| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.373 | | leniency | 0.745 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 75.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1404 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "very" | | 1 | "carefully" | | 2 | "slightly" | | 3 | "really" | | 4 | "slowly" |
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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) | |
| 89.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1404 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "flicker" | | 1 | "eyebrow" | | 2 | "standard" |
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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 | 1 | | narrationSentences | 75 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 75 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 64 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 19 | | totalWords | 1412 | | ratio | 0.013 | | matches | | 0 | "me" | | 1 | "job" | | 2 | "Standard" | | 3 | "London, Rory. Come to London, I'll find you a room, we'll be terrible together." | | 4 | "DL 1998" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 34.65% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 997 | | uniqueNames | 12 | | maxNameDensity | 2.31 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 23 | | Yu-Fei | 2 | | Silas | 7 | | Nest | 1 | | Whiskey | 1 | | Jameson | 1 | | Roath | 1 | | Park | 1 | | Eva | 15 | | Paddington | 1 | | Thursday | 1 | | Found | 3 |
| | persons | | 0 | "Rory" | | 1 | "Yu-Fei" | | 2 | "Silas" | | 3 | "Jameson" | | 4 | "Eva" |
| | places | | 0 | "Roath" | | 1 | "Park" | | 2 | "Paddington" |
| | globalScore | 0.347 | | windowScore | 0.5 | |
| 94.44% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 1 | | matches | | 0 | "smelled like it always did — spilled bitte" |
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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 | 1412 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 107 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 31.38 | | std | 31.77 | | cv | 1.013 | | sampleLengths | | 0 | 92 | | 1 | 71 | | 2 | 11 | | 3 | 116 | | 4 | 18 | | 5 | 1 | | 6 | 62 | | 7 | 18 | | 8 | 3 | | 9 | 20 | | 10 | 37 | | 11 | 42 | | 12 | 1 | | 13 | 38 | | 14 | 47 | | 15 | 5 | | 16 | 13 | | 17 | 17 | | 18 | 7 | | 19 | 4 | | 20 | 61 | | 21 | 19 | | 22 | 7 | | 23 | 75 | | 24 | 75 | | 25 | 3 | | 26 | 3 | | 27 | 23 | | 28 | 2 | | 29 | 96 | | 30 | 9 | | 31 | 9 | | 32 | 122 | | 33 | 20 | | 34 | 26 | | 35 | 9 | | 36 | 5 | | 37 | 61 | | 38 | 9 | | 39 | 40 | | 40 | 16 | | 41 | 45 | | 42 | 34 | | 43 | 10 | | 44 | 10 |
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| 86.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 75 | | matches | | 0 | "being told" | | 1 | "was cropped" | | 2 | "being asked" | | 3 | "been given" |
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| 72.61% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 157 | | matches | | 0 | "was forever losing" | | 1 | "was making" | | 2 | "were absorbing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 107 | | ratio | 0.056 | | matches | | 0 | "Inside, the Nest smelled like it always did — spilled bitter, old wood, the faint chalky ghost of the maps on the walls." | | 1 | "This was cropped short and bleached almost white, and there was ink climbing out of the collar of her coat and up the back of her neck — something dark and geometric that hadn't been there the last time Rory had put her arms around her." | | 2 | "And there it was — the face, the same wide mouth, the same slightly crooked left eyebrow that had been split by a swing set when they were nine." | | 3 | "It came out wrong — too sharp, more air than sound." | | 4 | "\"Of you turning up.\" She turned on the stool then, fully, and looked at Rory head-on, and Rory saw for the first time how tired she was — the grey under the eyes, the small hard set of the jaw." | | 5 | "\"So do you.\" Eva reached out, and for a moment Rory thought she was going to touch her face, but her fingers stopped at Rory's left wrist, where the sleeve had ridden up, and rested there — on the little crescent scar, white as a fingernail clipping, from the summer they'd both fallen off the same wall." |
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| 97.53% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 934 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 40 | | adverbRatio | 0.042826552462526764 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.013918629550321198 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 13.2 | | std | 13.55 | | cv | 1.027 | | sampleLengths | | 0 | 31 | | 1 | 37 | | 2 | 10 | | 3 | 14 | | 4 | 23 | | 5 | 23 | | 6 | 18 | | 7 | 5 | | 8 | 2 | | 9 | 11 | | 10 | 19 | | 11 | 22 | | 12 | 5 | | 13 | 24 | | 14 | 46 | | 15 | 18 | | 16 | 1 | | 17 | 3 | | 18 | 29 | | 19 | 1 | | 20 | 1 | | 21 | 6 | | 22 | 22 | | 23 | 4 | | 24 | 5 | | 25 | 5 | | 26 | 4 | | 27 | 3 | | 28 | 8 | | 29 | 12 | | 30 | 22 | | 31 | 6 | | 32 | 7 | | 33 | 2 | | 34 | 9 | | 35 | 4 | | 36 | 3 | | 37 | 22 | | 38 | 4 | | 39 | 1 | | 40 | 34 | | 41 | 4 | | 42 | 36 | | 43 | 5 | | 44 | 6 | | 45 | 5 | | 46 | 6 | | 47 | 6 | | 48 | 1 | | 49 | 17 |
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| 58.26% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.411214953271028 | | totalSentences | 107 | | uniqueOpeners | 44 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 60 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 60 | | matches | | 0 | "He looked up, and something" | | 1 | "She was three steps into" | | 2 | "Her eyes came up and" | | 3 | "Her voice had gone lower." | | 4 | "Her hands wanted somewhere to" | | 5 | "She put them in her" | | 6 | "She'd hated whiskey." | | 7 | "She'd once spat a mouthful" | | 8 | "She lifted her glass" | | 9 | "It came out wrong —" | | 10 | "Her tongue found the tannin," | | 11 | "She turned on the stool" | | 12 | "She thought about the girl" | | 13 | "She thought about how she" | | 14 | "she said, and took the" |
| | ratio | 0.25 | |
| 68.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 60 | | matches | | 0 | "The green neon buzzed like" | | 1 | "Rain slicked the pavement into" | | 2 | "Silas was down the far" | | 3 | "He looked up, and something" | | 4 | "A flicker of warning, maybe." | | 5 | "She was three steps into" | | 6 | "The woman at the bar" | | 7 | "Eva had worn it long" | | 8 | "This was cropped short and" | | 9 | "Rory set the delivery bag" | | 10 | "The woman turned." | | 11 | "A silver hoop through the" | | 12 | "Her eyes came up and" | | 13 | "Her voice had gone lower." | | 14 | "Eva glanced at it" | | 15 | "Rory came the rest of" | | 16 | "Her hands wanted somewhere to" | | 17 | "She put them in her" | | 18 | "Eva turned her glass on" | | 19 | "Whiskey, neat, half gone." |
| | ratio | 0.783 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 60 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "Straight as a ruler, shoulders set, the particular tilt of a head that had spent its adolescence being told to sit up." | | 1 | "And there it was — the face, the same wide mouth, the same slightly crooked left eyebrow that had been split by a swing set when they were nine." | | 2 | "Found the green sign and the man behind the bar with the signet ring and the bad knee who had asked her exactly no questions and given her exactly what she need…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 51 | | tagDensity | 0.235 | | leniency | 0.471 | | rawRatio | 0.083 | | effectiveRatio | 0.039 | |