| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1036 | | 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) | |
| 66.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1036 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "shattered" | | 1 | "footsteps" | | 2 | "charged" | | 3 | "warmth" | | 4 | "weight" |
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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 | 114 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 114 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 114 | | 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 | 0 | | markdownWords | 0 | | totalWords | 1036 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 72.78% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 1036 | | uniqueNames | 15 | | maxNameDensity | 1.54 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 16 | | Police | 1 | | Raven | 1 | | Nest | 1 | | Herrera | 2 | | Tube | 1 | | Detective | 3 | | Camden | 2 | | Saint | 1 | | Christopher | 1 | | Tomás | 6 | | Rain | 3 | | You | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Herrera" | | 5 | "Detective" | | 6 | "Camden" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "Tomás" | | 10 | "Rain" | | 11 | "You" |
| | places | | | globalScore | 0.728 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1036 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 114 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 30.47 | | std | 20.71 | | cv | 0.68 | | sampleLengths | | 0 | 70 | | 1 | 11 | | 2 | 7 | | 3 | 5 | | 4 | 36 | | 5 | 8 | | 6 | 21 | | 7 | 51 | | 8 | 67 | | 9 | 43 | | 10 | 40 | | 11 | 19 | | 12 | 16 | | 13 | 56 | | 14 | 15 | | 15 | 26 | | 16 | 36 | | 17 | 24 | | 18 | 45 | | 19 | 15 | | 20 | 7 | | 21 | 73 | | 22 | 61 | | 23 | 18 | | 24 | 7 | | 25 | 46 | | 26 | 30 | | 27 | 27 | | 28 | 40 | | 29 | 5 | | 30 | 46 | | 31 | 5 | | 32 | 54 | | 33 | 6 |
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| 92.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 114 | | matches | | 0 | "been bolted" | | 1 | "is wanted" | | 2 | "been announced" | | 3 | "being struck" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 183 | | matches | | 0 | "was already descending" | | 1 | "was pointing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 114 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1039 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.030798845043310877 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006737247353224254 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 114 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 114 | | mean | 9.09 | | std | 5.8 | | cv | 0.639 | | sampleLengths | | 0 | 13 | | 1 | 20 | | 2 | 14 | | 3 | 23 | | 4 | 4 | | 5 | 7 | | 6 | 7 | | 7 | 2 | | 8 | 3 | | 9 | 15 | | 10 | 2 | | 11 | 2 | | 12 | 17 | | 13 | 8 | | 14 | 1 | | 15 | 6 | | 16 | 5 | | 17 | 9 | | 18 | 13 | | 19 | 21 | | 20 | 17 | | 21 | 13 | | 22 | 7 | | 23 | 19 | | 24 | 11 | | 25 | 17 | | 26 | 6 | | 27 | 16 | | 28 | 11 | | 29 | 10 | | 30 | 8 | | 31 | 9 | | 32 | 11 | | 33 | 5 | | 34 | 7 | | 35 | 9 | | 36 | 2 | | 37 | 5 | | 38 | 3 | | 39 | 8 | | 40 | 4 | | 41 | 4 | | 42 | 2 | | 43 | 14 | | 44 | 5 | | 45 | 11 | | 46 | 24 | | 47 | 8 | | 48 | 4 | | 49 | 3 |
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| 55.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.3508771929824561 | | totalSentences | 114 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 103 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 103 | | matches | | 0 | "She kept her arms tight" | | 1 | "He glanced back once." | | 2 | "You are only making this" | | 3 | "His accent clipped every word." | | 4 | "You have no idea what" | | 5 | "Her worn leather watch snagged" | | 6 | "She shook it free and" | | 7 | "His footsteps rang on metal" | | 8 | "She dropped to one knee" | | 9 | "I need cover." | | 10 | "I'll handle the rest." | | 11 | "Her brown eyes tracked the" | | 12 | "Her partner's unsolved death three" | | 13 | "You walk in blind, you" | | 14 | "She could radio for backup." | | 15 | "She could cordon the entrance" | | 16 | "Her fingers brushed the cold" | | 17 | "She took the first step" | | 18 | "Her military bearing kept her" | | 19 | "His warm brown eyes met" |
| | ratio | 0.282 | |
| 27.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 103 | | matches | | 0 | "Rain lashed the Soho streets" | | 1 | "Detective Harlow Quinn pounded after" | | 2 | "The man's coat flared like" | | 3 | "She kept her arms tight" | | 4 | "He glanced back once." | | 5 | "Panic sharpened his features under" | | 6 | "Quinn narrowed the gap by" | | 7 | "The man ignored her and" | | 8 | "Quinn vaulted the spilled mess" | | 9 | "You are only making this" | | 10 | "the man shouted over his" | | 11 | "His accent clipped every word." | | 12 | "You have no idea what" | | 13 | "The green neon of the" | | 14 | "Quinn had first spotted him" | | 15 | "Her worn leather watch snagged" | | 16 | "She shook it free and" | | 17 | "Camden's skyline rose ahead, its" | | 18 | "The suspect veered into a" | | 19 | "His footsteps rang on metal" |
| | ratio | 0.864 | |
| 48.54% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 103 | | matches | | 0 | "Now the chase dragged them" |
| | ratio | 0.01 | |
| 82.07% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn pounded after the fleeing figure, her boots sending up sprays that caught the glow from passing taxis." | | 1 | "She kept her arms tight to her body, elbows locked in the rhythm that had carried her through eighteen years of foot pursuits." | | 2 | "The same name that had appeared in three of her off-the-books files." | | 3 | "The market's strange currents moved around her, voices rising and falling, deals being struck in hurried whispers." |
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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 | |