| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.51% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 589 | | 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) | |
| 40.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 589 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "traced" | | 1 | "silence" | | 2 | "warmth" | | 3 | "flickered" | | 4 | "trembled" | | 5 | "unspoken" |
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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 | 2 | | narrationSentences | 111 | | matches | | 0 | "o with hope" | | 1 | "was scared" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 111 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 16 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 586 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.61% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 586 | | uniqueNames | 9 | | maxNameDensity | 2.05 | | worstName | "Aurora" | | maxWindowNameDensity | 3 | | worstWindowName | "Aurora" | | discoveredNames | | Carter | 1 | | Aurora | 12 | | Richmond | 2 | | Cardiff | 2 | | Eva | 10 | | London | 1 | | Silas | 4 | | Rory | 3 | | You | 3 |
| | persons | | 0 | "Carter" | | 1 | "Aurora" | | 2 | "Eva" | | 3 | "Silas" | | 4 | "Rory" | | 5 | "You" |
| | places | | 0 | "Richmond" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0.476 | | windowScore | 0.667 | |
| 94.44% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like someone she used to know" |
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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 | 586 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 111 | | matches | | 0 | "replaying that silence" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 9.3 | | std | 7.14 | | cv | 0.768 | | sampleLengths | | 0 | 34 | | 1 | 17 | | 2 | 10 | | 3 | 1 | | 4 | 1 | | 5 | 12 | | 6 | 11 | | 7 | 32 | | 8 | 2 | | 9 | 2 | | 10 | 12 | | 11 | 1 | | 12 | 13 | | 13 | 4 | | 14 | 14 | | 15 | 9 | | 16 | 11 | | 17 | 7 | | 18 | 8 | | 19 | 12 | | 20 | 11 | | 21 | 3 | | 22 | 3 | | 23 | 8 | | 24 | 7 | | 25 | 11 | | 26 | 6 | | 27 | 23 | | 28 | 9 | | 29 | 3 | | 30 | 1 | | 31 | 10 | | 32 | 4 | | 33 | 3 | | 34 | 8 | | 35 | 3 | | 36 | 7 | | 37 | 5 | | 38 | 3 | | 39 | 9 | | 40 | 18 | | 41 | 13 | | 42 | 16 | | 43 | 13 | | 44 | 5 | | 45 | 3 | | 46 | 10 | | 47 | 15 | | 48 | 3 | | 49 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 111 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 124 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 111 | | ratio | 0.009 | | matches | | 0 | "Every promotion, every milestone—I missed your cheers." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 589 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.027164685908319185 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0050933786078098476 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 5.28 | | std | 3.13 | | cv | 0.593 | | sampleLengths | | 0 | 7 | | 1 | 13 | | 2 | 4 | | 3 | 10 | | 4 | 8 | | 5 | 9 | | 6 | 7 | | 7 | 3 | | 8 | 1 | | 9 | 1 | | 10 | 9 | | 11 | 3 | | 12 | 11 | | 13 | 10 | | 14 | 8 | | 15 | 2 | | 16 | 3 | | 17 | 9 | | 18 | 2 | | 19 | 2 | | 20 | 3 | | 21 | 6 | | 22 | 3 | | 23 | 1 | | 24 | 3 | | 25 | 10 | | 26 | 4 | | 27 | 14 | | 28 | 4 | | 29 | 2 | | 30 | 3 | | 31 | 4 | | 32 | 1 | | 33 | 6 | | 34 | 7 | | 35 | 8 | | 36 | 3 | | 37 | 2 | | 38 | 7 | | 39 | 3 | | 40 | 8 | | 41 | 3 | | 42 | 3 | | 43 | 4 | | 44 | 4 | | 45 | 7 | | 46 | 9 | | 47 | 2 | | 48 | 6 | | 49 | 3 |
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| 78.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4954954954954955 | | totalSentences | 111 | | uniqueOpeners | 55 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 97 | | matches | | 0 | "Then you vanished." | | 1 | "Maybe it didn’t." | | 2 | "Then let’s start now." |
| | ratio | 0.031 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 97 | | matches | | 0 | "His limp made the stool" | | 1 | "She twisted her wrist, felt" | | 2 | "Her eyes flared." | | 3 | "You here on business?" | | 4 | "She sank onto the stool" | | 5 | "I moved to Richmond." | | 6 | "I hear Cardiff suitcases emptied" | | 7 | "I waited an hour for" | | 8 | "I lost signal." | | 9 | "I didn’t laugh." | | 10 | "I blamed you." | | 11 | "She exhaled, cupped the mug." | | 12 | "I wrote you once." | | 13 | "Your handwriting, though." | | 14 | "I burned it." | | 15 | "I couldn’t face you." | | 16 | "You burned it?" | | 17 | "I spent years replaying that" | | 18 | "I was scared." | | 19 | "I missed you." |
| | ratio | 0.258 | |
| 78.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 97 | | matches | | 0 | "Neon green light stained the" | | 1 | "Aurora held her cycle lock" | | 2 | "A bell ticked once." | | 3 | "Silas crouched behind the bar," | | 4 | "His limp made the stool" | | 5 | "Aurora claimed it." | | 6 | "Silas set a chipped teapot" | | 7 | "Steam coiled upward." | | 8 | "She twisted her wrist, felt" | | 9 | "A shift in voices by" | | 10 | "A woman stood alone, back" | | 11 | "Hair cropped close." | | 12 | "The silhouette looked like someone" | | 13 | "The woman turned." | | 14 | "Her eyes flared." | | 15 | "Time slid backward." | | 16 | "Dust motes danced in the" | | 17 | "You here on business?" | | 18 | "She sank onto the stool" | | 19 | "I moved to Richmond." |
| | ratio | 0.763 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 97 | | matches | | 0 | "By the time I got" | | 1 | "Because I thought you’d tell" | | 2 | "Either would’ve made me feel" | | 3 | "Because I wasn’t sure what" |
| | ratio | 0.041 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 10 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |