| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "Rory said abruptly [abruptly]" |
| | dialogueSentences | 37 | | tagDensity | 0.351 | | leniency | 0.703 | | rawRatio | 0.077 | | effectiveRatio | 0.054 | |
| 86.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 739 | | totalAiIsmAdverbs | 2 | | 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) | |
| 52.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 739 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "echo" | | 1 | "trembled" | | 2 | "echoes" | | 3 | "measured" | | 4 | "constructed" | | 5 | "weight" | | 6 | "unspoken" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 49 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 49 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 735 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.44% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 540 | | uniqueNames | 9 | | maxNameDensity | 1.11 | | worstName | "Silas" | | maxWindowNameDensity | 2 | | worstWindowName | "Silas" | | discoveredNames | | Aurora | 1 | | Carter | 1 | | London | 2 | | Evan | 1 | | Blackwood | 1 | | Raven | 1 | | Nest | 1 | | Silas | 6 | | Rory | 3 |
| | persons | | 0 | "Aurora" | | 1 | "Carter" | | 2 | "Evan" | | 3 | "Blackwood" | | 4 | "Raven" | | 5 | "Silas" | | 6 | "Rory" |
| | places | | | globalScore | 0.944 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 34 | | 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 | 735 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 71 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 20.42 | | std | 13.15 | | cv | 0.644 | | sampleLengths | | 0 | 12 | | 1 | 58 | | 2 | 48 | | 3 | 53 | | 4 | 17 | | 5 | 37 | | 6 | 30 | | 7 | 25 | | 8 | 18 | | 9 | 13 | | 10 | 25 | | 11 | 20 | | 12 | 15 | | 13 | 39 | | 14 | 19 | | 15 | 14 | | 16 | 22 | | 17 | 15 | | 18 | 6 | | 19 | 14 | | 20 | 22 | | 21 | 13 | | 22 | 18 | | 23 | 10 | | 24 | 8 | | 25 | 22 | | 26 | 2 | | 27 | 10 | | 28 | 11 | | 29 | 9 | | 30 | 11 | | 31 | 15 | | 32 | 5 | | 33 | 30 | | 34 | 36 | | 35 | 13 |
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| 98.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 49 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 96 | | matches | | |
| 22.13% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 71 | | ratio | 0.042 | | matches | | 0 | "She turned slowly, bright blue eyes locking onto the stranger’s face — or rather, the man who used to be her closest friend." | | 1 | "Behind Silas, the hidden bookshelf trembled with muffled laughter, betrayals, and deals — echoes of his life under a different name." | | 2 | "The past had a way of catching up—with friends, with enemies, with ghosts long thought buried." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 544 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.029411764705882353 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.011029411764705883 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 10.35 | | std | 6.96 | | cv | 0.673 | | sampleLengths | | 0 | 12 | | 1 | 12 | | 2 | 23 | | 3 | 23 | | 4 | 29 | | 5 | 19 | | 6 | 32 | | 7 | 21 | | 8 | 10 | | 9 | 7 | | 10 | 3 | | 11 | 15 | | 12 | 11 | | 13 | 8 | | 14 | 15 | | 15 | 2 | | 16 | 13 | | 17 | 25 | | 18 | 9 | | 19 | 9 | | 20 | 8 | | 21 | 5 | | 22 | 15 | | 23 | 10 | | 24 | 15 | | 25 | 5 | | 26 | 5 | | 27 | 10 | | 28 | 28 | | 29 | 11 | | 30 | 19 | | 31 | 6 | | 32 | 8 | | 33 | 22 | | 34 | 9 | | 35 | 6 | | 36 | 2 | | 37 | 4 | | 38 | 5 | | 39 | 9 | | 40 | 8 | | 41 | 9 | | 42 | 5 | | 43 | 3 | | 44 | 10 | | 45 | 18 | | 46 | 5 | | 47 | 5 | | 48 | 3 | | 49 | 5 |
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| 91.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5633802816901409 | | totalSentences | 71 | | uniqueOpeners | 40 | |
| 75.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 44 | | matches | | 0 | "Instead, he took a slow" |
| | ratio | 0.023 | |
| 56.36% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 44 | | matches | | 0 | "She hadn’t heard that tone" | | 1 | "She turned slowly, bright blue" | | 2 | "His left leg bore the" | | 3 | "He didn’t smile." | | 4 | "She fought the impulse to" | | 5 | "Her voice was soft but" | | 6 | "His eyes held steady, hazel" | | 7 | "She shrugged, the sound hollow" | | 8 | "He folded his arms, the" | | 9 | "She took the bar stool" | | 10 | "His voice dropped, measured" | | 11 | "he said, eyes flicking sideways" | | 12 | "His brows knitted" | | 13 | "Her fingers clenched into fists" | | 14 | "She pressed her lips, then" | | 15 | "She swallowed hard, her voice" | | 16 | "His tone was a scalpel" | | 17 | "His fingers brushed the silver" |
| | ratio | 0.409 | |
| 28.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 44 | | matches | | 0 | "The voice cut through the" | | 1 | "Aurora Carter froze mid-step, the" | | 2 | "She hadn’t heard that tone" | | 3 | "She turned slowly, bright blue" | | 4 | "Silas Blackwood, with his grey-streaked" | | 5 | "His left leg bore the" | | 6 | "The bar’s green neon sign" | | 7 | "The word slipped out before" | | 8 | "The name was heavier than" | | 9 | "He didn’t smile." | | 10 | "The silver signet ring gleamed" | | 11 | "She fought the impulse to" | | 12 | "Her voice was soft but" | | 13 | "His eyes held steady, hazel" | | 14 | "She shrugged, the sound hollow" | | 15 | "He folded his arms, the" | | 16 | "She took the bar stool" | | 17 | "His voice dropped, measured" | | 18 | "A bartender slid past them," | | 19 | "The scent clung to the" |
| | ratio | 0.864 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 44 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 1 | | matches | | 0 | "She turned slowly, bright blue eyes locking onto the stranger’s face — or rather, the man who used to be her closest friend." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 4 | | matches | | 0 | "She took, wary but unwilling to flee" | | 1 | "His voice dropped, measured" | | 2 | "Silas replied, voice rough" | | 3 | "he said, eyes flicking sideways" |
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| 41.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 4 | | fancyTags | | 0 | "She pressed (press)" | | 1 | "he whispered (whisper)" | | 2 | "she admitted (admit)" | | 3 | "she whispered (whisper)" |
| | dialogueSentences | 37 | | tagDensity | 0.189 | | leniency | 0.378 | | rawRatio | 0.571 | | effectiveRatio | 0.216 | |