| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 22 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.12% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 842 | | totalAiIsmAdverbs | 2 | | 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) | |
| 4.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 842 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "glistening" | | 1 | "pounding" | | 2 | "etched" | | 3 | "dance" | | 4 | "scanning" | | 5 | "imposing" | | 6 | "furrowed" | | 7 | "raced" | | 8 | "chill" | | 9 | "stomach" | | 10 | "weight" | | 11 | "gleaming" | | 12 | "racing" | | 13 | "unraveling" | | 14 | "steeled" | | 15 | "resolve" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 77.83% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 3 | | narrationSentences | 53 | | matches | | 0 | "d in frustration" | | 1 | "e in surprise" | | 2 | "e with fear" |
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| 88.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 53 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 67 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 841 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 590 | | uniqueNames | 8 | | maxNameDensity | 3.22 | | worstName | "Harlow" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 19 | | Quinn | 1 | | Herrera | 1 | | Clique | 2 | | Tomás | 6 | | Silas | 4 | | Market | 2 | | Veil | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Tomás" | | 4 | "Silas" | | 5 | "Market" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 96.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a lead anchor, dragging her d" |
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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 | 841 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 67 | | matches | (empty) | |
| 65.50% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 29 | | std | 11 | | cv | 0.379 | | sampleLengths | | 0 | 32 | | 1 | 48 | | 2 | 42 | | 3 | 45 | | 4 | 37 | | 5 | 24 | | 6 | 30 | | 7 | 25 | | 8 | 38 | | 9 | 21 | | 10 | 18 | | 11 | 22 | | 12 | 30 | | 13 | 14 | | 14 | 6 | | 15 | 45 | | 16 | 9 | | 17 | 25 | | 18 | 26 | | 19 | 27 | | 20 | 30 | | 21 | 14 | | 22 | 28 | | 23 | 38 | | 24 | 34 | | 25 | 22 | | 26 | 47 | | 27 | 40 | | 28 | 24 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 53 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 104 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 67 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 591 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.025380710659898477 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.01015228426395939 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 67 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 67 | | mean | 12.55 | | std | 5.91 | | cv | 0.471 | | sampleLengths | | 0 | 14 | | 1 | 18 | | 2 | 14 | | 3 | 19 | | 4 | 15 | | 5 | 11 | | 6 | 13 | | 7 | 18 | | 8 | 18 | | 9 | 7 | | 10 | 9 | | 11 | 11 | | 12 | 10 | | 13 | 16 | | 14 | 11 | | 15 | 14 | | 16 | 10 | | 17 | 9 | | 18 | 12 | | 19 | 9 | | 20 | 25 | | 21 | 14 | | 22 | 14 | | 23 | 10 | | 24 | 12 | | 25 | 9 | | 26 | 8 | | 27 | 10 | | 28 | 13 | | 29 | 9 | | 30 | 11 | | 31 | 8 | | 32 | 11 | | 33 | 10 | | 34 | 4 | | 35 | 3 | | 36 | 3 | | 37 | 15 | | 38 | 16 | | 39 | 14 | | 40 | 3 | | 41 | 6 | | 42 | 5 | | 43 | 20 | | 44 | 6 | | 45 | 20 | | 46 | 2 | | 47 | 25 | | 48 | 13 | | 49 | 17 |
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| 75.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.4626865671641791 | | totalSentences | 67 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 51 | | matches | | 0 | "She knew he had information" | | 1 | "He ducked down a set" | | 2 | "She gripped the handle of" | | 3 | "She peered around the corner" | | 4 | "She surged forward, pushing past" | | 5 | "She yanked the curtain aside" | | 6 | "she growled, leveling her weapon" | | 7 | "He swept his arm out," | | 8 | "she demanded, though a part" | | 9 | "She couldn't just walk away," | | 10 | "He leaned in, his eyes" | | 11 | "She knew she was in" | | 12 | "She had to know the" |
| | ratio | 0.255 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 51 | | matches | | 0 | "Detective Harlow Quinn's boots splashed" | | 1 | "Tomás Herrera darted ahead, his" | | 2 | "Harlow shouted, her voice barely" | | 3 | "She knew he had information" | | 4 | "Tomás glanced over his shoulder," | | 5 | "He ducked down a set" | | 6 | "Harlow growled in frustration but" | | 7 | "The tunnel was dank and" | | 8 | "Harlow slowed her pace, her" | | 9 | "The shadows seemed to shift" | | 10 | "She gripped the handle of" | | 11 | "Harlow approached it cautiously, the" | | 12 | "She peered around the corner" | | 13 | "An expansive underground space stretched" | | 14 | "Dozens of people milled about," | | 15 | "Harlow stepped inside, her sharp" | | 16 | "There, near the back, she" | | 17 | "She surged forward, pushing past" | | 18 | "a burly man in a" | | 19 | "She yanked the curtain aside" |
| | ratio | 0.922 | |
| 98.04% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 51 | | matches | | 0 | "If she could just catch" |
| | ratio | 0.02 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 3 | | matches | | 0 | "Harlow shouted, her voice barely audible over the pounding of the downpour" | | 1 | "the man rumbled, his voice rich and sonorous" | | 2 | "He leaned in, his eyes boring into hers" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 4 | | fancyTags | | 0 | "Harlow shouted (shout)" | | 1 | "she growled (growl)" | | 2 | "Harlow snapped (snap)" | | 3 | "she demanded (demand)" |
| | dialogueSentences | 22 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 1 | | effectiveRatio | 0.364 | |