| 52.63% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 42 | | adverbTagCount | 7 | | adverbTags | | 0 | "Eva turned fully [fully]" | | 1 | "Eva said quietly [quietly]" | | 2 | "Eva's fingers tightened around [around]" | | 3 | "Eva said finally [finally]" | | 4 | "Eva hesitated then [then]" | | 5 | "The words tasted like [like]" | | 6 | "Silas said quietly [quietly]" |
| | dialogueSentences | 95 | | tagDensity | 0.442 | | leniency | 0.884 | | rawRatio | 0.167 | | effectiveRatio | 0.147 | |
| 62.57% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1336 | | totalAiIsmAdverbs | 10 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "barely above a whisper" | | count | 1 |
| | 4 | | | 5 | | | 6 | | adverb | "reluctantly" | | count | 1 |
| | 7 | |
| | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "suddenly" | | 3 | "barely above a whisper" | | 4 | "perfectly" | | 5 | "completely" | | 6 | "reluctantly" | | 7 | "really" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 55.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1336 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "flickered" | | 1 | "familiar" | | 2 | "perfect" | | 3 | "tracing" | | 4 | "silence" | | 5 | "unspoken" | | 6 | "whisper" | | 7 | "measured" | | 8 | "methodical" | | 9 | "stomach" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "blood ran cold" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "blood ran cold" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 93 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 93 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 144 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1330 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 81 | | wordCount | 841 | | uniqueNames | 7 | | maxNameDensity | 4.4 | | worstName | "Aurora" | | maxWindowNameDensity | 8.5 | | worstWindowName | "Eva" | | discoveredNames | | Aurora | 37 | | Eva | 30 | | Silas | 9 | | Mostly | 1 | | Evan | 2 | | London | 1 | | Cardiff | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Eva" | | 2 | "Silas" | | 3 | "Evan" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared beside her, refilling her glass without asking" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.752 | | wordCount | 1330 | | matches | | 0 | "not to Cardiff necessarily, but" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 144 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 69 | | mean | 19.28 | | std | 10.98 | | cv | 0.569 | | sampleLengths | | 0 | 47 | | 1 | 2 | | 2 | 12 | | 3 | 3 | | 4 | 49 | | 5 | 14 | | 6 | 22 | | 7 | 13 | | 8 | 27 | | 9 | 9 | | 10 | 14 | | 11 | 13 | | 12 | 10 | | 13 | 5 | | 14 | 19 | | 15 | 20 | | 16 | 8 | | 17 | 16 | | 18 | 13 | | 19 | 16 | | 20 | 21 | | 21 | 22 | | 22 | 12 | | 23 | 21 | | 24 | 9 | | 25 | 10 | | 26 | 10 | | 27 | 3 | | 28 | 23 | | 29 | 15 | | 30 | 13 | | 31 | 32 | | 32 | 11 | | 33 | 8 | | 34 | 25 | | 35 | 17 | | 36 | 23 | | 37 | 26 | | 38 | 35 | | 39 | 15 | | 40 | 40 | | 41 | 11 | | 42 | 16 | | 43 | 23 | | 44 | 15 | | 45 | 9 | | 46 | 33 | | 47 | 13 | | 48 | 17 | | 49 | 22 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 93 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 143 | | matches | (empty) | |
| 83.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 144 | | ratio | 0.021 | | matches | | 0 | "\"Not like you.\" Eva's bright green eyes—once so full of laughter—now carried a hardness that hadn't been there before." | | 1 | "The bar's background noise—clinking glasses, murmured conversations—suddenly seemed too loud." | | 2 | "For a moment, Aurora saw her old friend—the girl she'd grown up with, shared secrets with, made plans with." |
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| 98.77% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 846 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.04018912529550828 | | lyAdverbCount | 18 | | lyAdverbRatio | 0.02127659574468085 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 144 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 144 | | mean | 9.24 | | std | 5.77 | | cv | 0.625 | | sampleLengths | | 0 | 21 | | 1 | 17 | | 2 | 9 | | 3 | 2 | | 4 | 8 | | 5 | 4 | | 6 | 3 | | 7 | 8 | | 8 | 3 | | 9 | 10 | | 10 | 28 | | 11 | 12 | | 12 | 2 | | 13 | 19 | | 14 | 3 | | 15 | 10 | | 16 | 3 | | 17 | 19 | | 18 | 8 | | 19 | 7 | | 20 | 2 | | 21 | 11 | | 22 | 3 | | 23 | 11 | | 24 | 2 | | 25 | 5 | | 26 | 5 | | 27 | 4 | | 28 | 1 | | 29 | 8 | | 30 | 11 | | 31 | 16 | | 32 | 4 | | 33 | 6 | | 34 | 2 | | 35 | 14 | | 36 | 2 | | 37 | 8 | | 38 | 5 | | 39 | 13 | | 40 | 3 | | 41 | 13 | | 42 | 8 | | 43 | 13 | | 44 | 9 | | 45 | 10 | | 46 | 2 | | 47 | 11 | | 48 | 10 | | 49 | 8 |
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| 66.90% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.4513888888888889 | | totalSentences | 144 | | uniqueOpeners | 65 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 7 | | totalSentences | 89 | | matches | | 0 | "she admitted, the words feeling" | | 1 | "She glanced at it, her" | | 2 | "he asked, his voice gentle" | | 3 | "She was still staring at" | | 4 | "he said, polishing a glass" | | 5 | "He leaned forward slightly, his" | | 6 | "she whispered, the pieces clicking" |
| | ratio | 0.079 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 89 | | matches | | 0 | "The green neon glow of" | | 1 | "Silas moved along the polished" | | 2 | "The amber liquid caught the" | | 3 | "Aurora swirled the whisky" | | 4 | "The voice sliced through the" | | 5 | "Aurora turned slowly, her black" | | 6 | "The woman standing behind her" | | 7 | "The name felt strange on" | | 8 | "Eva slid onto the stool" | | 9 | "Aurora took another sip of" | | 10 | "Eva's bright green eyes—once so" | | 11 | "Aurora signaled to Silas for" | | 12 | "Eva turned fully toward her," | | 13 | "Aurora nodded at the diamond" | | 14 | "Eva's lips tightened" | | 15 | "Aurora meant it" | | 16 | "Eva accepted the glass Silas" | | 17 | "Eva's eyes dropped to Aurora's" | | 18 | "Aurora instinctively pulled her sleeve" | | 19 | "Eva swirled the whisky in" |
| | ratio | 0.966 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 1 | | matches | | 0 | "The woman standing behind her wore designer clothes that had probably cost more than Aurora's monthly rent, but beneath the expensive fabric and perfect makeup,…" |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 42 | | uselessAdditionCount | 6 | | matches | | 0 | "Eva slid, her fingers tracing the condensation on the bar" | | 1 | "Aurora slid, her legs slightly unsteady from the whisky" | | 2 | "he asked, his voice gentle" | | 3 | "Aurora turned, a knot forming in her stomach" | | 4 | "He leaned, his voice dropping to a near whisper" | | 5 | "she whispered, the pieces clicking into place with horrifying clarity" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "she admitted (admit)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 95 | | tagDensity | 0.074 | | leniency | 0.147 | | rawRatio | 0.286 | | effectiveRatio | 0.042 | |