| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 2 | | adverbTags | | 0 | "he asked quietly [quietly]" | | 1 | "she said slowly [slowly]" |
| | dialogueSentences | 50 | | tagDensity | 0.32 | | leniency | 0.64 | | rawRatio | 0.125 | | effectiveRatio | 0.08 | |
| 86.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1479 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "perfectly" | | 1 | "really" | | 2 | "slightly" | | 3 | "slowly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 69.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1479 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "measured" | | 1 | "weight" | | 2 | "flicker" | | 3 | "warmth" | | 4 | "could feel" | | 5 | "silence" | | 6 | "trembled" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 108 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 108 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 139 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1469 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.14% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 983 | | uniqueNames | 13 | | maxNameDensity | 1.02 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 3 | | Cambridge | 1 | | Tuesday | 1 | | Rory | 10 | | Moreau | 1 | | East | 1 | | London | 1 | | Carter | 1 | | French | 1 | | Despite | 1 | | Mayfair | 1 | | Lucien | 9 | | Ptolemy | 4 |
| | persons | | 0 | "Eva" | | 1 | "Rory" | | 2 | "Moreau" | | 3 | "Carter" | | 4 | "French" | | 5 | "Lucien" | | 6 | "Ptolemy" |
| | places | | 0 | "Cambridge" | | 1 | "East" | | 2 | "London" | | 3 | "Mayfair" |
| | globalScore | 0.991 | | windowScore | 1 | |
| 71.88% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed solid" | | 1 | "sounded like a promise" |
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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 | 1469 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 139 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 65 | | mean | 22.6 | | std | 19.38 | | cv | 0.857 | | sampleLengths | | 0 | 55 | | 1 | 5 | | 2 | 25 | | 3 | 20 | | 4 | 8 | | 5 | 7 | | 6 | 47 | | 7 | 12 | | 8 | 13 | | 9 | 10 | | 10 | 67 | | 11 | 1 | | 12 | 24 | | 13 | 5 | | 14 | 31 | | 15 | 2 | | 16 | 3 | | 17 | 4 | | 18 | 44 | | 19 | 3 | | 20 | 29 | | 21 | 9 | | 22 | 29 | | 23 | 31 | | 24 | 5 | | 25 | 3 | | 26 | 79 | | 27 | 5 | | 28 | 40 | | 29 | 13 | | 30 | 10 | | 31 | 35 | | 32 | 8 | | 33 | 4 | | 34 | 23 | | 35 | 25 | | 36 | 2 | | 37 | 52 | | 38 | 28 | | 39 | 5 | | 40 | 32 | | 41 | 24 | | 42 | 37 | | 43 | 73 | | 44 | 9 | | 45 | 21 | | 46 | 48 | | 47 | 14 | | 48 | 65 | | 49 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 108 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 181 | | matches | | 0 | "was hammering" | | 1 | "was holding" |
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| 19.53% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 139 | | ratio | 0.043 | | matches | | 0 | "When she opened the door, the smell of him hit first—cedar and something darker, like smoke trapped in old wood." | | 1 | "Had deleted his voicemails without listening, because if she heard his voice—that careful, measured voice that could promise safety or threaten ruin in the same breath—she might break." | | 2 | "He knew the way she took her coffee—black, no sugar, the way she folded her arms when she was nervous, the way her breath caught when he got too close." | | 3 | "Lucien crossed the threshold like a man entering enemy territory—careful, watchful, cataloguing every detail." | | 4 | "The way he'd kissed her back—desperate, hungry, like a man drowning—only to pull away with a sharp \"Non\" that had cut deeper than any blade." | | 5 | "Rory looked at him—really looked." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 991 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.02320887991927346 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.009081735620585268 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 139 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 139 | | mean | 10.57 | | std | 9.27 | | cv | 0.877 | | sampleLengths | | 0 | 18 | | 1 | 37 | | 2 | 5 | | 3 | 8 | | 4 | 3 | | 5 | 1 | | 6 | 13 | | 7 | 5 | | 8 | 15 | | 9 | 4 | | 10 | 4 | | 11 | 7 | | 12 | 10 | | 13 | 9 | | 14 | 5 | | 15 | 3 | | 16 | 10 | | 17 | 10 | | 18 | 5 | | 19 | 1 | | 20 | 1 | | 21 | 5 | | 22 | 4 | | 23 | 3 | | 24 | 6 | | 25 | 10 | | 26 | 15 | | 27 | 20 | | 28 | 21 | | 29 | 5 | | 30 | 6 | | 31 | 1 | | 32 | 6 | | 33 | 2 | | 34 | 2 | | 35 | 2 | | 36 | 12 | | 37 | 5 | | 38 | 4 | | 39 | 13 | | 40 | 1 | | 41 | 1 | | 42 | 12 | | 43 | 2 | | 44 | 3 | | 45 | 4 | | 46 | 1 | | 47 | 9 | | 48 | 6 | | 49 | 28 |
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| 50.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.37410071942446044 | | totalSentences | 139 | | uniqueOpeners | 52 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 75.32% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 94 | | matches | | 0 | "It wasn't the casual rap" | | 1 | "It was deliberate." | | 2 | "He couldn't be here." | | 3 | "She stared at the three" | | 4 | "She'd made sure of that." | | 5 | "She should ignore it." | | 6 | "She undid the bolts one" | | 7 | "He stood with his weight" | | 8 | "He tilted his head." | | 9 | "She'd always found the split" | | 10 | "She hadn't answered a single" | | 11 | "He smiled without warmth" | | 12 | "His smile vanished, and for" | | 13 | "he asked quietly" | | 14 | "He did know her." | | 15 | "He knew the way she" | | 16 | "He knew things she'd never" | | 17 | "His gaze swept the cramped" | | 18 | "His knuckles were white where" | | 19 | "He looked up at her," |
| | ratio | 0.362 | |
| 61.06% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 94 | | matches | | 0 | "Ptolemy had been restless for" | | 1 | "The knock made her freeze." | | 2 | "It wasn't the casual rap" | | 3 | "It was deliberate." | | 4 | "Rory set down her pen." | | 5 | "That couldn't be right." | | 6 | "He couldn't be here." | | 7 | "The second knock came, harder" | | 8 | "She stared at the three" | | 9 | "Lucien Moreau had no business" | | 10 | "She'd made sure of that." | | 11 | "A ghost of her own." | | 12 | "She should ignore it." | | 13 | "She undid the bolts one" | | 14 | "He stood with his weight" | | 15 | "The way he said her" | | 16 | "The French softened the vowels," | | 17 | "He tilted his head." | | 18 | "The movement was slight, but" | | 19 | "She'd always found the split" |
| | ratio | 0.798 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "Ptolemy had been restless for the better part of an hour, which meant Eva usually wasn't far behind." | | 1 | "Had deleted his voicemails without listening, because if she heard his voice—that careful, measured voice that could promise safety or threaten ruin in the same…" | | 2 | "His gaze swept the cramped flat, landing on the stacks of papers, the open laptop, the cat who sat on the counter with his tail flicking." |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice steadier than she felt" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.16 | | leniency | 0.32 | | rawRatio | 0 | | effectiveRatio | 0 | |