| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 28 | | adverbTagCount | 7 | | adverbTags | | 0 | "Eva pulled back [back]" | | 1 | "Eva gestured vaguely [vaguely]" | | 2 | "She touched automatically [automatically]" | | 3 | "Eva said quickly [quickly]" | | 4 | "Eva nodded too [too]" | | 5 | "Rory said quietly [quietly]" | | 6 | "Rory said eventually [eventually]" |
| | dialogueSentences | 61 | | tagDensity | 0.459 | | leniency | 0.918 | | rawRatio | 0.25 | | effectiveRatio | 0.23 | |
| 71.86% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1777 | | totalAiIsmAdverbs | 10 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | adverb | "deliberately" | | count | 1 |
| | 6 | |
| | highlights | | 0 | "suddenly" | | 1 | "carefully" | | 2 | "quickly" | | 3 | "lightly" | | 4 | "very" | | 5 | "deliberately" | | 6 | "really" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 85.93% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1777 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "flickered" | | 1 | "could feel" | | 2 | "flicker" | | 3 | "echoed" | | 4 | "firmly" |
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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 | 114 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 114 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 148 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 74 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1800 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 26 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 85 | | wordCount | 1225 | | uniqueNames | 16 | | maxNameDensity | 2.45 | | worstName | "Eva" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Eva" | | discoveredNames | | Golden | 3 | | Empress | 3 | | Nest | 1 | | Silas | 5 | | Eva | 30 | | Cardiff | 4 | | Converse | 1 | | Rory | 30 | | Parry | 1 | | Uni | 1 | | Evan | 1 | | London | 1 | | Time | 1 | | Since | 1 | | National | 1 | | Express | 1 |
| | persons | | 0 | "Silas" | | 1 | "Eva" | | 2 | "Rory" | | 3 | "Parry" | | 4 | "Evan" | | 5 | "Time" |
| | places | | 0 | "Golden" | | 1 | "Nest" | | 2 | "Cardiff" | | 3 | "London" |
| | globalScore | 0.276 | | windowScore | 0 | |
| 5.07% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 4 | | matches | | 0 | "smelled like lemon polish and spilt bitter" | | 1 | "smelled like something clean and sandalwoo" | | 2 | "smelled like roll-ups and cheap vanilla bo" | | 3 | "as if sensing the snag, set a glass down in front of Eva without her asking and a glass of water in front of Rory's usual spot" | | 4 | "quite hide" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.556 | | wordCount | 1800 | | matches | | 0 | "not unkindly, but appraising" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 148 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 28.57 | | std | 23.24 | | cv | 0.813 | | sampleLengths | | 0 | 7 | | 1 | 86 | | 2 | 66 | | 3 | 63 | | 4 | 7 | | 5 | 8 | | 6 | 21 | | 7 | 13 | | 8 | 63 | | 9 | 3 | | 10 | 2 | | 11 | 44 | | 12 | 1 | | 13 | 3 | | 14 | 77 | | 15 | 75 | | 16 | 27 | | 17 | 12 | | 18 | 51 | | 19 | 44 | | 20 | 5 | | 21 | 40 | | 22 | 34 | | 23 | 40 | | 24 | 7 | | 25 | 55 | | 26 | 18 | | 27 | 19 | | 28 | 7 | | 29 | 46 | | 30 | 11 | | 31 | 29 | | 32 | 76 | | 33 | 24 | | 34 | 24 | | 35 | 17 | | 36 | 14 | | 37 | 24 | | 38 | 39 | | 39 | 1 | | 40 | 38 | | 41 | 7 | | 42 | 2 | | 43 | 13 | | 44 | 24 | | 45 | 41 | | 46 | 3 | | 47 | 6 | | 48 | 7 | | 49 | 31 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 114 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 223 | | matches | | 0 | "was coming" | | 1 | "was already pooling" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 148 | | ratio | 0.007 | | matches | | 0 | "She looked - Rory fumbled for the word and disliked it as soon as she found it - finished." |
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| 92.06% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 489 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.049079754601226995 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0081799591002045 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 148 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 148 | | mean | 12.16 | | std | 11.09 | | cv | 0.912 | | sampleLengths | | 0 | 7 | | 1 | 47 | | 2 | 2 | | 3 | 11 | | 4 | 26 | | 5 | 7 | | 6 | 12 | | 7 | 39 | | 8 | 8 | | 9 | 5 | | 10 | 25 | | 11 | 18 | | 12 | 15 | | 13 | 7 | | 14 | 8 | | 15 | 9 | | 16 | 12 | | 17 | 4 | | 18 | 9 | | 19 | 16 | | 20 | 27 | | 21 | 20 | | 22 | 3 | | 23 | 2 | | 24 | 18 | | 25 | 12 | | 26 | 14 | | 27 | 1 | | 28 | 3 | | 29 | 29 | | 30 | 48 | | 31 | 6 | | 32 | 9 | | 33 | 4 | | 34 | 19 | | 35 | 37 | | 36 | 17 | | 37 | 5 | | 38 | 5 | | 39 | 11 | | 40 | 1 | | 41 | 3 | | 42 | 27 | | 43 | 16 | | 44 | 5 | | 45 | 13 | | 46 | 17 | | 47 | 14 | | 48 | 5 | | 49 | 28 |
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| 50.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.34459459459459457 | | totalSentences | 148 | | uniqueOpeners | 51 | |
| 32.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 103 | | matches | | 0 | "Too late for the after-work" |
| | ratio | 0.01 | |
| 91.84% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 103 | | matches | | 0 | "It painted the damp brick" | | 1 | "She'd lived above" | | 2 | "It smelled like lemon polish" | | 3 | "He moved with that slight" | | 4 | "His hair had gone more" | | 5 | "He looked up when she" | | 6 | "she said, lifting the container" | | 7 | "He smiled a little." | | 8 | "She was halfway across the" | | 9 | "It was such a specific" | | 10 | "It was Eva." | | 11 | "Her dark blonde hair was" | | 12 | "Her makeup was done." | | 13 | "She looked - Rory fumbled" | | 14 | "She slid off the barstool." | | 15 | "They collided halfway." | | 16 | "He was good at that." | | 17 | "Her gaze snagged and held" | | 18 | "She felt twenty again, then" | | 19 | "She touched automatically at her" |
| | ratio | 0.32 | |
| 27.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 103 | | matches | | 0 | "The green buzzed before she" | | 1 | "Rory was coming down the" | | 2 | "It painted the damp brick" | | 3 | "She'd lived above" | | 4 | "The bar was in that" | | 5 | "It smelled like lemon polish" | | 6 | "Silas was behind the bar." | | 7 | "He moved with that slight" | | 8 | "His hair had gone more" | | 9 | "The silver signet on his" | | 10 | "He looked up when she" | | 11 | "she said, lifting the container" | | 12 | "He smiled a little." | | 13 | "She was halfway across the" | | 14 | "A woman in a charcoal" | | 15 | "The woman turned." | | 16 | "It was such a specific" | | 17 | "The woman's face opened in" | | 18 | "It was Eva." | | 19 | "Eva Parry, who'd sat with" |
| | ratio | 0.864 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 103 | | matches | (empty) | | ratio | 0 | |
| 27.03% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 6 | | matches | | 0 | "Not the Eva from Cardiff, who had worn the same battered Converse for three years straight and kept her hair dyed with cheap manic-panic that bled onto Rory's p…" | | 1 | "Put together in a way that made Rory suddenly, acutely aware of her own straight black hair pulled back messily at her shoulders, the faded delivery t-shirt for…" | | 2 | "Eva hugged hard and smelled like something clean and sandalwood that didn't belong to the girl Rory remembered, who'd smelled like roll-ups and cheap vanilla bo…" | | 3 | "Silas, as if sensing the snag, set a glass down in front of Eva without her asking and a glass of water in front of Rory's usual spot." | | 4 | "He gave Rory a small look, the barest lift of his chin that meant I'm here, and then he retreated to the far end of the bar and started very deliberately wiping…" | | 5 | "Thought about the flat upstairs with its single window that looked out onto the fire escape and the green sign that buzzed all night." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 28 | | uselessAdditionCount | 1 | | matches | | 0 | "She touched automatically, thumb finding the small ridge of the crescent scar there" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 61 | | tagDensity | 0.262 | | leniency | 0.525 | | rawRatio | 0 | | effectiveRatio | 0 | |