| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 44 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.57% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1456 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 55.36% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1456 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "crystal" | | 1 | "traced" | | 2 | "scanned" | | 3 | "glinting" | | 4 | "weight" | | 5 | "coded" | | 6 | "familiar" | | 7 | "potential" | | 8 | "could feel" | | 9 | "perfect" | | 10 | "silence" | | 11 | "flickered" | | 12 | "warmth" |
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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 | 90 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 90 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 123 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1447 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 896 | | uniqueNames | 22 | | maxNameDensity | 0.78 | | worstName | "Declan" | | maxWindowNameDensity | 2 | | worstWindowName | "Declan" | | discoveredNames | | Soho | 1 | | Rory | 2 | | Raven | 1 | | Nest | 1 | | Powers | 3 | | Yu-Fei-a | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Old | 1 | | Compton | 1 | | Street | 1 | | Rees | 2 | | Cardiff | 2 | | Evan | 2 | | London | 1 | | Silas | 3 | | Macallan | 1 | | Point | 1 | | Declan | 7 | | Tarrington | 1 | | Hale | 1 |
| | persons | | 0 | "Rory" | | 1 | "Powers" | | 2 | "Rees" | | 3 | "Evan" | | 4 | "Silas" | | 5 | "Declan" | | 6 | "Hale" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Yu-Fei-a" | | 3 | "Charing" | | 4 | "Cross" | | 5 | "Road" | | 6 | "Old" | | 7 | "Compton" | | 8 | "Street" | | 9 | "Cardiff" | | 10 | "London" | | 11 | "Point" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1447 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 123 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 26.8 | | std | 20.18 | | cv | 0.753 | | sampleLengths | | 0 | 60 | | 1 | 46 | | 2 | 40 | | 3 | 49 | | 4 | 49 | | 5 | 1 | | 6 | 2 | | 7 | 71 | | 8 | 9 | | 9 | 45 | | 10 | 31 | | 11 | 3 | | 12 | 24 | | 13 | 7 | | 14 | 7 | | 15 | 62 | | 16 | 11 | | 17 | 3 | | 18 | 47 | | 19 | 17 | | 20 | 37 | | 21 | 6 | | 22 | 35 | | 23 | 41 | | 24 | 2 | | 25 | 57 | | 26 | 11 | | 27 | 3 | | 28 | 50 | | 29 | 46 | | 30 | 5 | | 31 | 42 | | 32 | 43 | | 33 | 17 | | 34 | 42 | | 35 | 9 | | 36 | 39 | | 37 | 8 | | 38 | 63 | | 39 | 27 | | 40 | 52 | | 41 | 15 | | 42 | 7 | | 43 | 39 | | 44 | 26 | | 45 | 4 | | 46 | 42 | | 47 | 3 | | 48 | 3 | | 49 | 28 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 172 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 1 | | flaggedSentences | 8 | | totalSentences | 123 | | ratio | 0.065 | | matches | | 0 | "The door only opened twice in the first hour—a pair of tourists who left after one overpriced pint, then a regular she didn’t know by name." | | 1 | "He was broader across the shoulders, softened slightly by middle management—but the way he held himself, that slight tilt of the head when he wasn’t sure, was unmistakable." | | 2 | "The fabric was expensive—soft wool, bespoke cut." | | 3 | "She felt the weight of his gaze—the inventory he was taking." | | 4 | "That was the same laugh—quick, surprised, like he hadn’t expected to find anything funny." | | 5 | "The scar sat pale against her skin—a perfect crescent, the mark of a button she’d caught falling off a ladder at twelve." | | 6 | "The job had saved her—the simple rhythm of taking packages from Point A to B, of every day being interchangeable, of not being watched or followed or argued into corners." | | 7 | "The rain had stopped; a slice of neon from the sign outside bled green across his cheekbone." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 907 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.024255788313120176 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005512679162072767 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 123 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 123 | | mean | 11.76 | | std | 9.7 | | cv | 0.825 | | sampleLengths | | 0 | 23 | | 1 | 16 | | 2 | 5 | | 3 | 16 | | 4 | 7 | | 5 | 26 | | 6 | 13 | | 7 | 7 | | 8 | 22 | | 9 | 3 | | 10 | 8 | | 11 | 20 | | 12 | 14 | | 13 | 15 | | 14 | 8 | | 15 | 13 | | 16 | 28 | | 17 | 1 | | 18 | 2 | | 19 | 8 | | 20 | 6 | | 21 | 20 | | 22 | 10 | | 23 | 9 | | 24 | 18 | | 25 | 7 | | 26 | 2 | | 27 | 7 | | 28 | 7 | | 29 | 11 | | 30 | 20 | | 31 | 9 | | 32 | 22 | | 33 | 3 | | 34 | 5 | | 35 | 5 | | 36 | 14 | | 37 | 4 | | 38 | 3 | | 39 | 7 | | 40 | 9 | | 41 | 11 | | 42 | 9 | | 43 | 22 | | 44 | 11 | | 45 | 5 | | 46 | 6 | | 47 | 3 | | 48 | 10 | | 49 | 14 |
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| 47.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3170731707317073 | | totalSentences | 123 | | uniqueOpeners | 39 | |
| 79.37% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 84 | | matches | | 0 | "Then graduation came, and Evan" | | 1 | "Instead she held his gaze" |
| | ratio | 0.024 | |
| 0.95% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 84 | | matches | | 0 | "She didn’t need to order." | | 1 | "He poured a fingers-width of" | | 2 | "She liked the quiet of" | | 3 | "He took a booth near" | | 4 | "Her phone buzzed with a" | | 5 | "She ignored it." | | 6 | "Her shift didn’t start for" | | 7 | "He shook rain from his" | | 8 | "She knew the face before" | | 9 | "He was broader across the" | | 10 | "She hadn’t heard that name" | | 11 | "They’d shared a study carrel" | | 12 | "He’d lent her his notes" | | 13 | "She’d bought him a pint" | | 14 | "She kept her voice flat," | | 15 | "He crossed the room, unbuttoning" | | 16 | "He settled on the stool" | | 17 | "She clinked hers against it," | | 18 | "He drank, set the glass" | | 19 | "She felt the weight of" |
| | ratio | 0.548 | |
| 13.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 84 | | matches | | 0 | "The rain had slicked Soho’s" | | 1 | "Silas nodded from behind the" | | 2 | "She didn’t need to order." | | 3 | "He poured a fingers-width of" | | 4 | "She liked the quiet of" | | 5 | "The door only opened twice" | | 6 | "He took a booth near" | | 7 | "Rory traced the rim of" | | 8 | "Her phone buzzed with a" | | 9 | "She ignored it." | | 10 | "Her shift didn’t start for" | | 11 | "The door swung open again," | | 12 | "A man in a charcoal" | | 13 | "He shook rain from his" | | 14 | "She knew the face before" | | 15 | "The jaw was sharper, the" | | 16 | "He was broader across the" | | 17 | "She hadn’t heard that name" | | 18 | "They’d shared a study carrel" | | 19 | "He’d lent her his notes" |
| | ratio | 0.893 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 1 | | matches | | 0 | "She wasn’t the girl who’d annotated case law with colour-coded tabs." |
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| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 44 | | tagDensity | 0.114 | | leniency | 0.227 | | rawRatio | 0 | | effectiveRatio | 0 | |