| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.227 | | leniency | 0.455 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 512 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 2.34% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 512 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "vibrated" | | 1 | "pulse" | | 2 | "quickened" | | 3 | "glint" | | 4 | "footsteps" | | 5 | "charged" | | 6 | "jaw clenched" | | 7 | "echoed" |
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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 | 78 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 95 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 15 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 509 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 428 | | uniqueNames | 4 | | maxNameDensity | 3.27 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 14 | | Quinn | 1 | | Herrera | 1 | | Tomás | 7 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Tomás" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 32 | | 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 | 509 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 16.97 | | std | 13.75 | | cv | 0.81 | | sampleLengths | | 0 | 47 | | 1 | 44 | | 2 | 11 | | 3 | 10 | | 4 | 11 | | 5 | 33 | | 6 | 36 | | 7 | 5 | | 8 | 14 | | 9 | 7 | | 10 | 6 | | 11 | 35 | | 12 | 3 | | 13 | 13 | | 14 | 7 | | 15 | 15 | | 16 | 7 | | 17 | 4 | | 18 | 37 | | 19 | 22 | | 20 | 9 | | 21 | 10 | | 22 | 5 | | 23 | 6 | | 24 | 43 | | 25 | 3 | | 26 | 4 | | 27 | 18 | | 28 | 30 | | 29 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 99 | | matches | (empty) | |
| 82.71% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 95 | | ratio | 0.021 | | matches | | 0 | "The suspect—a wiry man with a scar across his forearm—darted between alleyways." | | 1 | "Harlow’s eyes caught a glint of metal—a blade." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 431 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 7 | | adverbRatio | 0.016241299303944315 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.002320185614849188 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 95 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 95 | | mean | 5.36 | | std | 2.8 | | cv | 0.523 | | sampleLengths | | 0 | 10 | | 1 | 7 | | 2 | 11 | | 3 | 7 | | 4 | 12 | | 5 | 15 | | 6 | 7 | | 7 | 8 | | 8 | 7 | | 9 | 7 | | 10 | 11 | | 11 | 4 | | 12 | 6 | | 13 | 3 | | 14 | 5 | | 15 | 3 | | 16 | 4 | | 17 | 6 | | 18 | 9 | | 19 | 6 | | 20 | 3 | | 21 | 5 | | 22 | 7 | | 23 | 4 | | 24 | 2 | | 25 | 13 | | 26 | 7 | | 27 | 3 | | 28 | 5 | | 29 | 3 | | 30 | 11 | | 31 | 4 | | 32 | 3 | | 33 | 4 | | 34 | 2 | | 35 | 5 | | 36 | 9 | | 37 | 5 | | 38 | 8 | | 39 | 8 | | 40 | 3 | | 41 | 2 | | 42 | 5 | | 43 | 6 | | 44 | 6 | | 45 | 1 | | 46 | 7 | | 47 | 8 | | 48 | 4 | | 49 | 3 |
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| 58.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.37894736842105264 | | totalSentences | 95 | | uniqueOpeners | 36 | |
| 48.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 69 | | matches | | 0 | "Somewhere, a clock struck midnight." |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 10 | | totalSentences | 69 | | matches | | 0 | "Her sharp jaw set in" | | 1 | "She paused at a heavy" | | 2 | "she called, voice steady despite" | | 3 | "She glanced back." | | 4 | "Their voices were low, urgent." | | 5 | "They moved deeper into the" | | 6 | "His eyes glowed faintly." | | 7 | "Her hand flew to her" | | 8 | "She ducked, spun, fired." | | 9 | "She glanced back." |
| | ratio | 0.145 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 69 | | matches | | 0 | "Rain drummed against the pavement" | | 1 | "Neon signs bled color across" | | 2 | "Detective Harlow Quinn’s boots splashed" | | 3 | "Her sharp jaw set in" | | 4 | "The suspect—a wiry man with" | | 5 | "Tomás Herrera slipped through a" | | 6 | "The sign buzzed like an" | | 7 | "Harlow followed, her leather watch" | | 8 | "She paused at a heavy" | | 9 | "A faint hum vibrated through" | | 10 | "she called, voice steady despite" | | 11 | "Tomás turned, eyes narrowing." | | 12 | "Harlow stepped closer." | | 13 | "Rainwater dripped from her coat." | | 14 | "The door creaked open." | | 15 | "A narrow staircase descended into" | | 16 | "The air smelled of damp" | | 17 | "Harlow’s hand hovered near her" | | 18 | "She glanced back." | | 19 | "Tomás hesitated, then stepped inside." |
| | ratio | 0.986 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 7 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "she called, voice steady despite the storm" |
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| 13.64% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "she demanded (demand)" | | 1 | "she barked (bark)" | | 2 | "he whispered (whisper)" |
| | dialogueSentences | 22 | | tagDensity | 0.227 | | leniency | 0.455 | | rawRatio | 0.6 | | effectiveRatio | 0.273 | |