| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 126 | | tagDensity | 0.016 | | leniency | 0.032 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2063 | | 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) | |
| 90.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2063 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "pulse" | | 1 | "measured" | | 2 | "traced" |
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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 | 149 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 149 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 273 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2062 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 1 | | matches | | 0 | "When he finished, she asked him to bring his light closer." |
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| 12.96% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 84 | | wordCount | 1277 | | uniqueNames | 7 | | maxNameDensity | 2.74 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 35 | | Camden | 2 | | Sergeant | 1 | | Malik | 20 | | Venn | 8 | | Eva | 17 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Malik" | | 4 | "Venn" | | 5 | "Eva" |
| | places | (empty) | | globalScore | 0.13 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 95 | | 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 | 2062 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 273 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 209 | | mean | 9.87 | | std | 10.27 | | cv | 1.041 | | sampleLengths | | 0 | 41 | | 1 | 10 | | 2 | 4 | | 3 | 3 | | 4 | 16 | | 5 | 2 | | 6 | 15 | | 7 | 7 | | 8 | 4 | | 9 | 9 | | 10 | 52 | | 11 | 13 | | 12 | 26 | | 13 | 14 | | 14 | 2 | | 15 | 10 | | 16 | 9 | | 17 | 7 | | 18 | 3 | | 19 | 5 | | 20 | 41 | | 21 | 5 | | 22 | 51 | | 23 | 13 | | 24 | 3 | | 25 | 15 | | 26 | 3 | | 27 | 5 | | 28 | 5 | | 29 | 12 | | 30 | 12 | | 31 | 4 | | 32 | 32 | | 33 | 4 | | 34 | 46 | | 35 | 17 | | 36 | 3 | | 37 | 4 | | 38 | 3 | | 39 | 1 | | 40 | 26 | | 41 | 13 | | 42 | 5 | | 43 | 3 | | 44 | 2 | | 45 | 1 | | 46 | 2 | | 47 | 36 | | 48 | 20 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 149 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 199 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 273 | | ratio | 0.004 | | matches | | 0 | "The tiled station name had lost enough letters to leave CAM—EN." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1280 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.01796875 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.00078125 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 273 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 273 | | mean | 7.55 | | std | 5.19 | | cv | 0.687 | | sampleLengths | | 0 | 18 | | 1 | 8 | | 2 | 15 | | 3 | 10 | | 4 | 4 | | 5 | 3 | | 6 | 4 | | 7 | 12 | | 8 | 2 | | 9 | 15 | | 10 | 7 | | 11 | 4 | | 12 | 9 | | 13 | 7 | | 14 | 6 | | 15 | 28 | | 16 | 11 | | 17 | 8 | | 18 | 5 | | 19 | 13 | | 20 | 13 | | 21 | 14 | | 22 | 2 | | 23 | 10 | | 24 | 9 | | 25 | 7 | | 26 | 3 | | 27 | 5 | | 28 | 22 | | 29 | 11 | | 30 | 8 | | 31 | 5 | | 32 | 17 | | 33 | 7 | | 34 | 27 | | 35 | 5 | | 36 | 8 | | 37 | 3 | | 38 | 15 | | 39 | 3 | | 40 | 5 | | 41 | 5 | | 42 | 12 | | 43 | 12 | | 44 | 4 | | 45 | 32 | | 46 | 4 | | 47 | 9 | | 48 | 8 | | 49 | 19 |
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| 62.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3882783882783883 | | totalSentences | 273 | | uniqueOpeners | 106 | |
| 23.98% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 139 | | matches | | 0 | "More white grit filled the" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 139 | | matches | | 0 | "His tie hung loose, and" | | 1 | "She held a leather satchel" | | 2 | "His coat had folded under" | | 3 | "His right hand held nothing." | | 4 | "His thumb and forefinger formed" | | 5 | "Its drawers had come out" | | 6 | "She pushed a curl behind" | | 7 | "Her face lost colour as" | | 8 | "Its wooden front had split" | | 9 | "She looked at the drawer" | | 10 | "She pointed without touching." | | 11 | "She called for an evidence" | | 12 | "She crouched until her eyes" | | 13 | "It remained where she put" | | 14 | "She stood and brought the" | | 15 | "She put on fresh gloves" | | 16 | "Her fingertips collected station soot." | | 17 | "She lowered her hand and" | | 18 | "She shifted the light." | | 19 | "He opened his notebook again." |
| | ratio | 0.158 | |
| 42.73% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 116 | | totalSentences | 139 | | matches | | 0 | "Detective Harlow Quinn reached the" | | 1 | "A constable stood beside them," | | 2 | "Quinn opened her hand." | | 3 | "A groove ran through the" | | 4 | "The constable shifted aside." | | 5 | "Quinn pocketed the token and" | | 6 | "Stalls crowded the abandoned station" | | 7 | "Strings of dried plants hung" | | 8 | "The tiled station name had" | | 9 | "None looked at the body." | | 10 | "Detective Sergeant Malik waited beside" | | 11 | "His tie hung loose, and" | | 12 | "Malik pointed at a pair" | | 13 | "Quinn glanced at her worn" | | 14 | "A young woman sat on" | | 15 | "She held a leather satchel" | | 16 | "Quinn crouched beside the body." | | 17 | "Venn lay behind his counter," | | 18 | "His coat had folded under" | | 19 | "A dark patch spread across" |
| | ratio | 0.835 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 139 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 1 | | matches | | 0 | "A dark patch spread across his waistcoat, but the blood on the floor formed a narrow tongue that reached beneath the counter rather than pooling around him." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Malik continued (continue)" |
| | dialogueSentences | 126 | | tagDensity | 0.008 | | leniency | 0.016 | | rawRatio | 1 | | effectiveRatio | 0.016 | |