| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1093 | | 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) | |
| 3.93% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1093 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "calculated" | | 1 | "etched" | | 2 | "echoed" | | 3 | "oppressive" | | 4 | "silence" | | 5 | "gloom" | | 6 | "scanned" | | 7 | "shattered" | | 8 | "predictable" | | 9 | "weight" | | 10 | "synchronized" | | 11 | "chill" | | 12 | "cacophony" | | 13 | "velvet" | | 14 | "silk" | | 15 | "pulsed" | | 16 | "rhythmic" | | 17 | "scanning" | | 18 | "stark" | | 19 | "chaotic" |
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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 | 80 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 80 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1086 | | ratio | 0 | | matches | (empty) | |
| 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 | 22 | | wordCount | 1004 | | uniqueNames | 9 | | maxNameDensity | 1.2 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Camden | 2 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Morris | 2 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Morris" |
| | places | | | globalScore | 0.902 | | windowScore | 0.833 | |
| 41.30% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 3 | | matches | | 0 | "symbols that seemed to writhe under the dim streetlamp" | | 1 | "smelled like a lightning strike" | | 2 | "felt like cold stone" |
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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 | 1086 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 24.68 | | std | 15.68 | | cv | 0.635 | | sampleLengths | | 0 | 47 | | 1 | 56 | | 2 | 18 | | 3 | 50 | | 4 | 22 | | 5 | 9 | | 6 | 27 | | 7 | 7 | | 8 | 41 | | 9 | 12 | | 10 | 23 | | 11 | 42 | | 12 | 39 | | 13 | 40 | | 14 | 36 | | 15 | 10 | | 16 | 11 | | 17 | 27 | | 18 | 51 | | 19 | 37 | | 20 | 53 | | 21 | 13 | | 22 | 36 | | 23 | 32 | | 24 | 8 | | 25 | 4 | | 26 | 7 | | 27 | 20 | | 28 | 29 | | 29 | 43 | | 30 | 40 | | 31 | 13 | | 32 | 22 | | 33 | 3 | | 34 | 19 | | 35 | 4 | | 36 | 8 | | 37 | 13 | | 38 | 17 | | 39 | 5 | | 40 | 7 | | 41 | 14 | | 42 | 45 | | 43 | 26 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 80 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 162 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 1 | | flaggedSentences | 6 | | totalSentences | 90 | | ratio | 0.067 | | matches | | 0 | "A gust of stale, subterranean air hit her—cold, damp, and smelling of ancient dust." | | 1 | "The sounds of the surface—the rain, the distant siren, the hum of traffic—faded into a heavy, oppressive silence." | | 2 | "She had heard the whispers in the precinct—the ghost stories told by officers who had seen things that defied the penal code." | | 3 | "It wasn't a token—not a sanctioned one—but it was the only key she possessed." | | 4 | "The wood didn't creak; it sighed." | | 5 | "Figures moved through the crowd—some human, some draped in heavy robes to hide shapes that didn't fit the human mold." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1016 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.02066929133858268 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003937007874015748 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 12.07 | | std | 6.2 | | cv | 0.514 | | sampleLengths | | 0 | 14 | | 1 | 14 | | 2 | 19 | | 3 | 9 | | 4 | 19 | | 5 | 17 | | 6 | 11 | | 7 | 4 | | 8 | 14 | | 9 | 18 | | 10 | 8 | | 11 | 24 | | 12 | 12 | | 13 | 10 | | 14 | 9 | | 15 | 5 | | 16 | 5 | | 17 | 17 | | 18 | 7 | | 19 | 7 | | 20 | 11 | | 21 | 7 | | 22 | 16 | | 23 | 12 | | 24 | 23 | | 25 | 7 | | 26 | 6 | | 27 | 15 | | 28 | 14 | | 29 | 10 | | 30 | 11 | | 31 | 18 | | 32 | 17 | | 33 | 11 | | 34 | 12 | | 35 | 4 | | 36 | 16 | | 37 | 16 | | 38 | 10 | | 39 | 11 | | 40 | 7 | | 41 | 3 | | 42 | 17 | | 43 | 7 | | 44 | 8 | | 45 | 22 | | 46 | 14 | | 47 | 5 | | 48 | 32 | | 49 | 9 |
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| 33.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3 | | totalSentences | 90 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 75.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 80 | | matches | | 0 | "She maintained a predatory clip," | | 1 | "She glanced at the worn" | | 2 | "She caught the scent of" | | 3 | "He spun around, his face" | | 4 | "She didn't raise her weapon." | | 5 | "She squared her shoulders, the" | | 6 | "He reached into his pocket" | | 7 | "He held it up between" | | 8 | "He stepped back and dropped" | | 9 | "She gripped the handle and" | | 10 | "She climbed down the ladder," | | 11 | "She drew her flashlight." | | 12 | "They looked out of place" | | 13 | "It didn't budge." | | 14 | "She scanned the floor and" | | 15 | "She stepped back, her sharp" | | 16 | "She had heard the whispers" | | 17 | "She thought of DS Morris." | | 18 | "She remembered the look in" | | 19 | "It wasn't a token—not a" |
| | ratio | 0.363 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 80 | | matches | | 0 | "Rain lashed the pavement, turning" | | 1 | "Detective Harlow Quinn hammered her" | | 2 | "Quinn didn't sprint with the" | | 3 | "She maintained a predatory clip," | | 4 | "A flash of neon blue" | | 5 | "She glanced at the worn" | | 6 | "The streets of Camden slept," | | 7 | "The suspect veered sharply right," | | 8 | "Quinn pivoted, her soles gripping" | | 9 | "She caught the scent of" | | 10 | "The man stopped abruptly at" | | 11 | "He spun around, his face" | | 12 | "Quinn stopped ten feet away." | | 13 | "She didn't raise her weapon." | | 14 | "She squared her shoulders, the" | | 15 | "The man laughed, a wet," | | 16 | "He reached into his pocket" | | 17 | "He held it up between" | | 18 | "A piece of carved bone," | | 19 | "He stepped back and dropped" |
| | ratio | 0.9 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 80 | | matches | | 0 | "To her left, the path" | | 1 | "To her right, the door" | | 2 | "If she entered, she stepped" |
| | ratio | 0.038 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 4 | | matches | | 0 | "The suspect veered sharply right, sliding through a narrow alleyway that smelled of wet cardboard and rotting citrus." | | 1 | "She caught the scent of ozone and sulfur trailing behind the man, a sharp metallic tang that clawed at the back of her throat." | | 2 | "The air was thick with the smell of incense, roasting meats, and something that smelled like a lightning strike." | | 3 | "She lunged forward, shoving through the crowd, her shoulder colliding with a figure that felt like cold stone." |
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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 | |