| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.262 | | leniency | 0.524 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.94% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1416 | | totalAiIsmAdverbs | 2 | | 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) | |
| 64.69% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1416 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "etched" | | 1 | "aligned" | | 2 | "furrowed" | | 3 | "weight" | | 4 | "chill" | | 5 | "fractured" | | 6 | "standard" | | 7 | "familiar" | | 8 | "footsteps" | | 9 | "rhythmic" |
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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 | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 125 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 62 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1416 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.55% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 892 | | uniqueNames | 7 | | maxNameDensity | 1.01 | | worstName | "Fenchurch" | | maxWindowNameDensity | 2 | | worstWindowName | "Fenchurch" | | discoveredNames | | Tube | 1 | | Met | 1 | | Fenchurch | 9 | | Quinn | 9 | | Roundhouse | 1 | | Morris | 1 | | Victorian | 1 |
| | persons | | 0 | "Met" | | 1 | "Fenchurch" | | 2 | "Quinn" | | 3 | "Morris" |
| | places | (empty) | | globalScore | 0.996 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 4 | | matches | | 0 | "ch the Met had apparently pried open with a c" | | 1 | "Something between wonder and surprise, his lips" | | 2 | "something like it before, in a case file she" | | 3 | "quite fear but lived in the same neighbourhood" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.706 | | wordCount | 1416 | | matches | | 0 | "Not the copper tang of blood or the sour reek of a body gone to rot, but something older" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 125 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 32.18 | | std | 26.8 | | cv | 0.833 | | sampleLengths | | 0 | 41 | | 1 | 92 | | 2 | 61 | | 3 | 35 | | 4 | 32 | | 5 | 51 | | 6 | 64 | | 7 | 5 | | 8 | 33 | | 9 | 36 | | 10 | 5 | | 11 | 28 | | 12 | 24 | | 13 | 1 | | 14 | 69 | | 15 | 124 | | 16 | 7 | | 17 | 13 | | 18 | 50 | | 19 | 13 | | 20 | 39 | | 21 | 60 | | 22 | 36 | | 23 | 4 | | 24 | 54 | | 25 | 16 | | 26 | 62 | | 27 | 8 | | 28 | 72 | | 29 | 11 | | 30 | 13 | | 31 | 36 | | 32 | 20 | | 33 | 21 | | 34 | 24 | | 35 | 8 | | 36 | 2 | | 37 | 32 | | 38 | 6 | | 39 | 41 | | 40 | 10 | | 41 | 1 | | 42 | 2 | | 43 | 54 |
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| 90.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 94 | | matches | | 0 | "been closed" | | 1 | "were folded" | | 2 | "been wiped" | | 3 | "was flushed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 159 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 125 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 898 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.026726057906458798 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008908685968819599 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 125 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 125 | | mean | 11.33 | | std | 9.46 | | cv | 0.836 | | sampleLengths | | 0 | 5 | | 1 | 19 | | 2 | 1 | | 3 | 16 | | 4 | 30 | | 5 | 8 | | 6 | 23 | | 7 | 31 | | 8 | 9 | | 9 | 5 | | 10 | 3 | | 11 | 20 | | 12 | 24 | | 13 | 16 | | 14 | 9 | | 15 | 10 | | 16 | 11 | | 17 | 3 | | 18 | 3 | | 19 | 11 | | 20 | 4 | | 21 | 27 | | 22 | 24 | | 23 | 5 | | 24 | 11 | | 25 | 2 | | 26 | 2 | | 27 | 19 | | 28 | 3 | | 29 | 3 | | 30 | 5 | | 31 | 5 | | 32 | 9 | | 33 | 5 | | 34 | 2 | | 35 | 31 | | 36 | 19 | | 37 | 17 | | 38 | 5 | | 39 | 28 | | 40 | 5 | | 41 | 2 | | 42 | 3 | | 43 | 14 | | 44 | 1 | | 45 | 11 | | 46 | 22 | | 47 | 18 | | 48 | 14 | | 49 | 4 |
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| 76.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.488 | | totalSentences | 125 | | uniqueOpeners | 61 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 80 | | matches | | 0 | "She'd made the climb down" | | 1 | "His hands were folded across" | | 2 | "He pressed his palms together" | | 3 | "She crossed the platform, her" | | 4 | "He shrugged, his heavy coat" | | 5 | "She leaned in." | | 6 | "His fingernails were clean, unbroken." | | 7 | "His brow furrowed." | | 8 | "She moved along the platform," | | 9 | "She knelt and ran her" | | 10 | "She photographed it with her" | | 11 | "She'd seen something like it" | | 12 | "She pocketed her phone and" | | 13 | "It was heavier than it" | | 14 | "She turned it over" | | 15 | "She aimed her torch at" | | 16 | "She turned it over and" | | 17 | "She checked the back of" | | 18 | "His hand jerked back." | | 19 | "She walked to the wall" |
| | ratio | 0.275 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 80 | | matches | | 0 | "The smell hit her first." | | 1 | "Quinn ducked beneath the yellow" | | 2 | "Camden station had been closed" | | 3 | "The platforms sat thirty metres" | | 4 | "She'd made the climb down" | | 5 | "A man lay on the" | | 6 | "His hands were folded across" | | 7 | "He pressed his palms together" | | 8 | "She crossed the platform, her" | | 9 | "He shrugged, his heavy coat" | | 10 | "Quinn crouched beside the corpse." | | 11 | "Something between wonder and surprise," | | 12 | "She leaned in." | | 13 | "His fingernails were clean, unbroken." | | 14 | "The suit jacket hung perfectly," | | 15 | "Quinn stood, her knees protesting" | | 16 | "Fenchurch looked at the ground." | | 17 | "His brow furrowed." | | 18 | "She moved along the platform," | | 19 | "The tile walls were intact," |
| | ratio | 0.713 | |
| 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 | 37 | | technicalSentenceCount | 2 | | matches | | 0 | "His hands were folded across his chest as though someone had arranged him for a funeral, palms up, fingers relaxed." | | 1 | "Eva's voice came back tight with something that wasn't quite fear but lived in the same neighbourhood." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 4 | | matches | | 0 | "She crossed, her torch beam sweeping the ground" | | 1 | "He shrugged, his heavy coat bunching at the shoulders" | | 2 | "Quinn stood, her knees protesting" | | 3 | "Fenchurch shifted, uncomfortable" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.024 | | leniency | 0.048 | | rawRatio | 0 | | effectiveRatio | 0 | |