| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.32 | | leniency | 0.64 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1202 | | 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) | |
| 79.20% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1202 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "etched" | | 1 | "magnetic" | | 2 | "resolved" | | 3 | "silence" | | 4 | "stark" |
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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 | 107 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 107 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 141 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1202 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 902 | | uniqueNames | 9 | | maxNameDensity | 1.77 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 16 | | Tube | 2 | | Camden | 1 | | Yani | 1 | | Fletcher | 7 | | One | 1 | | Kowalski | 4 | | Bermondsey | 1 | | Eva | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Camden" | | 2 | "Yani" | | 3 | "Fletcher" | | 4 | "Kowalski" | | 5 | "Eva" |
| | places | | | globalScore | 0.613 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like a man reading something he di" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.832 | | wordCount | 1202 | | matches | | 0 | "Not the symbols themselves but the arrangement" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 141 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 73 | | mean | 16.47 | | std | 16.73 | | cv | 1.016 | | sampleLengths | | 0 | 27 | | 1 | 5 | | 2 | 45 | | 3 | 15 | | 4 | 29 | | 5 | 5 | | 6 | 53 | | 7 | 6 | | 8 | 42 | | 9 | 29 | | 10 | 5 | | 11 | 3 | | 12 | 33 | | 13 | 11 | | 14 | 24 | | 15 | 66 | | 16 | 10 | | 17 | 21 | | 18 | 3 | | 19 | 57 | | 20 | 4 | | 21 | 2 | | 22 | 26 | | 23 | 1 | | 24 | 4 | | 25 | 27 | | 26 | 6 | | 27 | 3 | | 28 | 2 | | 29 | 24 | | 30 | 11 | | 31 | 3 | | 32 | 2 | | 33 | 8 | | 34 | 17 | | 35 | 5 | | 36 | 44 | | 37 | 12 | | 38 | 26 | | 39 | 2 | | 40 | 1 | | 41 | 6 | | 42 | 17 | | 43 | 3 | | 44 | 37 | | 45 | 5 | | 46 | 11 | | 47 | 1 | | 48 | 5 | | 49 | 1 |
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| 92.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 107 | | matches | | 0 | "was etched" | | 1 | "was wrapped" | | 2 | "was smeared" | | 3 | "was gone" |
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| 27.96% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 155 | | matches | | 0 | "was looking" | | 1 | "was staring" | | 2 | "were typically screaming" | | 3 | "was stepping" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 141 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 903 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.031007751937984496 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.009966777408637873 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 141 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 141 | | mean | 8.52 | | std | 6.82 | | cv | 0.8 | | sampleLengths | | 0 | 27 | | 1 | 5 | | 2 | 12 | | 3 | 25 | | 4 | 8 | | 5 | 15 | | 6 | 27 | | 7 | 2 | | 8 | 5 | | 9 | 10 | | 10 | 6 | | 11 | 20 | | 12 | 5 | | 13 | 3 | | 14 | 9 | | 15 | 6 | | 16 | 10 | | 17 | 21 | | 18 | 11 | | 19 | 6 | | 20 | 23 | | 21 | 5 | | 22 | 2 | | 23 | 1 | | 24 | 2 | | 25 | 25 | | 26 | 6 | | 27 | 11 | | 28 | 13 | | 29 | 1 | | 30 | 1 | | 31 | 9 | | 32 | 2 | | 33 | 15 | | 34 | 12 | | 35 | 3 | | 36 | 11 | | 37 | 3 | | 38 | 20 | | 39 | 10 | | 40 | 5 | | 41 | 16 | | 42 | 3 | | 43 | 17 | | 44 | 3 | | 45 | 2 | | 46 | 22 | | 47 | 13 | | 48 | 4 | | 49 | 2 |
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| 57.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.40425531914893614 | | totalSentences | 141 | | uniqueOpeners | 57 | |
| 36.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 91 | | matches | | 0 | "Then she looked at the" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 91 | | matches | | 0 | "She was already past him." | | 1 | "He paused for the detail" | | 2 | "His face was slack and" | | 3 | "She photographed them methodically, moving" | | 4 | "She stood and kept moving" | | 5 | "She snapped on a glove" | | 6 | "She turned it." | | 7 | "She was staring at the" | | 8 | "Her green eyes were dry," | | 9 | "She pushed her glasses up" | | 10 | "She stopped herself" | | 11 | "It had happened before the" | | 12 | "She returned to the bench" | | 13 | "Her left hand found a" | | 14 | "She tilted her palm" | | 15 | "She was stepping away when" | | 16 | "Her body knew before her" | | 17 | "Her knuckles had gone white" | | 18 | "She was halfway to the" | | 19 | "He sounded like a man" |
| | ratio | 0.22 | |
| 47.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 91 | | matches | | 0 | "The police tape caught Quinn's" | | 1 | "She was already past him." | | 2 | "The abandoned Tube station beneath" | | 3 | "The old escalators stood frozen" | | 4 | "Quinn counted six technicians, three" | | 5 | "DS Yani Fletcher met her" | | 6 | "He paused for the detail" | | 7 | "Quinn crouched beside the body." | | 8 | "The man wore a long" | | 9 | "Someone who knew how to" | | 10 | "His face was slack and" | | 11 | "The markings were on the" | | 12 | "Someone had drawn a circle" | | 13 | "She photographed them methodically, moving" | | 14 | "Fletcher said, at her shoulder" | | 15 | "She stood and kept moving" | | 16 | "Fletcher crouched where she'd been" | | 17 | "She snapped on a glove" | | 18 | "The face was etched with" | | 19 | "The same kind of marks" |
| | ratio | 0.824 | |
| 54.95% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 91 | | matches | | 0 | "Before Fletcher's people had come" |
| | ratio | 0.011 | |
| 43.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 5 | | matches | | 0 | "His face was slack and pale, eyes fixed on the curved ceiling above as though it had said something surprising." | | 1 | "The needle inside swung south, settled, and swung south again, as though it couldn't find what it was looking for." | | 2 | "A freckled complexion that made her look young until you got close, at which point her face told a more complicated story." | | 3 | "People who found bodies in abandoned Tube stations were typically screaming or vomiting." | | 4 | "Eva Kowalski folded her hands in her lap with a precision that looked borrowed." |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn said, without turning" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.14 | | leniency | 0.28 | | rawRatio | 0 | | effectiveRatio | 0 | |