| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 896 | | 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) | |
| 60.94% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 896 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "chill" | | 1 | "pulsed" | | 2 | "scanned" | | 3 | "sturdy" | | 4 | "navigate" | | 5 | "depths" | | 6 | "jaw clenched" |
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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 | 2 | | narrationSentences | 75 | | matches | | 0 | "e in frustration" | | 1 | "d in determination" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 75 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 76 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 16 | | totalWords | 899 | | ratio | 0.018 | | matches | | 0 | "Weary days," | | 1 | "levant" | | 2 | "nose _- Tweek, the white. Another Ah surname, the thrust of the holy," |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 43.74% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 894 | | uniqueNames | 17 | | maxNameDensity | 2.13 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Detective" | | discoveredNames | | Detective | 18 | | Harlow | 2 | | Quinn | 19 | | Soho | 2 | | Újj | 1 | | January | 1 | | Raven | 1 | | Nest | 1 | | Morris | 1 | | Evaluating | 1 | | Tweek | 1 | | Ah | 1 | | Lightning | 1 | | Tomás | 1 | | Herrera | 1 | | Britishistani | 1 | | London | 1 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "January" | | 4 | "Raven" | | 5 | "Nest" | | 6 | "Morris" | | 7 | "Tomás" | | 8 | "Herrera" | | 9 | "Britishistani" |
| | places | | | globalScore | 0.437 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 88.77% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.112 | | wordCount | 899 | | matches | | 0 | "not a lock but a drawer" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 76 | | matches | (empty) | |
| 19.84% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 56.19 | | std | 12.33 | | cv | 0.219 | | sampleLengths | | 0 | 58 | | 1 | 39 | | 2 | 45 | | 3 | 58 | | 4 | 47 | | 5 | 41 | | 6 | 82 | | 7 | 49 | | 8 | 79 | | 9 | 51 | | 10 | 63 | | 11 | 47 | | 12 | 53 | | 13 | 57 | | 14 | 73 | | 15 | 57 |
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| 95.91% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 75 | | matches | | 0 | "was hidden" | | 1 | "was hidden" |
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| 48.48% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 132 | | matches | | 0 | "were, learning" | | 1 | "was happening" | | 2 | "was proving" |
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| 67.67% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 76 | | ratio | 0.026 | | matches | | 0 | "Nocturnal music pulsed from the bars they passed - jazz, electronica, bluegrass, a catchall soundtrack for the quarter's vibrant streetlife." | | 1 | "The clique's nocturnal agriculture meant that testosterone-addled _levant_ - serum-enhanced males - prowled these streets." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 697 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.011477761836441894 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.00430416068866571 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 76 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 76 | | mean | 11.83 | | std | 7.71 | | cv | 0.652 | | sampleLengths | | 0 | 29 | | 1 | 13 | | 2 | 16 | | 3 | 1 | | 4 | 11 | | 5 | 7 | | 6 | 20 | | 7 | 15 | | 8 | 18 | | 9 | 10 | | 10 | 2 | | 11 | 10 | | 12 | 15 | | 13 | 16 | | 14 | 2 | | 15 | 10 | | 16 | 5 | | 17 | 11 | | 18 | 2 | | 19 | 2 | | 20 | 1 | | 21 | 1 | | 22 | 14 | | 23 | 8 | | 24 | 8 | | 25 | 5 | | 26 | 4 | | 27 | 22 | | 28 | 10 | | 29 | 9 | | 30 | 29 | | 31 | 14 | | 32 | 30 | | 33 | 9 | | 34 | 16 | | 35 | 19 | | 36 | 5 | | 37 | 22 | | 38 | 24 | | 39 | 10 | | 40 | 11 | | 41 | 12 | | 42 | 25 | | 43 | 26 | | 44 | 17 | | 45 | 16 | | 46 | 12 | | 47 | 4 | | 48 | 1 | | 49 | 13 |
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| 61.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.40789473684210525 | | totalSentences | 76 | | uniqueOpeners | 31 | |
| 99.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 67 | | matches | | 0 | "Just the untouched smell of" | | 1 | "Again, her quarry had gotten" |
| | ratio | 0.03 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 67 | | matches | | 0 | "Her breath came in short" | | 1 | "It had tempered the glow" | | 2 | "She had to pin the" | | 3 | "She hit the mysteries in" | | 4 | "She straightened, adjusted her respirator." | | 5 | "She needed to round the" | | 6 | "She widened the gap by" | | 7 | "They got wide enough when" | | 8 | "He slid it right out." | | 9 | "Her fingers weren't far in" | | 10 | "She could smell moldy wood" | | 11 | "She couldn't smell anything new" | | 12 | "She pulled from her pockets" | | 13 | "She kicked open the door," | | 14 | "She closed her eyes." | | 15 | "She couldn't do this alone." | | 16 | "She needed someone who knew" | | 17 | "She stowed away her tools" | | 18 | "She needed to find Tomás" | | 19 | "She would find what she" |
| | ratio | 0.299 | |
| 57.01% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 67 | | matches | | 0 | "The rain plastered Detective Harlow" | | 1 | "Her breath came in short" | | 2 | "A dozen yards ahead, her" | | 3 | "Detective Quinn shoved off the" | | 4 | "Rain sluiced off the triangular" | | 5 | "The January rain had done" | | 6 | "It had tempered the glow" | | 7 | "_Weary days,_ she thought, forcing" | | 8 | "The alleys twisted and turned," | | 9 | "The clique's nocturnal agriculture meant" | | 10 | "She had to pin the" | | 11 | "Detective Quinn swerved around a" | | 12 | "She hit the mysteries in" | | 13 | "This would be harder than" | | 14 | "She straightened, adjusted her respirator." | | 15 | "Detective Quinn scanned for threats," | | 16 | "Another Ah surname, the thrust" | | 17 | "She needed to round the" | | 18 | "Detective Quinn searched her pockets" | | 19 | "The first key was hidden" |
| | ratio | 0.806 | |
| 74.63% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 67 | | matches | | 0 | "If anyone could understand the" |
| | ratio | 0.015 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 3 | | matches | | 0 | "She could smell moldy wood and the stale cigarette smoke of the patrons who had sneaked out the back entrance for quick drags over many years." | | 1 | "Detective Quinn crept down the alley, listening for noises that would give away what was happening inside." | | 2 | "She needed someone who knew the hard realities of the clique, who could navigate the complexities of their hidden-world." |
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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 | |