| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "she asked instead [instead]" |
| | dialogueSentences | 46 | | tagDensity | 0.326 | | leniency | 0.652 | | rawRatio | 0.067 | | effectiveRatio | 0.043 | |
| 76.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1271 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slightly" | | 1 | "quickly" | | 2 | "slowly" | | 3 | "really" |
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| 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) | |
| 37.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1271 | | totalAiIsms | 16 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | word | "carried the weight" | | count | 1 |
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| | highlights | | 0 | "flickered" | | 1 | "glinting" | | 2 | "warmth" | | 3 | "flicked" | | 4 | "traced" | | 5 | "silence" | | 6 | "weight" | | 7 | "familiar" | | 8 | "stomach" | | 9 | "unreadable" | | 10 | "carried the weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 85 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 85 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 31 | | totalWords | 1263 | | ratio | 0.025 | | matches | | 0 | "Talk or don’t, but you’re not leaving until you’ve got a plan." | | 1 | "Drink up. Then tell me what you need." | | 2 | "I need a new knee." | | 3 | "Then get one," | | 4 | "But first, drink." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 1020 | | uniqueNames | 8 | | maxNameDensity | 0.88 | | worstName | "Silas" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Silas | 9 | | Yu | 1 | | Fei | 1 | | Cardiff | 2 | | Evan | 2 | | London | 2 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Fei" | | 4 | "Evan" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like an anchor, something solid in" |
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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 | 1263 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 25.78 | | std | 20.79 | | cv | 0.807 | | sampleLengths | | 0 | 68 | | 1 | 63 | | 2 | 22 | | 3 | 18 | | 4 | 16 | | 5 | 68 | | 6 | 27 | | 7 | 4 | | 8 | 20 | | 9 | 15 | | 10 | 55 | | 11 | 4 | | 12 | 11 | | 13 | 21 | | 14 | 3 | | 15 | 1 | | 16 | 56 | | 17 | 8 | | 18 | 27 | | 19 | 43 | | 20 | 11 | | 21 | 16 | | 22 | 1 | | 23 | 13 | | 24 | 28 | | 25 | 58 | | 26 | 10 | | 27 | 20 | | 28 | 69 | | 29 | 32 | | 30 | 2 | | 31 | 18 | | 32 | 14 | | 33 | 21 | | 34 | 5 | | 35 | 51 | | 36 | 60 | | 37 | 19 | | 38 | 12 | | 39 | 8 | | 40 | 19 | | 41 | 24 | | 42 | 38 | | 43 | 13 | | 44 | 6 | | 45 | 67 | | 46 | 9 | | 47 | 20 | | 48 | 49 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 87.01% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 177 | | matches | | 0 | "was wiping" | | 1 | "was cataloging" | | 2 | "weren’t saying" |
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| 68.97% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 116 | | ratio | 0.026 | | matches | | 0 | "The delivery had taken longer than usual—Yu-Fei’s cousin had insisted on showing her the new wok, as if she hadn’t seen one before—and by the time she’d locked her bike out front, the neon sign above the door had already flickered to life." | | 1 | "She could see it—the train tickets, the familiar streets, the way her mother’s voice would crack when she answered the door." | | 2 | "But she’d recognized the look in his eyes—the one that said he’d seen too much and wasn’t sure he wanted to see anything else." |
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| 94.92% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1026 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.04580896686159844 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.00682261208576998 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 10.89 | | std | 8.62 | | cv | 0.791 | | sampleLengths | | 0 | 23 | | 1 | 29 | | 2 | 16 | | 3 | 6 | | 4 | 43 | | 5 | 14 | | 6 | 14 | | 7 | 6 | | 8 | 2 | | 9 | 15 | | 10 | 3 | | 11 | 9 | | 12 | 7 | | 13 | 19 | | 14 | 19 | | 15 | 3 | | 16 | 27 | | 17 | 5 | | 18 | 19 | | 19 | 3 | | 20 | 4 | | 21 | 18 | | 22 | 2 | | 23 | 11 | | 24 | 4 | | 25 | 3 | | 26 | 27 | | 27 | 25 | | 28 | 4 | | 29 | 7 | | 30 | 4 | | 31 | 12 | | 32 | 9 | | 33 | 3 | | 34 | 1 | | 35 | 4 | | 36 | 8 | | 37 | 26 | | 38 | 18 | | 39 | 8 | | 40 | 11 | | 41 | 16 | | 42 | 4 | | 43 | 23 | | 44 | 16 | | 45 | 6 | | 46 | 5 | | 47 | 6 | | 48 | 9 | | 49 | 1 |
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| 58.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.35344827586206895 | | totalSentences | 116 | | uniqueOpeners | 41 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 80 | | matches | | 0 | "Just studied her, the way" | | 1 | "Just nodded, like he’d expected" | | 2 | "Then, slowly, he reached into" | | 3 | "Then tell me what you" | | 4 | "*Then get one,* she’d said." | | 5 | "Then he reached for the" |
| | ratio | 0.075 | |
| 40.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 80 | | matches | | 0 | "She hadn’t meant to come" | | 1 | "He didn’t look up right" | | 2 | "She took a sip, the" | | 3 | "She swirled the glass, watching" | | 4 | "She should’ve left." | | 5 | "His hazel eyes flicked over" | | 6 | "He tilted his head, the" | | 7 | "She laughed, but it came" | | 8 | "He didn’t smile." | | 9 | "He tapped his knee, the" | | 10 | "She should’ve asked about the" | | 11 | "she asked instead" | | 12 | "He poured himself a finger" | | 13 | "She wanted to argue." | | 14 | "She wrapped her fingers around" | | 15 | "She exhaled, sharp" | | 16 | "He leaned forward, just slightly," | | 17 | "She could see it—the train" | | 18 | "she said, too quickly" | | 19 | "She opened her mouth, then" |
| | ratio | 0.45 | |
| 97.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 80 | | matches | | 0 | "The Raven’s Nest smelled of" | | 1 | "Rory wiped her hands on" | | 2 | "The ice clinked against the" | | 3 | "She hadn’t meant to come" | | 4 | "The delivery had taken longer" | | 5 | "The green glow seeped through" | | 6 | "Silas was wiping down the" | | 7 | "He didn’t look up right" | | 8 | "She took a sip, the" | | 9 | "A smirk tugged at the" | | 10 | "She swirled the glass, watching" | | 11 | "The bar was empty except" | | 12 | "She should’ve left." | | 13 | "Silas set the rag down." | | 14 | "His hazel eyes flicked over" | | 15 | "He tilted his head, the" | | 16 | "She laughed, but it came" | | 17 | "He didn’t smile." | | 18 | "Rory traced the rim of" | | 19 | "He tapped his knee, the" |
| | ratio | 0.725 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 80 | | matches | | 0 | "Now, he just waited." | | 1 | "*But first, drink.*" | | 2 | "Now, she turned the keys" |
| | ratio | 0.038 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 1 | | matches | | 0 | "The delivery had taken longer than usual—Yu-Fei’s cousin had insisted on showing her the new wok, as if she hadn’t seen one before—and by the time she’d locked …" |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 3 | | matches | | 0 | "She took, the bourbon burning just enough to make her throat tighten" | | 1 | "He poured, neat" | | 2 | "She exhaled, sharp" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.087 | | leniency | 0.174 | | rawRatio | 0 | | effectiveRatio | 0 | |