| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.419 | | leniency | 0.839 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 69.79% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 993 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "very" | | 1 | "carefully" | | 2 | "completely" | | 3 | "slightly" | | 4 | "sharply" |
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| 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) | |
| 89.93% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 993 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 52 | | matches | (empty) | |
| 60.44% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 52 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 70 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 996 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 784 | | uniqueNames | 14 | | maxNameDensity | 0.64 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Eva | 4 | | Tuesday | 1 | | British | 1 | | Library | 1 | | Ptolemy | 3 | | Golden | 1 | | Empress | 1 | | Moreau | 1 | | London | 3 | | Lucien | 3 | | East | 1 | | Tried | 1 | | Rory | 5 | | Like | 1 |
| | persons | | 0 | "Eva" | | 1 | "Ptolemy" | | 2 | "Empress" | | 3 | "Moreau" | | 4 | "Lucien" | | 5 | "Rory" |
| | places | | 0 | "British" | | 1 | "Library" | | 2 | "Golden" | | 3 | "London" | | 4 | "East" |
| | globalScore | 1 | | windowScore | 1 | |
| 14.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | glossingSentenceCount | 2 | | matches | | 0 | "quite protected him" | | 1 | "quite decide where to direct her attention" |
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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 | 996 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 70 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 26.92 | | std | 27.49 | | cv | 1.021 | | sampleLengths | | 0 | 95 | | 1 | 7 | | 2 | 74 | | 3 | 16 | | 4 | 17 | | 5 | 65 | | 6 | 4 | | 7 | 3 | | 8 | 7 | | 9 | 59 | | 10 | 6 | | 11 | 103 | | 12 | 14 | | 13 | 39 | | 14 | 11 | | 15 | 4 | | 16 | 12 | | 17 | 2 | | 18 | 62 | | 19 | 5 | | 20 | 11 | | 21 | 46 | | 22 | 7 | | 23 | 25 | | 24 | 2 | | 25 | 9 | | 26 | 47 | | 27 | 35 | | 28 | 8 | | 29 | 2 | | 30 | 73 | | 31 | 16 | | 32 | 45 | | 33 | 27 | | 34 | 16 | | 35 | 16 | | 36 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 52 | | matches | (empty) | |
| 48.48% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 132 | | matches | | 0 | "was buying" | | 1 | "wasn't towering " | | 2 | "wasn't performing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 70 | | ratio | 0.057 | | matches | | 0 | "His charcoal suit was immaculate despite the weather, which had been vicious all evening — the kind of London rain that came in sideways and picked pockets." | | 1 | "He looked at her then — properly looked, the amber eye and the black one both doing the thing they did where she couldn't quite decide where to direct her attention." | | 2 | "He occupied space in a way that had nothing to do with his height — five-eleven wasn't towering — and everything to do with how completely he seemed to fill whatever room he entered." | | 3 | "The rain worked at the window behind him and she could hear the street below — a motorbike, someone's laughter cutting off sharply, the ordinary noise of East London doing what it always did." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 782 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.03580562659846547 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.014066496163682864 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 70 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 70 | | mean | 14.23 | | std | 11.17 | | cv | 0.785 | | sampleLengths | | 0 | 37 | | 1 | 44 | | 2 | 14 | | 3 | 7 | | 4 | 27 | | 5 | 27 | | 6 | 20 | | 7 | 16 | | 8 | 12 | | 9 | 5 | | 10 | 31 | | 11 | 34 | | 12 | 4 | | 13 | 3 | | 14 | 7 | | 15 | 5 | | 16 | 23 | | 17 | 3 | | 18 | 28 | | 19 | 6 | | 20 | 12 | | 21 | 34 | | 22 | 5 | | 23 | 15 | | 24 | 37 | | 25 | 11 | | 26 | 3 | | 27 | 39 | | 28 | 11 | | 29 | 4 | | 30 | 12 | | 31 | 2 | | 32 | 30 | | 33 | 5 | | 34 | 27 | | 35 | 5 | | 36 | 11 | | 37 | 16 | | 38 | 12 | | 39 | 18 | | 40 | 7 | | 41 | 8 | | 42 | 15 | | 43 | 2 | | 44 | 2 | | 45 | 9 | | 46 | 3 | | 47 | 10 | | 48 | 34 | | 49 | 5 |
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| 67.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.42857142857142855 | | totalSentences | 70 | | uniqueOpeners | 30 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 47 | | matches | | 0 | "She set the box down," | | 1 | "She opened it on the" | | 2 | "His charcoal suit was immaculate" | | 3 | "His eyes moved past her" | | 4 | "She didn't move from the" | | 5 | "He looked at her then" | | 6 | "He shifted his weight onto" | | 7 | "She shut three deadbolts behind" | | 8 | "He occupied space in a" | | 9 | "She gestured at the sofa" | | 10 | "She went to the kitchen" | | 11 | "she said, loud enough to" | | 12 | "She came back into the" | | 13 | "She sat in the armchair" | | 14 | "He hadn't touched the tea." | | 15 | "He said it without preamble," | | 16 | "His accent thickened the vowels" | | 17 | "She let the word sit" | | 18 | "She looked up" | | 19 | "His jaw moved." |
| | ratio | 0.553 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 47 | | matches | | 0 | "The second knock came harder" | | 1 | "Rory had been sitting cross-legged" | | 2 | "She set the box down," | | 3 | "She opened it on the" | | 4 | "Lucien Moreau stood in the" | | 5 | "His charcoal suit was immaculate" | | 6 | "A single dark streak of" | | 7 | "His eyes moved past her" | | 8 | "She didn't move from the" | | 9 | "He looked at her then" | | 10 | "He shifted his weight onto" | | 11 | "The rain intensified behind him." | | 12 | "Ptolemy appeared at her ankle," | | 13 | "Rory stepped back." | | 14 | "She shut three deadbolts behind" | | 15 | "The flat looked worse with" | | 16 | "He occupied space in a" | | 17 | "Books teetered in their stacks." | | 18 | "Eva's research notes lay across" | | 19 | "The single lamp she'd been" |
| | ratio | 0.957 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 7 | | matches | | 0 | "Rory had been sitting cross-legged on the floor with Ptolemy's warm weight pressed against her thigh, eating cold noodles from the takeaway box she'd brought ov…" | | 1 | "Lucien Moreau stood in the narrow stairwell with rain on his shoulders and a look on his face that could have meant anything or nothing at all." | | 2 | "His charcoal suit was immaculate despite the weather, which had been vicious all evening — the kind of London rain that came in sideways and picked pockets." | | 3 | "He occupied space in a way that had nothing to do with his height — five-eleven wasn't towering — and everything to do with how completely he seemed to fill wha…" | | 4 | "The single lamp she'd been using cast the place in amber light, which did nothing for her composure because amber was the colour of one of his eyes and she'd sp…" | | 5 | "The rain worked at the window behind him and she could hear the street below — a motorbike, someone's laughter cutting off sharply, the ordinary noise of East L…" | | 6 | "She'd practiced steadiness under worse conditions than this, under conditions that had left her on a night bus to London with one bag and Eva's address on her p…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.129 | | leniency | 0.258 | | rawRatio | 0 | | effectiveRatio | 0 | |