| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said quietly [quietly]" |
| | dialogueSentences | 55 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.091 | | effectiveRatio | 0.036 | |
| 96.16% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1302 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 57.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1302 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "scanning" | | 1 | "measured" | | 2 | "silence" | | 3 | "flicker" | | 4 | "familiar" | | 5 | "etched" | | 6 | "weight" | | 7 | "warmth" | | 8 | "flickered" |
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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 | 1 | | narrationSentences | 80 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 80 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 124 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1291 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 909 | | uniqueNames | 7 | | maxNameDensity | 0.88 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 8 | | Cardiff | 1 | | Evan | 1 | | Liam | 7 | | Silas | 1 | | Sophie | 1 | | London | 1 |
| | persons | | 0 | "Rory" | | 1 | "Evan" | | 2 | "Liam" | | 3 | "Silas" | | 4 | "Sophie" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 45.08% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.549 | | wordCount | 1291 | | matches | | 0 | "not anger, not sadness, but a kind of recognition" | | 1 | "not sadness, but a kind of recognition" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 124 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 19.27 | | std | 17.95 | | cv | 0.931 | | sampleLengths | | 0 | 81 | | 1 | 62 | | 2 | 5 | | 3 | 26 | | 4 | 1 | | 5 | 13 | | 6 | 1 | | 7 | 65 | | 8 | 6 | | 9 | 33 | | 10 | 2 | | 11 | 13 | | 12 | 12 | | 13 | 2 | | 14 | 1 | | 15 | 30 | | 16 | 4 | | 17 | 10 | | 18 | 30 | | 19 | 4 | | 20 | 35 | | 21 | 31 | | 22 | 6 | | 23 | 11 | | 24 | 45 | | 25 | 21 | | 26 | 4 | | 27 | 44 | | 28 | 33 | | 29 | 5 | | 30 | 39 | | 31 | 7 | | 32 | 9 | | 33 | 15 | | 34 | 4 | | 35 | 6 | | 36 | 55 | | 37 | 5 | | 38 | 1 | | 39 | 1 | | 40 | 36 | | 41 | 15 | | 42 | 5 | | 43 | 18 | | 44 | 40 | | 45 | 20 | | 46 | 30 | | 47 | 20 | | 48 | 11 | | 49 | 7 |
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| 96.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 80 | | matches | | 0 | "were squared" | | 1 | "been replaced" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 165 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 124 | | ratio | 0.081 | | matches | | 0 | "Inside, the bar held its usual quiet—the clink of a glass against oak, the low murmur of a conversation near the back, the smell of old wood and spilled ale." | | 1 | "She remembered the way Liam had laughed then—too loud, too easy, filling the room like he owned it." | | 2 | "He tilted his head, a gesture she remembered—assessing, lawyerly." | | 3 | "Rory reached for a bottle of his old favourite—a single malt she’d stocked because Silas liked it too." | | 4 | "She’d heard about her through mutual friends—a solicitor from a good family, blonde, tennis player, exactly the kind of woman Liam’s mother had always wanted for him." | | 5 | "Rory looked at him then—really looked." | | 6 | "Liam ran a hand through his hair—still the same gesture, still the same way he did it when he was lost." | | 7 | "Something shifted in his eyes—not anger, not sadness, but a kind of recognition." | | 8 | "\"It suits you.\" He smiled, but it was the small one—the one he used when he was sad." | | 9 | "The weight of what they'd said—what they hadn't said—settled in her chest like stones." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 924 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.03463203463203463 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008658008658008658 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 124 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 124 | | mean | 10.41 | | std | 7.72 | | cv | 0.741 | | sampleLengths | | 0 | 14 | | 1 | 30 | | 2 | 17 | | 3 | 20 | | 4 | 8 | | 5 | 13 | | 6 | 17 | | 7 | 24 | | 8 | 5 | | 9 | 2 | | 10 | 6 | | 11 | 18 | | 12 | 1 | | 13 | 8 | | 14 | 5 | | 15 | 1 | | 16 | 2 | | 17 | 8 | | 18 | 37 | | 19 | 18 | | 20 | 4 | | 21 | 1 | | 22 | 1 | | 23 | 23 | | 24 | 7 | | 25 | 3 | | 26 | 2 | | 27 | 9 | | 28 | 4 | | 29 | 8 | | 30 | 4 | | 31 | 2 | | 32 | 1 | | 33 | 16 | | 34 | 14 | | 35 | 4 | | 36 | 10 | | 37 | 22 | | 38 | 8 | | 39 | 4 | | 40 | 19 | | 41 | 16 | | 42 | 18 | | 43 | 13 | | 44 | 4 | | 45 | 2 | | 46 | 11 | | 47 | 18 | | 48 | 17 | | 49 | 10 |
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| 50.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3548387096774194 | | totalSentences | 124 | | uniqueOpeners | 44 | |
| 48.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 69 | | matches | | 0 | "Then his eyes found her." |
| | ratio | 0.014 | |
| 28.70% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 69 | | matches | | 0 | "He was tall, broad-shouldered, with" | | 1 | "He wiped a hand across" | | 2 | "His mouth opened, closed, opened" | | 3 | "She set it down slow." | | 4 | "She remembered the way Liam" | | 5 | "He crossed the floor, bypassing" | | 6 | "His hands went into his" | | 7 | "He tilted his head, a" | | 8 | "She touched the black ends" | | 9 | "He pulled out a stool" | | 10 | "She poured a measure into" | | 11 | "He stared at it." | | 12 | "He picked up the glass," | | 13 | "She didn't answer." | | 14 | "She poured herself a tonic" | | 15 | "He said it like a" | | 16 | "She’d never met Sophie." | | 17 | "She’d heard about her through" | | 18 | "His shoulders were squared in" | | 19 | "he said quietly" |
| | ratio | 0.478 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 69 | | matches | | 0 | "The green neon hummed above" | | 1 | "Rory stood behind the counter," | | 2 | "The rag was grey and" | | 3 | "The door swung open with" | | 4 | "A man stepped in, shaking" | | 5 | "He was tall, broad-shouldered, with" | | 6 | "He wiped a hand across" | | 7 | "His mouth opened, closed, opened" | | 8 | "A name slipped out, tentative," | | 9 | "The glass in her hand" | | 10 | "She set it down slow." | | 11 | "That was how long since" | | 12 | "She remembered the way Liam" | | 13 | "This laugh was different." | | 14 | "He crossed the floor, bypassing" | | 15 | "His hands went into his" | | 16 | "He tilted his head, a" | | 17 | "She touched the black ends" | | 18 | "Silence hung between them, filled" | | 19 | "Rory picked up another glass," |
| | ratio | 0.942 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 86.47% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 3 | | matches | | 0 | "He was tall, broad-shouldered, with the kind of haircut that cost more than Rory’s weekly takeaway budget." | | 1 | "His shoulders were squared in a way that made him look armour-plated." | | 2 | "But there was a new line between his brows, etched deep, and the easy laughter that used to bubble under his skin had been replaced by something heavier." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 55 | | tagDensity | 0.109 | | leniency | 0.218 | | rawRatio | 0 | | effectiveRatio | 0 | |