| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 72 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1664 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 72.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1664 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "lilt" | | 1 | "potential" | | 2 | "silence" | | 3 | "silk" | | 4 | "tension" | | 5 | "weight" | | 6 | "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 | 115 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 115 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 170 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 21 | | totalWords | 1664 | | ratio | 0.013 | | matches | | 0 | "I thought you were in Cardiff. I thought you were studying law. I thought you were going to be somebody." | | 1 | "Jess" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 75 | | wordCount | 1095 | | uniqueNames | 14 | | maxNameDensity | 2.83 | | worstName | "Jess" | | maxWindowNameDensity | 5 | | worstWindowName | "Jess" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Burberry | 2 | | Morgan | 2 | | Jess | 31 | | Aurora | 1 | | Rory | 29 | | Cardiff | 2 | | Glenlivet | 1 | | London | 1 | | Evan | 1 | | Pupil | 1 | | Barrister | 1 | | Chambers | 1 |
| | persons | | 0 | "Raven" | | 1 | "Morgan" | | 2 | "Jess" | | 3 | "Rory" | | 4 | "Evan" | | 5 | "Chambers" |
| | places | | 0 | "Burberry" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0.084 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | 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 | 1664 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 170 | | matches | | 0 | "Hated that she" | | 1 | "meant that this" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 84 | | mean | 19.81 | | std | 16.75 | | cv | 0.846 | | sampleLengths | | 0 | 76 | | 1 | 13 | | 2 | 3 | | 3 | 24 | | 4 | 2 | | 5 | 57 | | 6 | 2 | | 7 | 28 | | 8 | 32 | | 9 | 1 | | 10 | 30 | | 11 | 8 | | 12 | 35 | | 13 | 32 | | 14 | 10 | | 15 | 39 | | 16 | 21 | | 17 | 40 | | 18 | 10 | | 19 | 5 | | 20 | 17 | | 21 | 18 | | 22 | 19 | | 23 | 2 | | 24 | 25 | | 25 | 4 | | 26 | 11 | | 27 | 2 | | 28 | 19 | | 29 | 17 | | 30 | 70 | | 31 | 10 | | 32 | 4 | | 33 | 7 | | 34 | 32 | | 35 | 4 | | 36 | 3 | | 37 | 24 | | 38 | 15 | | 39 | 7 | | 40 | 19 | | 41 | 12 | | 42 | 1 | | 43 | 13 | | 44 | 35 | | 45 | 47 | | 46 | 6 | | 47 | 40 | | 48 | 2 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 195 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 170 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 980 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.02959183673469388 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0020408163265306124 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 170 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 170 | | mean | 9.79 | | std | 7.65 | | cv | 0.781 | | sampleLengths | | 0 | 22 | | 1 | 21 | | 2 | 4 | | 3 | 29 | | 4 | 4 | | 5 | 9 | | 6 | 3 | | 7 | 8 | | 8 | 12 | | 9 | 4 | | 10 | 2 | | 11 | 12 | | 12 | 12 | | 13 | 7 | | 14 | 5 | | 15 | 21 | | 16 | 2 | | 17 | 3 | | 18 | 9 | | 19 | 16 | | 20 | 5 | | 21 | 12 | | 22 | 10 | | 23 | 5 | | 24 | 1 | | 25 | 9 | | 26 | 6 | | 27 | 4 | | 28 | 11 | | 29 | 6 | | 30 | 2 | | 31 | 9 | | 32 | 26 | | 33 | 11 | | 34 | 21 | | 35 | 10 | | 36 | 13 | | 37 | 6 | | 38 | 6 | | 39 | 6 | | 40 | 8 | | 41 | 14 | | 42 | 7 | | 43 | 5 | | 44 | 19 | | 45 | 7 | | 46 | 5 | | 47 | 4 | | 48 | 5 | | 49 | 5 |
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| 51.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.34705882352941175 | | totalSentences | 170 | | uniqueOpeners | 59 | |
| 93.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 107 | | matches | | 0 | "Then the tilt of the" | | 1 | "Then the widening of them." | | 2 | "Instead, she said," |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 107 | | matches | | 0 | "She'd filled out since university," | | 1 | "She thought about ducking, about" | | 2 | "She was Rory here." | | 3 | "She was no one's daughter," | | 4 | "She set down the cloth" | | 5 | "She stopped two feet from" | | 6 | "*I thought you were in" | | 7 | "I thought you were studying" | | 8 | "I thought you were going" | | 9 | "She knew the order from" | | 10 | "They'd snuck a bottle of" | | 11 | "She hadn't said his name" | | 12 | "She'd trained herself not to" | | 13 | "She felt the weight of" | | 14 | "She set down the glass" | | 15 | "She pulled out a card" | | 16 | "She pushed through the door" | | 17 | "He picked up Jess's abandoned" | | 18 | "He just nodded, the way" | | 19 | "She reached for another glass." |
| | ratio | 0.196 | |
| 25.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 93 | | totalSentences | 107 | | matches | | 0 | "The green neon of the" | | 1 | "Rory worked a chamois cloth" | | 2 | "Tuesday nights were dead." | | 3 | "The door swung open." | | 4 | "Rory didn't look up, just" | | 5 | "The woman's voice caught something" | | 6 | "A lilt, a rhythm, a" | | 7 | "Rory's hand stopped mid-reach." | | 8 | "The woman stood in the" | | 9 | "A silver briefcase, the kind" | | 10 | "She'd filled out since university," | | 11 | "Rory's throat tightened." | | 12 | "She thought about ducking, about" | | 13 | "The recognition came in stages." | | 14 | "The name landed like a" | | 15 | "Rory hadn't been Aurora in" | | 16 | "She was Rory here." | | 17 | "She was no one's daughter," | | 18 | "She set down the cloth" | | 19 | "Jess crossed the room, heels" |
| | ratio | 0.869 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 107 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 2 | | matches | | 0 | "She'd filled out since university, her face losing the softness of twenty-two and settling into the sharper architecture of approaching thirty." | | 1 | "Rory watched Jess's face cycle through confusion, concern, and that particular brand of pity reserved for people who'd wasted potential." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 72 | | tagDensity | 0.056 | | leniency | 0.111 | | rawRatio | 0.25 | | effectiveRatio | 0.028 | |