| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 97.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1775 | | 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) | |
| 52.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1775 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "shattered" | | 1 | "measured" | | 2 | "weight" | | 3 | "velvet" | | 4 | "etched" | | 5 | "trembled" | | 6 | "standard" | | 7 | "traced" | | 8 | "echoed" | | 9 | "pulsed" | | 10 | "flickered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 297 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 2 | | narrationSentences | 297 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 297 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1775 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1775 | | uniqueNames | 11 | | maxNameDensity | 0.79 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 3 | | Quinn | 14 | | Met | 2 | | Detective | 1 | | Kowalski | 1 | | North | 1 | | London | 1 | | Morris | 3 | | Mayfair | 1 | | You | 13 | | Eva | 6 |
| | persons | | 0 | "Quinn" | | 1 | "Met" | | 2 | "Kowalski" | | 3 | "Morris" | | 4 | "You" | | 5 | "Eva" |
| | places | | 0 | "Camden" | | 1 | "North" | | 2 | "London" | | 3 | "Mayfair" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 146 | | 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 | 1775 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 297 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 36.98 | | std | 45.23 | | cv | 1.223 | | sampleLengths | | 0 | 88 | | 1 | 108 | | 2 | 5 | | 3 | 36 | | 4 | 35 | | 5 | 20 | | 6 | 44 | | 7 | 26 | | 8 | 6 | | 9 | 43 | | 10 | 57 | | 11 | 83 | | 12 | 16 | | 13 | 34 | | 14 | 4 | | 15 | 25 | | 16 | 4 | | 17 | 37 | | 18 | 39 | | 19 | 17 | | 20 | 10 | | 21 | 60 | | 22 | 61 | | 23 | 4 | | 24 | 2 | | 25 | 3 | | 26 | 33 | | 27 | 56 | | 28 | 22 | | 29 | 8 | | 30 | 18 | | 31 | 65 | | 32 | 40 | | 33 | 19 | | 34 | 9 | | 35 | 19 | | 36 | 45 | | 37 | 50 | | 38 | 15 | | 39 | 35 | | 40 | 73 | | 41 | 5 | | 42 | 4 | | 43 | 10 | | 44 | 70 | | 45 | 4 | | 46 | 15 | | 47 | 293 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 297 | | matches | | 0 | "got jumped" | | 1 | "get turned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 319 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 297 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1785 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.01680672268907563 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0028011204481792717 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 297 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 297 | | mean | 5.98 | | std | 3.31 | | cv | 0.555 | | sampleLengths | | 0 | 11 | | 1 | 10 | | 2 | 9 | | 3 | 16 | | 4 | 17 | | 5 | 7 | | 6 | 7 | | 7 | 11 | | 8 | 9 | | 9 | 11 | | 10 | 10 | | 11 | 12 | | 12 | 11 | | 13 | 9 | | 14 | 2 | | 15 | 8 | | 16 | 9 | | 17 | 6 | | 18 | 10 | | 19 | 5 | | 20 | 4 | | 21 | 1 | | 22 | 1 | | 23 | 5 | | 24 | 9 | | 25 | 14 | | 26 | 13 | | 27 | 9 | | 28 | 5 | | 29 | 21 | | 30 | 3 | | 31 | 6 | | 32 | 11 | | 33 | 8 | | 34 | 5 | | 35 | 4 | | 36 | 1 | | 37 | 5 | | 38 | 5 | | 39 | 5 | | 40 | 8 | | 41 | 3 | | 42 | 5 | | 43 | 8 | | 44 | 12 | | 45 | 1 | | 46 | 6 | | 47 | 4 | | 48 | 3 | | 49 | 10 |
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| 35.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 44 | | diversityRatio | 0.30067567567567566 | | totalSentences | 296 | | uniqueOpeners | 89 | |
| 39.22% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 255 | | matches | | 0 | "Too light for two men" | | 1 | "Too dense for brass." | | 2 | "Just a controlled extraction." |
| | ratio | 0.012 | |
| 77.25% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 91 | | totalSentences | 255 | | matches | | 0 | "Her boots crunched over shattered" | | 1 | "She kept her hands away" | | 2 | "She crossed the chalk outline" | | 3 | "His fingers curled tight around" | | 4 | "She knelt, the damp seeping" | | 5 | "She brushed back the victim's" | | 6 | "You see a stabbing, Detective." | | 7 | "She adjusted her round glasses" | | 8 | "I see a man who" | | 9 | "She tucked a curl of" | | 10 | "You'll see faint scorch marks." | | 11 | "She shifted her weight to" | | 12 | "Her worn leather watch caught" | | 13 | "You ran a full spectrographic" | | 14 | "I don't run machines." | | 15 | "I read patterns." | | 16 | "She walked the perimeter of" | | 17 | "Her eyes caught the scuff" | | 18 | "She stopped near a rusted" | | 19 | "She pulled on a fresh" |
| | ratio | 0.357 | |
| 28.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 220 | | totalSentences | 255 | | matches | | 0 | "Harlow Quinn stepped through the" | | 1 | "Her boots crunched over shattered" | | 2 | "She kept her hands away" | | 3 | "A uniform shouted from the" | | 4 | "Flashlight beams cut through the" | | 5 | "The air carried the metallic" | | 6 | "She crossed the chalk outline" | | 7 | "The victim lay sprawled near" | | 8 | "The body belonged to a" | | 9 | "A suit jacket hung torn" | | 10 | "His fingers curled tight around" | | 11 | "The uniform near the head" | | 12 | "She knelt, the damp seeping" | | 13 | "She brushed back the victim's" | | 14 | "A jagged laceration split his" | | 15 | "The wound edges showed no" | | 16 | "The skin pulled tight." | | 17 | "You see a stabbing, Detective." | | 18 | "The voice came from behind" | | 19 | "Eva Kowalski ducked through the" |
| | ratio | 0.863 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 255 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 1 | | matches | | 0 | "The same houses that funneled unregistered artefacts into private collections." |
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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 | |