| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said softly [softly]" | | 1 | "Silas asked softly [softly]" |
| | dialogueSentences | 49 | | tagDensity | 0.388 | | leniency | 0.776 | | rawRatio | 0.105 | | effectiveRatio | 0.082 | |
| 74.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1192 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slowly" | | 1 | "sharply" | | 2 | "softly" | | 3 | "precisely" | | 4 | "slightly" |
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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) | |
| 79.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1192 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "trembled" | | 2 | "weight" | | 3 | "flicker" | | 4 | "shattered" |
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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 | 63 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 63 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 93 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1192 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 822 | | uniqueNames | 5 | | maxNameDensity | 1.34 | | worstName | "Evan" | | maxWindowNameDensity | 3 | | worstWindowName | "Evan" | | discoveredNames | | Soho | 2 | | Silas | 7 | | Evan | 11 | | Rory | 9 | | Cardiff | 1 |
| | persons | | | places | | | globalScore | 0.831 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | 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 | 1192 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 24.83 | | std | 20.65 | | cv | 0.831 | | sampleLengths | | 0 | 5 | | 1 | 31 | | 2 | 15 | | 3 | 22 | | 4 | 83 | | 5 | 46 | | 6 | 35 | | 7 | 58 | | 8 | 7 | | 9 | 34 | | 10 | 1 | | 11 | 33 | | 12 | 5 | | 13 | 14 | | 14 | 55 | | 15 | 12 | | 16 | 3 | | 17 | 5 | | 18 | 55 | | 19 | 5 | | 20 | 7 | | 21 | 30 | | 22 | 23 | | 23 | 38 | | 24 | 7 | | 25 | 42 | | 26 | 3 | | 27 | 3 | | 28 | 32 | | 29 | 7 | | 30 | 91 | | 31 | 14 | | 32 | 17 | | 33 | 48 | | 34 | 31 | | 35 | 5 | | 36 | 12 | | 37 | 4 | | 38 | 45 | | 39 | 29 | | 40 | 28 | | 41 | 9 | | 42 | 30 | | 43 | 13 | | 44 | 29 | | 45 | 28 | | 46 | 4 | | 47 | 39 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 63 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 135 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 93 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 827 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 24 | | adverbRatio | 0.02902055622732769 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.012091898428053204 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 93 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 93 | | mean | 12.82 | | std | 8.52 | | cv | 0.665 | | sampleLengths | | 0 | 5 | | 1 | 8 | | 2 | 23 | | 3 | 15 | | 4 | 11 | | 5 | 8 | | 6 | 3 | | 7 | 21 | | 8 | 3 | | 9 | 19 | | 10 | 14 | | 11 | 26 | | 12 | 5 | | 13 | 7 | | 14 | 9 | | 15 | 25 | | 16 | 14 | | 17 | 15 | | 18 | 6 | | 19 | 21 | | 20 | 18 | | 21 | 19 | | 22 | 7 | | 23 | 11 | | 24 | 16 | | 25 | 7 | | 26 | 1 | | 27 | 16 | | 28 | 17 | | 29 | 5 | | 30 | 14 | | 31 | 22 | | 32 | 7 | | 33 | 26 | | 34 | 12 | | 35 | 3 | | 36 | 5 | | 37 | 25 | | 38 | 24 | | 39 | 6 | | 40 | 5 | | 41 | 7 | | 42 | 27 | | 43 | 3 | | 44 | 9 | | 45 | 9 | | 46 | 5 | | 47 | 38 | | 48 | 7 | | 49 | 18 |
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| 55.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3763440860215054 | | totalSentences | 93 | | uniqueOpeners | 35 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 16.49% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 57 | | matches | | 0 | "She wiped down the mahogany" | | 1 | "She turned slowly." | | 2 | "He looked sculpted." | | 3 | "He took two steps toward" | | 4 | "His leather soles clicked sharply" | | 5 | "She shoved the rag into" | | 6 | "He slid onto the end" | | 7 | "He smiled, but it failed" | | 8 | "His irises, once a pale," | | 9 | "He accepted the correction with" | | 10 | "Her left hand trembled, just" | | 11 | "She pressed her wrist against" | | 12 | "she said, pouring two fingers" | | 13 | "He picked up the glass," | | 14 | "He trailed off, his gaze" | | 15 | "she said softly, keeping her" | | 16 | "He didn't wipe it away." | | 17 | "He leaned forward, resting his" | | 18 | "His slight limp was heavy" | | 19 | "His hazel eyes swept over" |
| | ratio | 0.509 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 57 | | matches | | 0 | "Rory didn't glance up from" | | 1 | "She wiped down the mahogany" | | 2 | "The voice hit her like" | | 3 | "Rory froze, the rag motionless" | | 4 | "She turned slowly." | | 5 | "Evan stood beneath the green" | | 6 | "He looked sculpted." | | 7 | "The roundness of his jaw," | | 8 | "A bespoke slate grey wool" | | 9 | "The cheap tobacco stink that" | | 10 | "He took two steps toward" | | 11 | "His leather soles clicked sharply" | | 12 | "Rory said, her voice dropping" | | 13 | "She shoved the rag into" | | 14 | "He slid onto the end" | | 15 | "Evan ran a manicured hand" | | 16 | "He smiled, but it failed" | | 17 | "His irises, once a pale," | | 18 | "He accepted the correction with" | | 19 | "Rory pulled a heavy glass" |
| | ratio | 1 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 32.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 4 | | matches | | 0 | "A bespoke slate grey wool overcoat hung over broad shoulders that used to slouch." | | 1 | "The cheap tobacco stink that had settled into his skin for three years had vanished, replaced by the faint, clinical tang of sandalwood and expensive citrus." | | 2 | "His slight limp was heavy tonight, the knee clearly bothering him, but he moved with that fluid, quiet confidence that made men half his age back down in dark a…" | | 3 | "Evan straightened up, his eyes darting to Silas, assessing the grey hair, the trim beard, the subtle shift in weight that spoke of a man who knew precisely how …" |
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| 19.74% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 4 | | matches | | 0 | "Rory said, her voice dropping an octave" | | 1 | "He trailed, his gaze lingering on the hollow of her throat" | | 2 | "Rory gripped, her knuckles turning white" | | 3 | "Rory said, not taking her eyes off Evan" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 49 | | tagDensity | 0.245 | | leniency | 0.49 | | rawRatio | 0.083 | | effectiveRatio | 0.041 | |