| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 113 | | tagDensity | 0.097 | | leniency | 0.195 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.21% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1789 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | 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) | |
| 86.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1789 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "pulse" | | 1 | "trembled" | | 2 | "sense of" | | 3 | "warmth" |
| |
| 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 | 151 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 151 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 253 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1789 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 24 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 90 | | wordCount | 1139 | | uniqueNames | 14 | | maxNameDensity | 2.46 | | worstName | "Quinn" | | maxWindowNameDensity | 6.5 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 28 | | Tube | 1 | | Brighton | 1 | | Sergeant | 1 | | Patel | 26 | | Camden | 1 | | Oram | 9 | | Havelock | 1 | | Street | 1 | | Kowalski | 1 | | Eva | 17 | | Morris | 1 | | Whitechapel | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Patel" | | 4 | "Oram" | | 5 | "Kowalski" | | 6 | "Eva" | | 7 | "Morris" |
| | places | | 0 | "Brighton" | | 1 | "Camden" | | 2 | "Havelock" | | 3 | "Street" |
| | globalScore | 0.271 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | 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 | 1789 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 253 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 152 | | mean | 11.77 | | std | 11.68 | | cv | 0.992 | | sampleLengths | | 0 | 14 | | 1 | 49 | | 2 | 8 | | 3 | 38 | | 4 | 5 | | 5 | 16 | | 6 | 25 | | 7 | 5 | | 8 | 4 | | 9 | 33 | | 10 | 13 | | 11 | 49 | | 12 | 18 | | 13 | 3 | | 14 | 18 | | 15 | 2 | | 16 | 36 | | 17 | 5 | | 18 | 2 | | 19 | 3 | | 20 | 7 | | 21 | 21 | | 22 | 2 | | 23 | 9 | | 24 | 5 | | 25 | 8 | | 26 | 17 | | 27 | 10 | | 28 | 8 | | 29 | 37 | | 30 | 6 | | 31 | 6 | | 32 | 3 | | 33 | 15 | | 34 | 11 | | 35 | 5 | | 36 | 5 | | 37 | 13 | | 38 | 4 | | 39 | 17 | | 40 | 10 | | 41 | 12 | | 42 | 7 | | 43 | 67 | | 44 | 4 | | 45 | 8 | | 46 | 1 | | 47 | 7 | | 48 | 6 | | 49 | 11 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 151 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 198 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 253 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1141 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.007887817703768623 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0008764241893076249 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 253 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 253 | | mean | 7.07 | | std | 4.88 | | cv | 0.691 | | sampleLengths | | 0 | 14 | | 1 | 20 | | 2 | 5 | | 3 | 24 | | 4 | 8 | | 5 | 22 | | 6 | 10 | | 7 | 6 | | 8 | 5 | | 9 | 12 | | 10 | 4 | | 11 | 11 | | 12 | 14 | | 13 | 4 | | 14 | 1 | | 15 | 4 | | 16 | 20 | | 17 | 13 | | 18 | 6 | | 19 | 7 | | 20 | 8 | | 21 | 10 | | 22 | 7 | | 23 | 5 | | 24 | 19 | | 25 | 5 | | 26 | 13 | | 27 | 3 | | 28 | 6 | | 29 | 12 | | 30 | 2 | | 31 | 5 | | 32 | 10 | | 33 | 21 | | 34 | 5 | | 35 | 2 | | 36 | 3 | | 37 | 7 | | 38 | 14 | | 39 | 7 | | 40 | 2 | | 41 | 3 | | 42 | 6 | | 43 | 5 | | 44 | 8 | | 45 | 14 | | 46 | 3 | | 47 | 3 | | 48 | 7 | | 49 | 8 |
| |
| 46.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.2727272727272727 | | totalSentences | 253 | | uniqueOpeners | 69 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 138 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 138 | | matches | | 0 | "His throat bore a narrow" | | 1 | "Her leather watch clicked against" | | 2 | "He held a clear evidence" | | 3 | "His coat had collected dust" | | 4 | "His fingers curled around a" | | 5 | "Its needle trembled beneath the" | | 6 | "It pointed down the platform," | | 7 | "She moved around Oram without" | | 8 | "It had run under his" | | 9 | "They began six feet from" | | 10 | "She climbed after it." | | 11 | "Its edge had dried into" | | 12 | "She looked up at the" | | 13 | "He glanced at the steps." | | 14 | "He looked towards the tunnel." | | 15 | "She knelt near his coat" | | 16 | "She opened the flap and" | | 17 | "Her curly red hair had" | | 18 | "He lowered the radio." | | 19 | "She opened the folded paper" |
| | ratio | 0.188 | |
| 32.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 118 | | totalSentences | 138 | | matches | | 0 | "Detective Harlow Quinn ducked beneath" | | 1 | "The platform lights had failed" | | 2 | "Dust hung above the tracks." | | 3 | "The body lay beside the" | | 4 | "A man in a charcoal" | | 5 | "Blood spread across his shirt" | | 6 | "His throat bore a narrow" | | 7 | "Quinn stopped three paces away." | | 8 | "Her leather watch clicked against" | | 9 | "Detective Sergeant Patel called from" | | 10 | "Quinn studied the corpse." | | 11 | "Patel wore a disposable suit" | | 12 | "He held a clear evidence" | | 13 | "Quinn looked past him towards" | | 14 | "The tunnel beyond the platform" | | 15 | "The police had entered through" | | 16 | "Patel pointed towards the ticket" | | 17 | "A brass padlock hung from" | | 18 | "Quinn crouched beside the body." | | 19 | "The man’s shoes were clean" |
| | ratio | 0.855 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 138 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 2 | | fancyTags | | 0 | "he continued (continue)" | | 1 | "Patel snapped (snap)" |
| | dialogueSentences | 113 | | tagDensity | 0.088 | | leniency | 0.177 | | rawRatio | 0.2 | | effectiveRatio | 0.035 | |