| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1426 | | 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) | |
| 89.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1426 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "flicked" | | 1 | "eyebrow" | | 2 | "silence" |
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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 | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 82 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 147 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 64 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1429 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 753 | | uniqueNames | 10 | | maxNameDensity | 2.79 | | worstName | "Rory" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | London | 1 | | Golden | 1 | | Empress | 1 | | Silas | 5 | | Rory | 21 | | Evan | 1 | | Eva | 18 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Empress" | | 3 | "Silas" | | 4 | "Rory" | | 5 | "Evan" | | 6 | "Eva" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Golden" |
| | globalScore | 0.106 | | windowScore | 0 | |
| 53.85% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like santal and something clean" | | 1 | "smelled like fried rice and bar soap" |
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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 | 1429 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 147 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 107 | | mean | 13.36 | | std | 13.23 | | cv | 0.991 | | sampleLengths | | 0 | 26 | | 1 | 42 | | 2 | 70 | | 3 | 12 | | 4 | 32 | | 5 | 14 | | 6 | 8 | | 7 | 5 | | 8 | 26 | | 9 | 4 | | 10 | 49 | | 11 | 31 | | 12 | 1 | | 13 | 6 | | 14 | 1 | | 15 | 19 | | 16 | 25 | | 17 | 28 | | 18 | 15 | | 19 | 7 | | 20 | 20 | | 21 | 3 | | 22 | 1 | | 23 | 10 | | 24 | 22 | | 25 | 6 | | 26 | 25 | | 27 | 8 | | 28 | 10 | | 29 | 7 | | 30 | 6 | | 31 | 16 | | 32 | 3 | | 33 | 33 | | 34 | 7 | | 35 | 4 | | 36 | 7 | | 37 | 2 | | 38 | 1 | | 39 | 10 | | 40 | 36 | | 41 | 10 | | 42 | 14 | | 43 | 6 | | 44 | 5 | | 45 | 21 | | 46 | 9 | | 47 | 32 | | 48 | 13 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 82 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 137 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 147 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 870 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.02528735632183908 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 147 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 147 | | mean | 9.72 | | std | 8.94 | | cv | 0.92 | | sampleLengths | | 0 | 14 | | 1 | 12 | | 2 | 12 | | 3 | 30 | | 4 | 9 | | 5 | 25 | | 6 | 11 | | 7 | 10 | | 8 | 15 | | 9 | 12 | | 10 | 11 | | 11 | 13 | | 12 | 8 | | 13 | 14 | | 14 | 5 | | 15 | 3 | | 16 | 5 | | 17 | 9 | | 18 | 12 | | 19 | 5 | | 20 | 4 | | 21 | 8 | | 22 | 17 | | 23 | 10 | | 24 | 14 | | 25 | 7 | | 26 | 24 | | 27 | 1 | | 28 | 3 | | 29 | 3 | | 30 | 1 | | 31 | 19 | | 32 | 3 | | 33 | 17 | | 34 | 5 | | 35 | 7 | | 36 | 21 | | 37 | 15 | | 38 | 7 | | 39 | 5 | | 40 | 7 | | 41 | 8 | | 42 | 3 | | 43 | 1 | | 44 | 5 | | 45 | 5 | | 46 | 22 | | 47 | 6 | | 48 | 25 | | 49 | 8 |
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| 55.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3469387755102041 | | totalSentences | 147 | | uniqueOpeners | 51 | |
| 42.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 78 | | matches | | 0 | "Then the woman pushed the" |
| | ratio | 0.013 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 78 | | matches | | 0 | "Her shift at Golden Empress" | | 1 | "Her shoulder-length black hair stuck" | | 2 | "He had his right hand" | | 3 | "His left leg took a" | | 4 | "Her mouth twitched." | | 5 | "His beard, grey-streaked auburn like" | | 6 | "He reached for a glass." | | 7 | "Her hair was blonde and" | | 8 | "Her bag was leather and" | | 9 | "Her hand dropped." | | 10 | "He gave Rory a look" | | 11 | "She smelled like santal and" | | 12 | "She leaned on the bar." | | 13 | "Her hands knew the work." | | 14 | "She slid the glass across." | | 15 | "Her nails were manicured, short," | | 16 | "She turned her wrist over" | | 17 | "She picked at a loose" | | 18 | "Her hand went to her" | | 19 | "Her eyes were wet." |
| | ratio | 0.269 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 78 | | matches | | 0 | "The green neon above The" | | 1 | "Rain had come and gone" | | 2 | "The walls wore their collection" | | 3 | "Aurora wiped down the far" | | 4 | "Her shift at Golden Empress" | | 5 | "Her shoulder-length black hair stuck" | | 6 | "The bright blue of her" | | 7 | "The door opened and brought" | | 8 | "Silas looked up from the" | | 9 | "He had his right hand" | | 10 | "His left leg took a" | | 11 | "Rory flicked water at him." | | 12 | "Her mouth twitched." | | 13 | "Silas eased himself onto a" | | 14 | "His beard, grey-streaked auburn like" | | 15 | "He reached for a glass." | | 16 | "The door opened again." | | 17 | "This time the bell above" | | 18 | "A woman stepped in and" | | 19 | "Her hair was blonde and" |
| | ratio | 0.923 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 78 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 1 | | matches | | 0 | "The expensive coat and the sharp haircut could not hide the girl who used to sleep on Rory's floor when her stepdad drank too much, who taught Rory how to smoke…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |