |
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| 18.63% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 92 | | ratio | 0.043 | | matches | | 0 | "He wasn’t just running; he was running *to* something." | | 1 | "There was a soft, grating sound, and a section of the wall—a cleverly disguised door—swung inward." | | 2 | "A wave of damp, cold air, smelling of old stone and something else—ozone and dried herbs—washed over her." | | 3 | "Muffled sounds came from beyond it—a low hum of conversation, the clink of glass, a strange, melodic chiming that set her teeth on edge." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1113 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.0215633423180593 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.0071877807726864335 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 11.98 | | std | 7.08 | | cv | 0.591 | | sampleLengths | | 0 | 15 | | 1 | 15 | | 2 | 16 | | 3 | 25 | | 4 | 20 | | 5 | 20 | | 6 | 7 | | 7 | 14 | | 8 | 20 | | 9 | 16 | | 10 | 8 | | 11 | 10 | | 12 | 10 | | 13 | 25 | | 14 | 9 | | 15 | 2 | | 16 | 24 | | 17 | 4 | | 18 | 14 | | 19 | 3 | | 20 | 16 | | 21 | 9 | | 22 | 8 | | 23 | 17 | | 24 | 16 | | 25 | 7 | | 26 | 3 | | 27 | 17 | | 28 | 18 | | 29 | 13 | | 30 | 16 | | 31 | 2 | | 32 | 9 | | 33 | 4 | | 34 | 7 | | 35 | 11 | | 36 | 12 | | 37 | 24 | | 38 | 9 | | 39 | 14 | | 40 | 20 | | 41 | 17 | | 42 | 6 | | 43 | 7 | | 44 | 3 | | 45 | 13 | | 46 | 7 | | 47 | 15 | | 48 | 20 | | 49 | 14 |
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| 37.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.30434782608695654 | | totalSentences | 92 | | uniqueOpeners | 28 | |
| 76.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 87 | | matches | | 0 | "Instead, he pressed himself into" | | 1 | "Instead, he held up a" |
| | ratio | 0.023 | |
| 59.08% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 87 | | matches | | 0 | "Her worn leather watch was" | | 1 | "She kept her eyes locked" | | 2 | "Her suspect, a wiry man" | | 3 | "He’d bolted the moment he’d" | | 4 | "He was fast, but she" | | 5 | "She pushed past a couple" | | 6 | "She gained ground, her longer" | | 7 | "She could hear the ragged" | | 8 | "He wasn’t just running; he" | | 9 | "He skidded to a halt" | | 10 | "Her gut tightened." | | 11 | "Her last partner, Morris, had" | | 12 | "He slipped through, into a" | | 13 | "She crossed the lane in" | | 14 | "She drew her service weapon," | | 15 | "He was still running." | | 16 | "She started down, each step" | | 17 | "She pushed the curtain aside" | | 18 | "It was an abandoned Tube" | | 19 | "Her suspect was already halfway" |
| | ratio | 0.402 | |
| 34.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 87 | | matches | | 0 | "The rain fell in silver" | | 1 | "Detective Harlow Quinn’s breath came" | | 2 | "Her worn leather watch was" | | 3 | "She kept her eyes locked" | | 4 | "Her suspect, a wiry man" | | 5 | "He’d bolted the moment he’d" | | 6 | "He was fast, but she" | | 7 | "She pushed past a couple" | | 8 | "The suspect darted down a" | | 9 | "Quinn followed, her shoes splashing" | | 10 | "The alley opened onto a" | | 11 | "She gained ground, her longer" | | 12 | "She could hear the ragged" | | 13 | "He wasn’t just running; he" | | 14 | "He skidded to a halt" | | 15 | "Quinn knew the place." | | 16 | "A dive bar, the kind" | | 17 | "Her gut tightened." | | 18 | "Her last partner, Morris, had" | | 19 | "The memory was a cold" |
| | ratio | 0.851 | |
| 57.47% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 87 | | matches | | | ratio | 0.011 | |
| 75.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 5 | | matches | | 0 | "He skidded to a halt before a recessed doorway, beneath a distinctive green neon sign that buzzed faintly in the rain: *The Raven’s Nest*." | | 1 | "A dive bar, the kind that attracted a certain clientele who preferred the shadows." | | 2 | "The space beyond was vast, a cavernous hollow that felt both ancient and alive." | | 3 | "The suspect snatched it and turned, his eyes darting around the market before he hurried toward a darker tunnel leading even deeper into the station’s bowels." | | 4 | "She saw Morris’s face, not as he was in life, laughing at his own bad jokes, but as he was in her last memory of him: pale, terrified, whispering about doors th…" |
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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 | |