| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.85% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 813 | | 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) | |
| 32.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 813 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "stark" | | 1 | "echoing" | | 2 | "scanned" | | 3 | "flicked" | | 4 | "glinting" | | 5 | "racing" | | 6 | "echoed" | | 7 | "reminder" | | 8 | "whisper" | | 9 | "weight" | | 10 | "flicker" |
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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 | | |
| 85.32% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 3 | | narrationSentences | 63 | | matches | | 0 | "was desperate" | | 1 | "e with fear" | | 2 | "a flicker of surprise" |
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| 97.51% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 63 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 812 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 665 | | uniqueNames | 9 | | maxNameDensity | 2.56 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 17 | | Tomás | 1 | | Herrera | 12 | | Veil | 2 | | Market | 2 | | Raven | 2 | | Nest | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Market" | | 5 | "Raven" |
| | places | | | globalScore | 0.222 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 76.85% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.232 | | wordCount | 812 | | matches | | 0 | "not to grab him, but to shove him hard against the wall" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 33.83 | | std | 20.1 | | cv | 0.594 | | sampleLengths | | 0 | 84 | | 1 | 61 | | 2 | 57 | | 3 | 68 | | 4 | 49 | | 5 | 16 | | 6 | 37 | | 7 | 19 | | 8 | 28 | | 9 | 49 | | 10 | 17 | | 11 | 17 | | 12 | 16 | | 13 | 24 | | 14 | 20 | | 15 | 23 | | 16 | 16 | | 17 | 53 | | 18 | 14 | | 19 | 55 | | 20 | 31 | | 21 | 13 | | 22 | 13 | | 23 | 32 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 63 | | matches | (empty) | |
| 83.04% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 114 | | matches | | 0 | "was trying" | | 1 | "was dealing" |
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| 69.60% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 78 | | ratio | 0.026 | | matches | | 0 | "She’d seen his kind before – the ones who thought they could hide in the city’s underbelly." | | 1 | "Then, she saw it – a slight movement behind a stack of discarded crates near the wall’s base." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 666 | | adjectiveStacks | 1 | | stackExamples | | 0 | "narrow, rain-slicked street," |
| | adverbCount | 16 | | adverbRatio | 0.024024024024024024 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.015015015015015015 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 10.41 | | std | 6.12 | | cv | 0.588 | | sampleLengths | | 0 | 13 | | 1 | 26 | | 2 | 18 | | 3 | 27 | | 4 | 13 | | 5 | 17 | | 6 | 19 | | 7 | 4 | | 8 | 4 | | 9 | 4 | | 10 | 3 | | 11 | 24 | | 12 | 14 | | 13 | 6 | | 14 | 10 | | 15 | 13 | | 16 | 4 | | 17 | 20 | | 18 | 7 | | 19 | 18 | | 20 | 6 | | 21 | 14 | | 22 | 21 | | 23 | 14 | | 24 | 13 | | 25 | 3 | | 26 | 9 | | 27 | 12 | | 28 | 6 | | 29 | 10 | | 30 | 12 | | 31 | 7 | | 32 | 8 | | 33 | 20 | | 34 | 20 | | 35 | 14 | | 36 | 7 | | 37 | 8 | | 38 | 10 | | 39 | 7 | | 40 | 10 | | 41 | 7 | | 42 | 12 | | 43 | 4 | | 44 | 10 | | 45 | 14 | | 46 | 10 | | 47 | 10 | | 48 | 10 | | 49 | 5 |
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| 50.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3333333333333333 | | totalSentences | 78 | | uniqueOpeners | 26 | |
| 53.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 62 | | matches | | | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 62 | | matches | | 0 | "Her worn leather watch ticked" | | 1 | "She’d seen his kind before" | | 2 | "He was desperate, reckless." | | 3 | "She didn’t hesitate." | | 4 | "She didn’t slow, didn’t look" | | 5 | "She scanned the shadows, her" | | 6 | "She reached the crates just" | | 7 | "He scrambled, scrambling towards the" | | 8 | "He looked up at her," | | 9 | "he gasped, his voice strained" | | 10 | "He scrambled to his feet," | | 11 | "He hesitated, then pointed a" | | 12 | "She didn’t have one." | | 13 | "Her partner’s death echoed in" | | 14 | "She couldn’t let her guard" | | 15 | "She didn’t have one." | | 16 | "she said, her voice firm," | | 17 | "She turned her back on" |
| | ratio | 0.29 | |
| 16.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 62 | | matches | | 0 | "Rain lashed the pavement, a" | | 1 | "Detective Harlow Quinn’s breath misted" | | 2 | "Her worn leather watch ticked" | | 3 | "The suspect, Tomás Herrera, a" | | 4 | "Quinn’s sharp jawline set, her" | | 5 | "She’d seen his kind before" | | 6 | "The bone token requirement for" | | 7 | "Herrera didn’t have one." | | 8 | "He was desperate, reckless." | | 9 | "That was his weakness." | | 10 | "She didn’t hesitate." | | 11 | "The alley opened into a" | | 12 | "Quinn’s boots splashed through a" | | 13 | "She didn’t slow, didn’t look" | | 14 | "The chase was her focus," | | 15 | "The street ended abruptly at" | | 16 | "Herrera had vanished again." | | 17 | "Quinn pressed her back against" | | 18 | "She scanned the shadows, her" | | 19 | "Herrera was trying to climb" |
| | ratio | 0.887 | |
| 80.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 62 | | matches | | 0 | "Before he could finish, Quinn’s" |
| | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 4 | | matches | | 0 | "Quinn barked, her voice cutting through the rain’s roar" | | 1 | "he gasped, his voice strained" | | 2 | "Quinn cut, her gaze piercing" | | 3 | "Quinn finished, her voice cold" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 4 | | fancyTags | | 0 | "Quinn barked (bark)" | | 1 | "he gasped (gasp)" | | 2 | "she murmured (murmur)" | | 3 | "Quinn confirmed (confirm)" |
| | dialogueSentences | 24 | | tagDensity | 0.208 | | leniency | 0.417 | | rawRatio | 0.8 | | effectiveRatio | 0.333 | |