| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 39 | | tagDensity | 0.205 | | leniency | 0.41 | | rawRatio | 0.125 | | effectiveRatio | 0.051 | |
| 94.45% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 901 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 901 | | totalAiIsms | 24 | | found | | | highlights | | 0 | "weight" | | 1 | "charged" | | 2 | "measured" | | 3 | "silence" | | 4 | "flicked" | | 5 | "glinting" | | 6 | "unspoken" | | 7 | "tension" | | 8 | "pulsed" | | 9 | "electric" | | 10 | "whisper" |
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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 | 76 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 76 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 40 | | markdownWords | 182 | | totalWords | 894 | | ratio | 0.204 | | matches | | 0 | "\"You shouldn’t have come.\"" | | 1 | "\"I didn’t have a choice,\"" | | 2 | "\"You showed up at my flat. Again.\"" | | 3 | "\"I didn’t mean to scare you.\"" | | 4 | "\"You did.\"" | | 5 | "\"You always do.\"" | | 6 | "\"You’re still here.\"" | | 7 | "\"I’m not running.\"" | | 8 | "\"Good.\"" | | 9 | "\"Oh, for heaven’s sake, if you two are going to stand there like a couple of teenagers, I’ll just—\"" | | 10 | "\"You know what they say about ghosts,\"" | | 11 | "\"they don’t go away.\"" | | 12 | "\"No, they don’t.\"" | | 13 | "\"You’re not a ghost.\"" | | 14 | "\"No.\"" | | 15 | "\"But you are.\"" | | 16 | "\"You’re not afraid of me.\"" | | 17 | "\"I’m afraid of what I see.\"" | | 18 | "\"Then you’re not the same person who left.\"" | | 19 | "\"I’m not.\"" | | 20 | "\"Well, I’m starving. You two look like you could use a bite to eat.\"" | | 21 | "\"I’ll get it.\"" | | 22 | "\"You’re not leaving,\"" | | 23 | "\"I’m not.\"" | | 24 | "\"Well, if you’re not going, I am. I’ve got a date with a very particular kind of curry.\"" | | 25 | "\"You’re staying.\"" | | 26 | "\"You’re not running,\"" | | 27 | "\"No.\"" | | 28 | "\"I’m not.\"" | | 29 | "\"You’re not a ghost.\"" | | 30 | "\"No.\"" | | 31 | "\"You’re not afraid.\"" | | 32 | "\"I’m afraid of what I see.\"" | | 33 | "\"Of what I’m becoming.\"" | | 34 | "\"Then you’re not the same person who left.\"" | | 35 | "\"I’m not.\"" | | 36 | "\"You’re not a ghost.\"" | | 37 | "\"I’m not.\"" | | 38 | "\"You’re staying.\"" | | 39 | "\"I am.\"" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 37.64% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 712 | | uniqueNames | 5 | | maxNameDensity | 2.25 | | worstName | "Lucien" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 8 | | Carter | 1 | | Lucien | 16 | | Moreau | 1 | | Aurora | 14 |
| | persons | | 0 | "Eva" | | 1 | "Carter" | | 2 | "Lucien" | | 3 | "Moreau" | | 4 | "Aurora" |
| | places | (empty) | | globalScore | 0.376 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | 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 | 894 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 20.32 | | std | 14.04 | | cv | 0.691 | | sampleLengths | | 0 | 57 | | 1 | 29 | | 2 | 39 | | 3 | 13 | | 4 | 60 | | 5 | 30 | | 6 | 12 | | 7 | 16 | | 8 | 24 | | 9 | 30 | | 10 | 6 | | 11 | 46 | | 12 | 43 | | 13 | 14 | | 14 | 24 | | 15 | 19 | | 16 | 14 | | 17 | 16 | | 18 | 23 | | 19 | 11 | | 20 | 7 | | 21 | 34 | | 22 | 9 | | 23 | 28 | | 24 | 5 | | 25 | 5 | | 26 | 32 | | 27 | 17 | | 28 | 7 | | 29 | 34 | | 30 | 6 | | 31 | 7 | | 32 | 36 | | 33 | 4 | | 34 | 18 | | 35 | 15 | | 36 | 11 | | 37 | 7 | | 38 | 33 | | 39 | 9 | | 40 | 6 | | 41 | 14 | | 42 | 11 | | 43 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 76 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 120 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 106 | | ratio | 0.057 | | matches | | 0 | "Aurora exhaled, rubbing her wrist where the scar from her childhood accident still stung—just a little." | | 1 | "The door had opened before she could react, and there he stood—Lucien Moreau, his cane resting against the wall, his suit immaculate despite the way the streetlight slanted across his face." | | 2 | "Aurora glanced around the flat—bookshelves sagging under the weight of scrolls, the scent of old parchment and something else, something sharper, like the edge of a blade." | | 3 | "Then, from the other side of the room, a voice—Eva’s, sharp and amused." | | 4 | "The weight of his stare, the unspoken tension, the way his fingers flexed near the cane—it all settled over her like a shroud." | | 5 | "The weight of his stare, the unspoken tension, the way his fingers flexed near the cane—it all settled over her like a shroud." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 719 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.031988873435326845 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 8.43 | | std | 6.76 | | cv | 0.801 | | sampleLengths | | 0 | 22 | | 1 | 24 | | 2 | 11 | | 3 | 23 | | 4 | 3 | | 5 | 3 | | 6 | 16 | | 7 | 23 | | 8 | 9 | | 9 | 4 | | 10 | 2 | | 11 | 31 | | 12 | 27 | | 13 | 12 | | 14 | 11 | | 15 | 7 | | 16 | 6 | | 17 | 6 | | 18 | 13 | | 19 | 3 | | 20 | 12 | | 21 | 9 | | 22 | 3 | | 23 | 27 | | 24 | 3 | | 25 | 5 | | 26 | 1 | | 27 | 2 | | 28 | 12 | | 29 | 13 | | 30 | 19 | | 31 | 8 | | 32 | 22 | | 33 | 13 | | 34 | 11 | | 35 | 3 | | 36 | 6 | | 37 | 4 | | 38 | 10 | | 39 | 4 | | 40 | 15 | | 41 | 4 | | 42 | 11 | | 43 | 3 | | 44 | 5 | | 45 | 6 | | 46 | 5 | | 47 | 15 | | 48 | 4 | | 49 | 4 |
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| 47.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.18867924528301888 | | totalSentences | 106 | | uniqueOpeners | 20 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 71 | | matches | | 0 | "Then, from the other side" | | 1 | "Then, from the other room," | | 2 | "Then, from the corner of" |
| | ratio | 0.042 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 71 | | matches | | 0 | "He didn’t rise." | | 1 | "She hadn’t thought about it" | | 2 | "His amber and black eyes" | | 3 | "she said, voice rougher than" | | 4 | "She stepped forward, fingers brushing" | | 5 | "She crossed her arms, the" | | 6 | "She moved before Lucien could" | | 7 | "She met his gaze, the" | | 8 | "She just watched him, the" | | 9 | "he said again" | | 10 | "She met his gaze" | | 11 | "Her voice was low, rough" |
| | ratio | 0.169 | |
| 2.25% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 71 | | matches | | 0 | "The front door of Eva’s" | | 1 | "Aurora Carter stepped inside, her" | | 2 | "The moment the door clicked" | | 3 | "Ptolemy, the tabby cat, hissed" | | 4 | "He didn’t rise." | | 5 | "Aurora exhaled, rubbing her wrist" | | 6 | "She hadn’t thought about it" | | 7 | "A voice, smooth and low," | | 8 | "The door had opened before" | | 9 | "His amber and black eyes" | | 10 | "she said, voice rougher than" | | 11 | "She stepped forward, fingers brushing" | | 12 | "Lucien’s lips quirked, just a" | | 13 | "She crossed her arms, the" | | 14 | "The air between them thickened," | | 15 | "Lucien’s cane tapped against the" | | 16 | "Aurora glanced around the flat—bookshelves" | | 17 | "Lucien exhaled, slow and measured." | | 18 | "The kind of silence that" | | 19 | "Aurora didn’t wait to hear" |
| | ratio | 0.915 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 71 | | matches | | 0 | "Before she could think." | | 1 | "Before she could lie." |
| | ratio | 0.028 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 1 | | matches | | 0 | "The kind of silence that hung between them like a half-remembered song." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 3 | | matches | | 0 | "she said, voice rougher than she intended" | | 1 | "She crossed, the scar’s memory sharp beneath her fingers" | | 2 | "She met, the weight of it pressing down" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 39 | | tagDensity | 0.103 | | leniency | 0.205 | | rawRatio | 0.25 | | effectiveRatio | 0.051 | |