| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 106 | | tagDensity | 0.028 | | leniency | 0.057 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1910 | | 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) | |
| 79.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1910 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "velvet" | | 1 | "trembled" | | 2 | "etched" | | 3 | "pumping" | | 4 | "measured" |
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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 | 153 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 153 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 256 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1910 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 1 | | matches | | 0 | "From the platform, Eva called out." |
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| 29.15% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 69 | | wordCount | 1324 | | uniqueNames | 10 | | maxNameDensity | 2.42 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 32 | | Metropolitan | 1 | | Police | 1 | | Sergeant | 1 | | Baines | 14 | | Vane | 9 | | Bloomsbury | 1 | | Eva | 8 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Sergeant" | | 4 | "Baines" | | 5 | "Vane" | | 6 | "Eva" |
| | places | | | globalScore | 0.292 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 102 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like scratches until Quinn leaned" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1910 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 256 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 192 | | mean | 9.95 | | std | 11.56 | | cv | 1.162 | | sampleLengths | | 0 | 11 | | 1 | 52 | | 2 | 18 | | 3 | 31 | | 4 | 7 | | 5 | 8 | | 6 | 28 | | 7 | 3 | | 8 | 3 | | 9 | 3 | | 10 | 1 | | 11 | 6 | | 12 | 2 | | 13 | 2 | | 14 | 7 | | 15 | 4 | | 16 | 6 | | 17 | 3 | | 18 | 26 | | 19 | 34 | | 20 | 3 | | 21 | 5 | | 22 | 14 | | 23 | 9 | | 24 | 9 | | 25 | 6 | | 26 | 6 | | 27 | 58 | | 28 | 9 | | 29 | 20 | | 30 | 2 | | 31 | 1 | | 32 | 22 | | 33 | 1 | | 34 | 10 | | 35 | 15 | | 36 | 3 | | 37 | 5 | | 38 | 8 | | 39 | 52 | | 40 | 42 | | 41 | 12 | | 42 | 1 | | 43 | 1 | | 44 | 3 | | 45 | 4 | | 46 | 7 | | 47 | 4 | | 48 | 4 | | 49 | 8 |
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| 93.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 153 | | matches | | 0 | "been sewn" | | 1 | "been brushed" | | 2 | "been opened" | | 3 | "been wiped" | | 4 | "been painted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 219 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 256 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1325 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 34 | | adverbRatio | 0.025660377358490565 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004528301886792453 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 256 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 256 | | mean | 7.46 | | std | 5.29 | | cv | 0.709 | | sampleLengths | | 0 | 11 | | 1 | 10 | | 2 | 19 | | 3 | 23 | | 4 | 18 | | 5 | 14 | | 6 | 6 | | 7 | 11 | | 8 | 7 | | 9 | 8 | | 10 | 6 | | 11 | 13 | | 12 | 9 | | 13 | 3 | | 14 | 3 | | 15 | 3 | | 16 | 1 | | 17 | 6 | | 18 | 2 | | 19 | 2 | | 20 | 7 | | 21 | 4 | | 22 | 6 | | 23 | 3 | | 24 | 7 | | 25 | 10 | | 26 | 7 | | 27 | 2 | | 28 | 13 | | 29 | 13 | | 30 | 8 | | 31 | 3 | | 32 | 5 | | 33 | 14 | | 34 | 9 | | 35 | 9 | | 36 | 6 | | 37 | 6 | | 38 | 19 | | 39 | 15 | | 40 | 24 | | 41 | 9 | | 42 | 10 | | 43 | 10 | | 44 | 2 | | 45 | 1 | | 46 | 22 | | 47 | 1 | | 48 | 10 | | 49 | 15 |
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| 52.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.34375 | | totalSentences | 256 | | uniqueOpeners | 88 | |
| 23.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 142 | | matches | | | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 142 | | matches | | 0 | "Their soles looked too thin" | | 1 | "Her leather watch had stopped" | | 2 | "She tapped the glass with" | | 3 | "He had taken off his" | | 4 | "His tie hung loose, stained" | | 5 | "He looked up." | | 6 | "He wore a green velvet" | | 7 | "His skin had the grey," | | 8 | "His mouth had been sewn" | | 9 | "Her curly red hair spilled" | | 10 | "She held both hands around" | | 11 | "She crouched beside Vane." | | 12 | "she told the scene officer" | | 13 | "Its velvet had been brushed" | | 14 | "His left cuff had a" | | 15 | "She rubbed a gloved fingertip" | | 16 | "They had needed a steady" | | 17 | "Her gaze travelled from Vane’s" | | 18 | "Its hands still showed 2:17." | | 19 | "He followed her eyes." |
| | ratio | 0.218 | |
| 37.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 120 | | totalSentences | 142 | | matches | | 0 | "The old Tube station had" | | 1 | "Water had stripped the enamel" | | 2 | "Tiles lay broken across the" | | 3 | "The tracks ended beneath a" | | 4 | "Detective Harlow Quinn stepped through" | | 5 | "Neither wore Metropolitan Police issue" | | 6 | "Their soles looked too thin" | | 7 | "Quinn looked at the gate’s" | | 8 | "A chain wrapped through the" | | 9 | "The padlock hung open, its" | | 10 | "The constable blinked." | | 11 | "Quinn turned her sharp jaw" | | 12 | "The man reached for his" | | 13 | "Quinn walked on." | | 14 | "Her leather watch had stopped" | | 15 | "The second hand trembled between" | | 16 | "She tapped the glass with" | | 17 | "He had taken off his" | | 18 | "His tie hung loose, stained" | | 19 | "He looked up." |
| | ratio | 0.845 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 142 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 2 | | matches | | 0 | "Always at the edge of something that went wrong: private collections broken into, stolen manuscripts recovered without clear provenance, a fire at a shuttered b…" | | 1 | "Darkness filled it with a density that swallowed the torch beam after three feet." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 106 | | tagDensity | 0.028 | | leniency | 0.057 | | rawRatio | 0 | | effectiveRatio | 0 | |