| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 33 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 114 | | tagDensity | 0.289 | | leniency | 0.579 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.73% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2340 | | totalAiIsmAdverbs | 2 | | 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) | |
| 95.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2340 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 158 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 158 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 239 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2340 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 30 | | unquotedAttributions | 1 | | matches | | 0 | "He had begun leaving soup downstairs at closing time, always too much for himself, he said." |
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| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 147 | | wordCount | 1679 | | uniqueNames | 9 | | maxNameDensity | 3.81 | | worstName | "Rory" | | maxWindowNameDensity | 6.5 | | worstWindowName | "Rory" | | discoveredNames | | Silas | 14 | | Soho | 1 | | Rory | 64 | | Cardiff | 2 | | Eva | 59 | | London | 3 | | Brendan | 1 | | Jennifer | 2 | | Yu-Fei | 1 |
| | persons | | 0 | "Silas" | | 1 | "Rory" | | 2 | "Eva" | | 3 | "Brendan" | | 4 | "Jennifer" | | 5 | "Yu-Fei" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 118 | | 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 | 2340 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 239 | | matches | | 0 | "winding that hair" | | 1 | "snapped that she" | | 2 | "ask that the" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 126 | | mean | 18.57 | | std | 21.32 | | cv | 1.148 | | sampleLengths | | 0 | 8 | | 1 | 93 | | 2 | 31 | | 3 | 32 | | 4 | 3 | | 5 | 101 | | 6 | 1 | | 7 | 16 | | 8 | 9 | | 9 | 33 | | 10 | 37 | | 11 | 5 | | 12 | 2 | | 13 | 47 | | 14 | 5 | | 15 | 7 | | 16 | 3 | | 17 | 7 | | 18 | 89 | | 19 | 15 | | 20 | 3 | | 21 | 8 | | 22 | 10 | | 23 | 1 | | 24 | 42 | | 25 | 10 | | 26 | 11 | | 27 | 15 | | 28 | 8 | | 29 | 8 | | 30 | 22 | | 31 | 2 | | 32 | 51 | | 33 | 8 | | 34 | 8 | | 35 | 2 | | 36 | 18 | | 37 | 7 | | 38 | 18 | | 39 | 18 | | 40 | 2 | | 41 | 6 | | 42 | 76 | | 43 | 7 | | 44 | 3 | | 45 | 4 | | 46 | 11 | | 47 | 2 | | 48 | 7 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 158 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 323 | | matches | | 0 | "was turning" | | 1 | "was staying" | | 2 | "was waiting" | | 3 | "was asking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 239 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1680 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.029166666666666667 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.004761904761904762 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 239 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 239 | | mean | 9.79 | | std | 6.85 | | cv | 0.699 | | sampleLengths | | 0 | 8 | | 1 | 23 | | 2 | 14 | | 3 | 34 | | 4 | 22 | | 5 | 15 | | 6 | 16 | | 7 | 7 | | 8 | 25 | | 9 | 3 | | 10 | 8 | | 11 | 32 | | 12 | 12 | | 13 | 17 | | 14 | 32 | | 15 | 1 | | 16 | 12 | | 17 | 4 | | 18 | 4 | | 19 | 5 | | 20 | 12 | | 21 | 5 | | 22 | 16 | | 23 | 6 | | 24 | 14 | | 25 | 6 | | 26 | 11 | | 27 | 5 | | 28 | 2 | | 29 | 20 | | 30 | 9 | | 31 | 11 | | 32 | 7 | | 33 | 5 | | 34 | 5 | | 35 | 2 | | 36 | 3 | | 37 | 7 | | 38 | 13 | | 39 | 23 | | 40 | 20 | | 41 | 21 | | 42 | 12 | | 43 | 8 | | 44 | 7 | | 45 | 3 | | 46 | 8 | | 47 | 4 | | 48 | 6 | | 49 | 1 |
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| 44.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.26359832635983266 | | totalSentences | 239 | | uniqueOpeners | 63 | |
| 98.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 136 | | matches | | 0 | "Then Eva turned fully towards" | | 1 | "Instead she rested her palms" | | 2 | "Then she saw how carefully" | | 3 | "Then she started down the" |
| | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 136 | | matches | | 0 | "She meant to take one," | | 1 | "She was turning it in" | | 2 | "Her hair, once long and" | | 3 | "She wore a charcoal coat" | | 4 | "It held water, half gone." | | 5 | "His eyes moved from Eva" | | 6 | "She gave him a small" | | 7 | "It was such an ordinary" | | 8 | "She had been avoiding the" | | 9 | "Their lives had seemed close" | | 10 | "She picked up the lemon" | | 11 | "He looked at Eva" | | 12 | "He held out his right" | | 13 | "His silver signet ring caught" | | 14 | "He knew she was the" | | 15 | "He went to serve someone" | | 16 | "She could make tea later." | | 17 | "She had spent the first" | | 18 | "She caught it with her" | | 19 | "She pulled the cuff down," |
| | ratio | 0.272 | |
| 33.53% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 116 | | totalSentences | 136 | | matches | | 0 | "Rory had come downstairs to" | | 1 | "She meant to take one," | | 2 | "Rory waited by the end" | | 3 | "The green neon sign outside" | | 4 | "Each time the door opened," | | 5 | "Rory took a lemon from" | | 6 | "She was turning it in" | | 7 | "Rory looked up." | | 8 | "Eva stood with one elbow" | | 9 | "Her hair, once long and" | | 10 | "She wore a charcoal coat" | | 11 | "Eva’s gaze dropped to the" | | 12 | "Rory set it down." | | 13 | "Eva glanced at the glass" | | 14 | "It held water, half gone." | | 15 | "Silas looked over from the" | | 16 | "His eyes moved from Eva" | | 17 | "She gave him a small" | | 18 | "Whatever he made of it," | | 19 | "It was such an ordinary" |
| | ratio | 0.853 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 136 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 74 | | technicalSentenceCount | 2 | | matches | | 0 | "For a moment Rory could see the girl who had sat next to her in classrooms in Cardiff, pressing her thumb into every orange at lunch until she found one that ga…" | | 1 | "Rory remembered a vending machine that ate Eva’s coins, the departure board changing while they watched, Eva offering to call Brendan and Jennifer when Rory rea…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 33 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 20 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 114 | | tagDensity | 0.175 | | leniency | 0.351 | | rawRatio | 0.05 | | effectiveRatio | 0.018 | |