| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 53 | | tagDensity | 0.094 | | leniency | 0.189 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 84.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1301 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "sharply" | | 1 | "gently" | | 2 | "slightly" | | 3 | "suddenly" |
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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) | |
| 65.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1301 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "tension" | | 1 | "familiar" | | 2 | "flicked" | | 3 | "silence" | | 4 | "echoed" | | 5 | "loomed" | | 6 | "shattered" | | 7 | "flickered" |
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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 | 86 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 86 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 3 | | totalWords | 1294 | | ratio | 0.002 | | matches | | 0 | "Scrape. Scrape." | | 1 | "clack" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 60.41% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 893 | | uniqueNames | 6 | | maxNameDensity | 1.79 | | worstName | "Silas" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | London | 1 | | Rory | 6 | | Silas | 16 | | Talisker | 1 | | Raven | 1 | | Nest | 1 |
| | persons | | | places | | | globalScore | 0.604 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 1 | | matches | | 0 | "breath that seemed to deflate the air in the room" |
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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 | 1294 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 135 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 75 | | mean | 17.25 | | std | 17.4 | | cv | 1.008 | | sampleLengths | | 0 | 50 | | 1 | 33 | | 2 | 27 | | 3 | 2 | | 4 | 49 | | 5 | 5 | | 6 | 1 | | 7 | 60 | | 8 | 13 | | 9 | 5 | | 10 | 2 | | 11 | 3 | | 12 | 25 | | 13 | 2 | | 14 | 47 | | 15 | 1 | | 16 | 54 | | 17 | 1 | | 18 | 28 | | 19 | 11 | | 20 | 36 | | 21 | 20 | | 22 | 3 | | 23 | 2 | | 24 | 2 | | 25 | 38 | | 26 | 13 | | 27 | 11 | | 28 | 6 | | 29 | 56 | | 30 | 1 | | 31 | 28 | | 32 | 30 | | 33 | 4 | | 34 | 6 | | 35 | 1 | | 36 | 47 | | 37 | 55 | | 38 | 4 | | 39 | 54 | | 40 | 1 | | 41 | 4 | | 42 | 4 | | 43 | 3 | | 44 | 22 | | 45 | 5 | | 46 | 2 | | 47 | 24 | | 48 | 8 | | 49 | 17 |
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| 97.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 86 | | matches | | 0 | "was tucked" | | 1 | "were hidden" |
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| 70.97% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 155 | | matches | | 0 | "was polishing" | | 1 | "was trying" | | 2 | "was trying" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 135 | | ratio | 0.015 | | matches | | 0 | "The bell above jingled—a cheerful, discordant sound—and a blast of cold air swept through the dim interior, scattering the gathered smoke." | | 1 | "He didn't look at her face; he looked at her arm, pulling it out of the pocket." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 905 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.02430939226519337 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.0066298342541436465 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 9.59 | | std | 6.67 | | cv | 0.695 | | sampleLengths | | 0 | 12 | | 1 | 17 | | 2 | 21 | | 3 | 10 | | 4 | 23 | | 5 | 16 | | 6 | 11 | | 7 | 2 | | 8 | 13 | | 9 | 19 | | 10 | 5 | | 11 | 12 | | 12 | 5 | | 13 | 1 | | 14 | 16 | | 15 | 23 | | 16 | 21 | | 17 | 13 | | 18 | 5 | | 19 | 2 | | 20 | 3 | | 21 | 16 | | 22 | 7 | | 23 | 1 | | 24 | 1 | | 25 | 2 | | 26 | 7 | | 27 | 14 | | 28 | 13 | | 29 | 5 | | 30 | 8 | | 31 | 1 | | 32 | 4 | | 33 | 12 | | 34 | 7 | | 35 | 9 | | 36 | 22 | | 37 | 1 | | 38 | 28 | | 39 | 6 | | 40 | 5 | | 41 | 10 | | 42 | 14 | | 43 | 12 | | 44 | 20 | | 45 | 3 | | 46 | 2 | | 47 | 2 | | 48 | 15 | | 49 | 10 |
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| 43.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.2740740740740741 | | totalSentences | 135 | | uniqueOpeners | 37 | |
| 41.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 81 | | matches | | 0 | "Suddenly, a loud crash shattered" |
| | ratio | 0.012 | |
| 52.10% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 81 | | matches | | 0 | "She didn’t take off the" | | 1 | "It slid an inch on" | | 2 | "He took in the line" | | 3 | "Her left wrist was tucked" | | 4 | "She slid onto the stool," | | 5 | "He reached under the bar" | | 6 | "He poured amber liquid into" | | 7 | "It was cold in her" | | 8 | "She threw it back in" | | 9 | "She wiped her mouth with" | | 10 | "He wore his usual suit," | | 11 | "He reached behind the bar" | | 12 | "She didn't argue." | | 13 | "She stood up, the movement" | | 14 | "She rested her chin on" | | 15 | "He opened it, the hum" | | 16 | "He took out a small," | | 17 | "She looked up then, her" | | 18 | "He didn't look at her" | | 19 | "She wore a heavy wool" |
| | ratio | 0.42 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 81 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Aurora yanked the heavy oak" | | 2 | "The bell above jingled—a cheerful," | | 3 | "Silas didn’t look up from" | | 4 | "The brass coaster was half" | | 5 | "Silas said, his voice rougher" | | 6 | "Rory stumbled past him, her" | | 7 | "She didn’t take off the" | | 8 | "Silas set the coaster down." | | 9 | "It slid an inch on" | | 10 | "Silas turned, wiping his hands" | | 11 | "He took in the line" | | 12 | "Her left wrist was tucked" | | 13 | "She slid onto the stool," | | 14 | "The stool legs scraped against" | | 15 | "Silas studied her for a" | | 16 | "The blue eyes behind the" | | 17 | "He reached under the bar" | | 18 | "He poured amber liquid into" | | 19 | "Rory grabbed the glass." |
| | ratio | 0.938 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 2 | | matches | | 0 | "Silas turned, wiping his hands on a rag that smelled of lemon polish and old secrets." | | 1 | "The crescent-shaped scar from her childhood was there, a silent witness to a life that was trying to swallow her whole." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "she whispered (whisper)" |
| | dialogueSentences | 53 | | tagDensity | 0.038 | | leniency | 0.075 | | rawRatio | 0.5 | | effectiveRatio | 0.038 | |