| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1659 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 63.83% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1659 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "pulsed" | | 1 | "trembled" | | 2 | "warmth" | | 3 | "echo" | | 4 | "flickered" | | 5 | "perfect" | | 6 | "weight" |
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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 | 221 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 4 | | narrationSentences | 221 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 227 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1659 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 64.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 1631 | | uniqueNames | 9 | | maxNameDensity | 1.72 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Carter | 1 | | Fae | 1 | | Grove | 4 | | Yu-Fei | 1 | | Silas | 1 | | Richmond | 1 | | Park | 1 | | Isolde | 1 | | Aurora | 28 |
| | persons | | 0 | "Carter" | | 1 | "Silas" | | 2 | "Isolde" | | 3 | "Aurora" |
| | places | | 0 | "Fae" | | 1 | "Grove" | | 2 | "Richmond" | | 3 | "Park" |
| | globalScore | 0.642 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 133 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared over her shoulder" |
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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 | 1659 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 227 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 134 | | mean | 12.38 | | std | 13.42 | | cv | 1.084 | | sampleLengths | | 0 | 12 | | 1 | 46 | | 2 | 12 | | 3 | 29 | | 4 | 3 | | 5 | 15 | | 6 | 8 | | 7 | 10 | | 8 | 54 | | 9 | 5 | | 10 | 3 | | 11 | 41 | | 12 | 31 | | 13 | 4 | | 14 | 5 | | 15 | 3 | | 16 | 44 | | 17 | 6 | | 18 | 3 | | 19 | 4 | | 20 | 20 | | 21 | 34 | | 22 | 6 | | 23 | 6 | | 24 | 2 | | 25 | 26 | | 26 | 6 | | 27 | 5 | | 28 | 65 | | 29 | 28 | | 30 | 8 | | 31 | 22 | | 32 | 7 | | 33 | 8 | | 34 | 2 | | 35 | 13 | | 36 | 5 | | 37 | 1 | | 38 | 1 | | 39 | 12 | | 40 | 1 | | 41 | 3 | | 42 | 24 | | 43 | 3 | | 44 | 45 | | 45 | 10 | | 46 | 8 | | 47 | 17 | | 48 | 2 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 221 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 268 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 227 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1635 | | adjectiveStacks | 1 | | stackExamples | | 0 | "White star-shaped blooms" |
| | adverbCount | 42 | | adverbRatio | 0.025688073394495414 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0012232415902140672 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 227 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 227 | | mean | 7.31 | | std | 4.52 | | cv | 0.619 | | sampleLengths | | 0 | 12 | | 1 | 8 | | 2 | 18 | | 3 | 14 | | 4 | 6 | | 5 | 12 | | 6 | 10 | | 7 | 9 | | 8 | 10 | | 9 | 2 | | 10 | 1 | | 11 | 9 | | 12 | 2 | | 13 | 4 | | 14 | 3 | | 15 | 5 | | 16 | 10 | | 17 | 6 | | 18 | 12 | | 19 | 12 | | 20 | 24 | | 21 | 5 | | 22 | 3 | | 23 | 4 | | 24 | 16 | | 25 | 6 | | 26 | 15 | | 27 | 9 | | 28 | 4 | | 29 | 7 | | 30 | 11 | | 31 | 4 | | 32 | 5 | | 33 | 3 | | 34 | 18 | | 35 | 9 | | 36 | 6 | | 37 | 5 | | 38 | 3 | | 39 | 3 | | 40 | 6 | | 41 | 3 | | 42 | 4 | | 43 | 6 | | 44 | 5 | | 45 | 9 | | 46 | 5 | | 47 | 8 | | 48 | 10 | | 49 | 11 |
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| 39.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 24 | | diversityRatio | 0.2345132743362832 | | totalSentences | 226 | | uniqueOpeners | 53 | |
| 16.84% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 198 | | matches | | 0 | "Then the screen returned to" |
| | ratio | 0.005 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 56 | | totalSentences | 198 | | matches | | 0 | "Its orange light clung to" | | 1 | "She touched the deep crimson" | | 2 | "It had pulsed twice since" | | 3 | "She drew out her phone" | | 4 | "She had promised Yu-Fei she" | | 5 | "She had promised Silas she" | | 6 | "She had promised herself she" | | 7 | "She stepped through the stones." | | 8 | "It didn’t grow colder." | | 9 | "She spun around." | | 10 | "Her own breath seemed too" | | 11 | "She pulled out her phone" | | 12 | "She turned back towards the" | | 13 | "It passed at the edge" | | 14 | "Her hand tightened around the" | | 15 | "She had come to the" | | 16 | "She had found a photograph" | | 17 | "She took a step towards" | | 18 | "It faded, then returned from" | | 19 | "She glanced at her phone." |
| | ratio | 0.283 | |
| 48.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 163 | | totalSentences | 198 | | matches | | 0 | "Aurora Carter crossed between the" | | 1 | "The last streetlamp had vanished" | | 2 | "Its orange light clung to" | | 3 | "Aurora paused with one boot" | | 4 | "The pendant rested against her" | | 5 | "She touched the deep crimson" | | 6 | "It had pulsed twice since" | | 7 | "She drew out her phone" | | 8 | "The time read 12:14." | | 9 | "The phone showed the same" | | 10 | "Aurora slipped it into her" | | 11 | "She had promised Yu-Fei she" | | 12 | "She had promised Silas she" | | 13 | "She had promised herself she" | | 14 | "She stepped through the stones." | | 15 | "The air changed." | | 16 | "It didn’t grow colder." | | 17 | "The damp smell of Richmond" | | 18 | "The Grove smelled of flowering" | | 19 | "Wildflowers spread between the roots" |
| | ratio | 0.823 | |
| 25.25% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 198 | | matches | | 0 | "Now she stood inside the" |
| | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 1 | | matches | | 0 | "She remembered falling against a broken fence as a child, her mother pressing a tea towel to the cut while her father shouted for the neighbour." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 3 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she whispered (whisper)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.75 | | effectiveRatio | 0.6 | |