| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 77.06% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1308 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slightly" | | 1 | "softly" | | 2 | "really" | | 3 | "gently" |
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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) | |
| 38.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1308 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "chill" | | 1 | "weight" | | 2 | "familiar" | | 3 | "silence" | | 4 | "velvet" | | 5 | "glinting" | | 6 | "chaotic" | | 7 | "eyebrow" | | 8 | "whisper" | | 9 | "echoed" | | 10 | "flicker" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "eyes widened" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 67 | | matches | | 0 | "felt the sting" | | 1 | "the feeling of regret" |
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| 78.89% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 67 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 68 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1308 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1080 | | uniqueNames | 12 | | maxNameDensity | 0.93 | | worstName | "Eva" | | maxWindowNameDensity | 2 | | worstWindowName | "Eva" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Silas | 4 | | Taff | 1 | | London | 2 | | Rory | 8 | | Cardiff | 2 | | Eva | 10 | | Atlantic | 1 | | Aurora | 6 | | Evan | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Taff" | | 4 | "Rory" | | 5 | "Eva" | | 6 | "Aurora" | | 7 | "Evan" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Cardiff" |
| | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 4 | | matches | | 0 | "as if holding herself together" | | 1 | "as if trying to capture a face in the dark" | | 2 | "felt like hiding" | | 3 | "tasted like old wine and cold rain. It ta" | | 4 | "tasted like a life that had been offered" |
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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 | 1308 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 68 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 38.47 | | std | 26.96 | | cv | 0.701 | | sampleLengths | | 0 | 81 | | 1 | 108 | | 2 | 4 | | 3 | 55 | | 4 | 17 | | 5 | 33 | | 6 | 1 | | 7 | 93 | | 8 | 30 | | 9 | 15 | | 10 | 44 | | 11 | 19 | | 12 | 53 | | 13 | 12 | | 14 | 57 | | 15 | 35 | | 16 | 4 | | 17 | 20 | | 18 | 5 | | 19 | 23 | | 20 | 80 | | 21 | 25 | | 22 | 41 | | 23 | 68 | | 24 | 7 | | 25 | 41 | | 26 | 55 | | 27 | 29 | | 28 | 51 | | 29 | 32 | | 30 | 5 | | 31 | 42 | | 32 | 59 | | 33 | 64 |
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| 84.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 67 | | matches | | 0 | "were pinned" | | 1 | "was combed" | | 2 | "been ten" | | 3 | "been offered" | | 4 | "been walked" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 189 | | matches | | 0 | "wasn’t commanding" | | 1 | "was staring" | | 2 | "was sitting" | | 3 | "was wearing" | | 4 | "were coming" | | 5 | "was staying" | | 6 | "was still staring" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 68 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 676 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 14 | | adverbRatio | 0.020710059171597635 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0073964497041420114 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 19.24 | | std | 15.57 | | cv | 0.809 | | sampleLengths | | 0 | 21 | | 1 | 19 | | 2 | 18 | | 3 | 23 | | 4 | 16 | | 5 | 23 | | 6 | 13 | | 7 | 13 | | 8 | 16 | | 9 | 27 | | 10 | 4 | | 11 | 14 | | 12 | 16 | | 13 | 7 | | 14 | 18 | | 15 | 2 | | 16 | 15 | | 17 | 12 | | 18 | 21 | | 19 | 1 | | 20 | 5 | | 21 | 18 | | 22 | 29 | | 23 | 13 | | 24 | 10 | | 25 | 18 | | 26 | 20 | | 27 | 10 | | 28 | 15 | | 29 | 44 | | 30 | 6 | | 31 | 13 | | 32 | 7 | | 33 | 24 | | 34 | 22 | | 35 | 12 | | 36 | 4 | | 37 | 53 | | 38 | 35 | | 39 | 4 | | 40 | 4 | | 41 | 6 | | 42 | 10 | | 43 | 5 | | 44 | 14 | | 45 | 9 | | 46 | 8 | | 47 | 13 | | 48 | 21 | | 49 | 19 |
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| 40.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3235294117647059 | | totalSentences | 68 | | uniqueOpeners | 22 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 67.27% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 55 | | matches | | 0 | "Her delivery bag hung heavy" | | 1 | "She pushed open the heavy" | | 2 | "She had forgotten the way" | | 3 | "He was staring into a" | | 4 | "He didn’t look up." | | 5 | "She moved toward the counter," | | 6 | "It was a shape that" | | 7 | "It had been ten years." | | 8 | "Her voice was raspy, stripped" | | 9 | "She shifted her weight, the" | | 10 | "She laughed, a dry, brittle" | | 11 | "She said we were going" | | 12 | "We were twenty-five when we" | | 13 | "She could see her own" | | 14 | "She saw the straight black" | | 15 | "She thought of Evan, and" | | 16 | "She thought of the flat" | | 17 | "I’m not the same" | | 18 | "She pushed the glass forward," | | 19 | "She took a breath, the" |
| | ratio | 0.382 | |
| 23.64% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 55 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Aurora pulled her collar up" | | 2 | "Her delivery bag hung heavy" | | 3 | "She pushed open the heavy" | | 4 | "The bar was dim, lit" | | 5 | "Maps were pinned to the" | | 6 | "She had forgotten the way" | | 7 | "The grey-streaked auburn hair was" | | 8 | "He was staring into a" | | 9 | "He didn’t look up." | | 10 | "Aurora stepped further in, her" | | 11 | "She moved toward the counter," | | 12 | "It was a shape that" | | 13 | "Someone was sitting in the" | | 14 | "The figure pushed off the" | | 15 | "The woman was wearing a" | | 16 | "It had been ten years." | | 17 | "The Eva she remembered had" | | 18 | "This woman stood still, her" | | 19 | "Rory blinked, her bright blue" |
| | ratio | 0.873 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 55.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 5 | | matches | | 0 | "This woman stood still, her posture rigid, her hands tucked into her pockets as if holding herself together." | | 1 | "Eva’s head snapped up. Her hazel eyes widened, the pupils dilating as if trying to capture a face in the dark. She didn’t smile. She didn’t rush forward. She ju…" | | 2 | "She saw the straight black hair, the bright blue eyes that had gone cold, hardened by a life she hadn’t chosen." | | 3 | "Eva finished her water. The ice had melted. She set the glass down, the clink sharp in the quiet room. She didn’t say goodbye. She didn’t say she was staying. S…" | | 4 | "Rory touched the scar on her wrist. She knew the feeling of regret now. It tasted like old wine and cold rain. It tasted like a life that had been offered and r…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.063 | | leniency | 0.125 | | rawRatio | 0 | | effectiveRatio | 0 | |