| 80.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 3 | | adverbTags | | 0 | "She glanced around [around]" | | 1 | "Eva gestured vaguely [vaguely]" | | 2 | "Rory lied back [back]" |
| | dialogueSentences | 50 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.15 | | effectiveRatio | 0.12 | |
| 80.53% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1284 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slowly" | | 1 | "completely" | | 2 | "sharply" | | 3 | "slightly" | | 4 | "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) | |
| 18.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1284 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "familiar" | | 1 | "comforting" | | 2 | "gloom" | | 3 | "chill" | | 4 | "glinting" | | 5 | "chaotic" | | 6 | "whisper" | | 7 | "silence" | | 8 | "flickered" | | 9 | "flicker" | | 10 | "unspoken" | | 11 | "fractured" | | 12 | "weight" | | 13 | "depths" | | 14 | "echo" | | 15 | "tracing" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
| | 1 | | label | "flicker of emotion" | | count | 1 |
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| | highlights | | 0 | "clenched into fists" | | 1 | "a flicker of fear" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 90 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 9 | | totalWords | 1278 | | ratio | 0.007 | | matches | | 0 | "Just get out, Rory. Please. Get the next train." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 959 | | uniqueNames | 10 | | maxNameDensity | 2.5 | | worstName | "Rory" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 24 | | Raven | 1 | | Nest | 1 | | Silas | 9 | | Tuesday | 1 | | Eva | 15 | | Chinese | 1 | | Cardiff | 1 | | Evan | 1 | | London | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Silas" | | 4 | "Eva" | | 5 | "Evan" |
| | places | | | globalScore | 0.249 | | windowScore | 0.167 | |
| 77.54% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a woman who delivered Chinese" | | 1 | "looked like a woman who won arguments in" |
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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 | 1278 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 31.95 | | std | 20.43 | | cv | 0.639 | | sampleLengths | | 0 | 62 | | 1 | 23 | | 2 | 26 | | 3 | 28 | | 4 | 64 | | 5 | 11 | | 6 | 73 | | 7 | 2 | | 8 | 19 | | 9 | 30 | | 10 | 16 | | 11 | 15 | | 12 | 38 | | 13 | 21 | | 14 | 27 | | 15 | 104 | | 16 | 8 | | 17 | 12 | | 18 | 31 | | 19 | 16 | | 20 | 56 | | 21 | 8 | | 22 | 27 | | 23 | 50 | | 24 | 14 | | 25 | 23 | | 26 | 48 | | 27 | 43 | | 28 | 62 | | 29 | 14 | | 30 | 34 | | 31 | 35 | | 32 | 29 | | 33 | 30 | | 34 | 45 | | 35 | 32 | | 36 | 21 | | 37 | 32 | | 38 | 38 | | 39 | 11 |
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| 89.67% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 90 | | matches | | 0 | "was gone" | | 1 | "were fixed" | | 2 | "were clenched" | | 3 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 152 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 118 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 964 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 34 | | adverbRatio | 0.035269709543568464 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.014522821576763486 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 10.83 | | std | 7.76 | | cv | 0.716 | | sampleLengths | | 0 | 11 | | 1 | 3 | | 2 | 23 | | 3 | 25 | | 4 | 13 | | 5 | 5 | | 6 | 4 | | 7 | 1 | | 8 | 19 | | 9 | 7 | | 10 | 22 | | 11 | 6 | | 12 | 15 | | 13 | 4 | | 14 | 20 | | 15 | 3 | | 16 | 6 | | 17 | 10 | | 18 | 6 | | 19 | 6 | | 20 | 5 | | 21 | 8 | | 22 | 9 | | 23 | 20 | | 24 | 15 | | 25 | 9 | | 26 | 12 | | 27 | 2 | | 28 | 6 | | 29 | 10 | | 30 | 3 | | 31 | 14 | | 32 | 15 | | 33 | 1 | | 34 | 11 | | 35 | 5 | | 36 | 12 | | 37 | 3 | | 38 | 24 | | 39 | 14 | | 40 | 21 | | 41 | 2 | | 42 | 7 | | 43 | 7 | | 44 | 11 | | 45 | 3 | | 46 | 7 | | 47 | 27 | | 48 | 6 | | 49 | 17 |
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| 63.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4152542372881356 | | totalSentences | 118 | | uniqueOpeners | 49 | |
| 76.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 87 | | matches | | 0 | "Only her eyes were the" | | 1 | "*Just get out, Rory." |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 87 | | matches | | 0 | "She balanced a stack of" | | 1 | "His movements were precise, economical." | | 2 | "He didn’t look up." | | 3 | "He placed the glass upside" | | 4 | "She glanced around" | | 5 | "She moved towards the bar," | | 6 | "she said, her voice low" | | 7 | "She wrapped her fingers around" | | 8 | "She took a sip" | | 9 | "His hand rested on the" | | 10 | "She stared into her drink" | | 11 | "His gaze drifted past her," | | 12 | "It was smoother than she" | | 13 | "She turned slowly, her heart" | | 14 | "Her hair was a sleek," | | 15 | "She wore a tailored charcoal-grey" | | 16 | "She put her glass down," | | 17 | "She looked like a woman" | | 18 | "She looked away, towards the" | | 19 | "He stopped beside Rory, a" |
| | ratio | 0.299 | |
| 23.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 87 | | matches | | 0 | "The bell above the door" | | 1 | "Tonight, it grated." | | 2 | "She balanced a stack of" | | 3 | "The familiar, comforting gloom of" | | 4 | "Silas stood behind the bar," | | 5 | "His movements were precise, economical." | | 6 | "He didn’t look up." | | 7 | "Rory slid the boxes onto" | | 8 | "He placed the glass upside" | | 9 | "Rory ran a hand through" | | 10 | "She glanced around" | | 11 | "The bar was empty, save" | | 12 | "A lone drinker." | | 13 | "She moved towards the bar," | | 14 | "The fabric was heavy and" | | 15 | "she said, her voice low" | | 16 | "Silas reached for a bottle" | | 17 | "A chair scraped against the" | | 18 | "Rory paid it no mind," | | 19 | "The glass slid across the" |
| | ratio | 0.874 | |
| 57.47% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 87 | | matches | | 0 | "Before Rory could ask what" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 1 | | matches | | 0 | "She looked like a woman who delivered Chinese food for a living and lived in a cramped flat above a bar, a woman who still flinched at the sound of a raised voi…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 5 | | matches | | 0 | "He placed, its rim catching the low light" | | 1 | "she said, her voice low" | | 2 | "Rory asked, her voice harder than she intended" | | 3 | "Eva said, her voice flat and cold" | | 4 | "she said, her voice shaking slightly" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 50 | | tagDensity | 0.12 | | leniency | 0.24 | | rawRatio | 0.167 | | effectiveRatio | 0.04 | |