| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.294 | | leniency | 0.588 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 83.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 908 | | totalAiIsmAdverbs | 3 | | 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) | |
| 88.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 908 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 41 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 41 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 64 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 908 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 542 | | uniqueNames | 8 | | maxNameDensity | 0.92 | | worstName | "Owen" | | maxWindowNameDensity | 2 | | worstWindowName | "Owen" | | discoveredNames | | Rory | 4 | | Soho | 1 | | Pryce | 1 | | Cardiff | 1 | | Bay | 1 | | Silas | 3 | | Owen | 5 | | Scotch | 1 |
| | persons | | 0 | "Rory" | | 1 | "Pryce" | | 2 | "Silas" | | 3 | "Owen" | | 4 | "Scotch" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 28 | | 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 | 908 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 64 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 25.94 | | std | 26.69 | | cv | 1.029 | | sampleLengths | | 0 | 87 | | 1 | 18 | | 2 | 9 | | 3 | 39 | | 4 | 5 | | 5 | 104 | | 6 | 1 | | 7 | 5 | | 8 | 31 | | 9 | 18 | | 10 | 49 | | 11 | 32 | | 12 | 16 | | 13 | 11 | | 14 | 55 | | 15 | 5 | | 16 | 36 | | 17 | 3 | | 18 | 3 | | 19 | 16 | | 20 | 5 | | 21 | 42 | | 22 | 3 | | 23 | 4 | | 24 | 3 | | 25 | 1 | | 26 | 32 | | 27 | 11 | | 28 | 88 | | 29 | 21 | | 30 | 62 | | 31 | 32 | | 32 | 5 | | 33 | 48 | | 34 | 8 |
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| 79.59% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 41 | | matches | | 0 | "been left" | | 1 | "was allowed" | | 2 | "being asked" |
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| 58.16% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 94 | | matches | | 0 | "was giving" | | 1 | "was watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 64 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 542 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.027675276752767528 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.005535055350553505 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 64 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 64 | | mean | 14.19 | | std | 11.11 | | cv | 0.783 | | sampleLengths | | 0 | 34 | | 1 | 2 | | 2 | 24 | | 3 | 27 | | 4 | 17 | | 5 | 1 | | 6 | 9 | | 7 | 28 | | 8 | 11 | | 9 | 5 | | 10 | 15 | | 11 | 21 | | 12 | 13 | | 13 | 38 | | 14 | 17 | | 15 | 1 | | 16 | 5 | | 17 | 24 | | 18 | 7 | | 19 | 18 | | 20 | 23 | | 21 | 12 | | 22 | 14 | | 23 | 9 | | 24 | 23 | | 25 | 16 | | 26 | 7 | | 27 | 4 | | 28 | 28 | | 29 | 4 | | 30 | 23 | | 31 | 5 | | 32 | 19 | | 33 | 17 | | 34 | 3 | | 35 | 3 | | 36 | 6 | | 37 | 7 | | 38 | 3 | | 39 | 5 | | 40 | 3 | | 41 | 19 | | 42 | 20 | | 43 | 3 | | 44 | 4 | | 45 | 3 | | 46 | 1 | | 47 | 18 | | 48 | 14 | | 49 | 11 |
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| 61.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4375 | | totalSentences | 64 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 35 | | matches | (empty) | | ratio | 0 | |
| 2.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 35 | | matches | | 0 | "She took the stool at" | | 1 | "He set the glass down," | | 2 | "She turned on the stool." | | 3 | "His overcoat was charcoal wool," | | 4 | "His hair had gone short" | | 5 | "He used to wear it" | | 6 | "He glanced down, then back" | | 7 | "He came over slowly, and" | | 8 | "He hunched over the bar" | | 9 | "He almost smiled" | | 10 | "He was giving them room," | | 11 | "He turned the glass" | | 12 | "He drank, and set the" | | 13 | "She looked at the maps" | | 14 | "She heard her own voice" | | 15 | "He nodded slowly, like a" | | 16 | "She picked up her Scotch," | | 17 | "She could feel the shape" | | 18 | "He was watching the mirror," |
| | ratio | 0.543 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 33 | | totalSentences | 35 | | matches | | 0 | "The green neon above the" | | 1 | "The bar held its usual" | | 2 | "She took the stool at" | | 3 | "Silas lifted a hand from" | | 4 | "He set the glass down," | | 5 | "She turned on the stool." | | 6 | "Owen Pryce stood on the" | | 7 | "His overcoat was charcoal wool," | | 8 | "His hair had gone short" | | 9 | "He used to wear it" | | 10 | "He glanced down, then back" | | 11 | "He came over slowly, and" | | 12 | "Owen took the stool two" | | 13 | "He hunched over the bar" | | 14 | "He almost smiled" | | 15 | "Silas slid a glass of" | | 16 | "Rory watched him go." | | 17 | "He was giving them room," | | 18 | "He turned the glass" | | 19 | "The bar went on around" |
| | ratio | 0.943 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 35 | | matches | | | ratio | 0.029 | |
| 67.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 19 | | technicalSentenceCount | 2 | | matches | | 0 | "The green neon above the door buzzed the way it always did, a hum that settled in the back of the teeth, and Rory pushed through under it with rain sliding off …" | | 1 | "Silas slid a glass of something amber toward Owen without being asked, then went down the bar to polish a row of tumblers that did not need polishing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.147 | | leniency | 0.294 | | rawRatio | 0 | | effectiveRatio | 0 | |