| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn turned back [back]" |
| | dialogueSentences | 49 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.071 | | effectiveRatio | 0.041 | |
| 90.29% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1545 | | totalAiIsmAdverbs | 3 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | |
| | highlights | | 0 | "carefully" | | 1 | "deliberately" | | 2 | "very" |
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| 60.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 83.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1545 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "scanned" | | 1 | "traced" | | 2 | "etched" | | 3 | "silence" |
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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 | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 93 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 93 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 128 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 60 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1539 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 6.40% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 766 | | uniqueNames | 7 | | maxNameDensity | 2.87 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 22 | | Northern | 1 | | Line | 1 | | Chen | 10 | | Kowalski | 1 | | Eva | 15 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Chen" | | 3 | "Kowalski" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 0.064 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 1 | | matches | | 0 | "something between wariness and curiosity" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.65 | | wordCount | 1539 | | matches | | 0 | "not toward the tunnel or the body, but directly at Eva" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 128 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 25.65 | | std | 17.12 | | cv | 0.667 | | sampleLengths | | 0 | 31 | | 1 | 55 | | 2 | 9 | | 3 | 29 | | 4 | 3 | | 5 | 42 | | 6 | 14 | | 7 | 38 | | 8 | 33 | | 9 | 30 | | 10 | 3 | | 11 | 36 | | 12 | 41 | | 13 | 31 | | 14 | 11 | | 15 | 5 | | 16 | 36 | | 17 | 25 | | 18 | 53 | | 19 | 10 | | 20 | 12 | | 21 | 9 | | 22 | 14 | | 23 | 7 | | 24 | 6 | | 25 | 8 | | 26 | 34 | | 27 | 27 | | 28 | 17 | | 29 | 29 | | 30 | 45 | | 31 | 13 | | 32 | 46 | | 33 | 42 | | 34 | 20 | | 35 | 44 | | 36 | 41 | | 37 | 6 | | 38 | 6 | | 39 | 19 | | 40 | 47 | | 41 | 4 | | 42 | 6 | | 43 | 66 | | 44 | 7 | | 45 | 64 | | 46 | 11 | | 47 | 44 | | 48 | 57 | | 49 | 42 |
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| 90.17% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 93 | | matches | | 0 | "was cracked" | | 1 | "were pitted" | | 2 | "was disturbed" | | 3 | "been rearranged" |
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| 49.62% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 133 | | matches | | 0 | "was coming" | | 1 | "was still spinning" | | 2 | "was looking" |
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| 98.21% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 128 | | ratio | 0.016 | | matches | | 0 | "Something metallic that Harlow Quinn had learned to recognize over eighteen years—blood, but old." | | 1 | "The soles were nearly clean—no gravel, no tube station grime." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 771 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.0311284046692607 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.009079118028534372 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 128 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 128 | | mean | 12.02 | | std | 11.24 | | cv | 0.935 | | sampleLengths | | 0 | 11 | | 1 | 14 | | 2 | 6 | | 3 | 25 | | 4 | 30 | | 5 | 2 | | 6 | 1 | | 7 | 3 | | 8 | 3 | | 9 | 16 | | 10 | 9 | | 11 | 4 | | 12 | 3 | | 13 | 6 | | 14 | 36 | | 15 | 14 | | 16 | 7 | | 17 | 31 | | 18 | 5 | | 19 | 16 | | 20 | 7 | | 21 | 5 | | 22 | 8 | | 23 | 10 | | 24 | 5 | | 25 | 7 | | 26 | 3 | | 27 | 14 | | 28 | 22 | | 29 | 6 | | 30 | 11 | | 31 | 5 | | 32 | 19 | | 33 | 7 | | 34 | 8 | | 35 | 13 | | 36 | 3 | | 37 | 6 | | 38 | 5 | | 39 | 5 | | 40 | 19 | | 41 | 17 | | 42 | 13 | | 43 | 12 | | 44 | 4 | | 45 | 2 | | 46 | 36 | | 47 | 11 | | 48 | 10 | | 49 | 3 |
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| 60.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3828125 | | totalSentences | 128 | | uniqueOpeners | 49 | |
| 41.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 80 | | matches | | 0 | "Definitely not underground grit." |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 80 | | matches | | 0 | "She ducked under the police" | | 1 | "She had the floor." | | 2 | "She shifted her gaze to" | | 3 | "She traced the pattern with" | | 4 | "She clutched her worn leather" | | 5 | "He held up an evidence" | | 6 | "She ignored it." | | 7 | "She pointed to the floor," | | 8 | "She tucked a strand of" | | 9 | "She stopped three feet from" | | 10 | "She pulled a pair of" | | 11 | "She held the fragment up" | | 12 | "She reached into her satchel" | | 13 | "Her eyes stayed fixed on" |
| | ratio | 0.175 | |
| 41.25% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 80 | | matches | | 0 | "The tunnel stank of damp" | | 1 | "Something metallic that Harlow Quinn" | | 2 | "Hours old at least, not" | | 3 | "She ducked under the police" | | 4 | "The halogen lamps the scene" | | 5 | "Quinn pulled on her nitrile" | | 6 | "The photographer was already done," | | 7 | "She had the floor." | | 8 | "DS Chen answered from behind" | | 9 | "Quinn didn't phrase it as" | | 10 | "Chen stepped closer, his notepad" | | 11 | "Quinn looked at the watch." | | 12 | "The face was cracked, the" | | 13 | "The discrepancy itched at her." | | 14 | "She shifted her gaze to" | | 15 | "A man who hadn't fought." | | 16 | "Quinn stood and scanned the" | | 17 | "The tiles were pitted with" | | 18 | "Graffiti covered the advertising boards." | | 19 | "She traced the pattern with" |
| | ratio | 0.838 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 1 | | matches | | 0 | "Quinn looked down at the body, at the cracked watch stopped at an impossible time, at the seaweed and the dirt and the careful arrangement of a man who had been…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.061 | | leniency | 0.122 | | rawRatio | 0 | | effectiveRatio | 0 | |