| 51.85% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 2 | | adverbTags | | 0 | "he said finally [finally]" | | 1 | "he said finally [finally]" |
| | dialogueSentences | 27 | | tagDensity | 0.37 | | leniency | 0.741 | | rawRatio | 0.2 | | effectiveRatio | 0.148 | |
| 93.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 823 | | totalAiIsmAdverbs | 1 | | 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) | |
| 2.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 823 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "pulsed" | | 1 | "tracing" | | 2 | "weight" | | 3 | "silence" | | 4 | "unspoken" | | 5 | "pulse" | | 6 | "racing" | | 7 | "unreadable" | | 8 | "reminder" |
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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 | 0 | | narrationSentences | 61 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 61 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 817 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 78.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 696 | | uniqueNames | 9 | | maxNameDensity | 1.44 | | worstName | "Silas" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Carter | 1 | | Rory | 7 | | London | 2 | | Evan | 3 | | Blackwood | 1 | | Silas | 10 | | Cardiff | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Rory" | | 4 | "Evan" | | 5 | "Blackwood" | | 6 | "Silas" |
| | places | | | globalScore | 0.782 | | windowScore | 0.833 | |
| 87.50% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | glossingSentenceCount | 1 | | matches | | 0 | "something like amusement" |
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| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 2.448 | | wordCount | 817 | | matches | | 0 | "Not just at leaving Evan, not just at leaving Cardiff, but at leaving *herself*" | | 1 | "not just at leaving Cardiff, but at leaving *herself*" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 28.17 | | std | 23.42 | | cv | 0.831 | | sampleLengths | | 0 | 87 | | 1 | 5 | | 2 | 71 | | 3 | 13 | | 4 | 54 | | 5 | 21 | | 6 | 16 | | 7 | 36 | | 8 | 9 | | 9 | 42 | | 10 | 19 | | 11 | 7 | | 12 | 95 | | 13 | 17 | | 14 | 8 | | 15 | 24 | | 16 | 28 | | 17 | 23 | | 18 | 25 | | 19 | 18 | | 20 | 13 | | 21 | 20 | | 22 | 12 | | 23 | 48 | | 24 | 16 | | 25 | 13 | | 26 | 55 | | 27 | 7 | | 28 | 15 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 61 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 125 | | matches | (empty) | |
| 32.97% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 78 | | ratio | 0.038 | | matches | | 0 | "Aurora Carter—Rory, as she preferred—stood at the bar, her fingers tracing the rim of her glass as she watched the door." | | 1 | "His grey-streaked auburn hair was neatly combed, his beard trimmed just so, but his eyes—hazel, sharp as a blade—locked onto hers across the bar." | | 2 | "He studied her for a long moment, his gaze lingering on the small crescent scar on her left wrist—the one she’d gotten as a kid, from a bike accident." |
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| 92.62% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 702 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.04843304843304843 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.017094017094017096 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 10.47 | | std | 7.34 | | cv | 0.701 | | sampleLengths | | 0 | 24 | | 1 | 19 | | 2 | 21 | | 3 | 6 | | 4 | 17 | | 5 | 5 | | 6 | 13 | | 7 | 20 | | 8 | 24 | | 9 | 14 | | 10 | 13 | | 11 | 3 | | 12 | 4 | | 13 | 19 | | 14 | 19 | | 15 | 9 | | 16 | 18 | | 17 | 3 | | 18 | 13 | | 19 | 3 | | 20 | 17 | | 21 | 16 | | 22 | 3 | | 23 | 6 | | 24 | 3 | | 25 | 29 | | 26 | 7 | | 27 | 6 | | 28 | 13 | | 29 | 6 | | 30 | 3 | | 31 | 4 | | 32 | 4 | | 33 | 3 | | 34 | 24 | | 35 | 24 | | 36 | 13 | | 37 | 27 | | 38 | 6 | | 39 | 9 | | 40 | 2 | | 41 | 3 | | 42 | 5 | | 43 | 6 | | 44 | 9 | | 45 | 9 | | 46 | 3 | | 47 | 4 | | 48 | 21 | | 49 | 14 |
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| 39.74% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.2564102564102564 | | totalSentences | 78 | | uniqueOpeners | 20 | |
| 56.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 59 | | matches | | 0 | "Then the door swung open." |
| | ratio | 0.017 | |
| 30.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 59 | | matches | | 0 | "She hadn’t been here in" | | 1 | "He moved with the slow," | | 2 | "His grey-streaked auburn hair was" | | 3 | "he said, his voice low," | | 4 | "She didn’t need to." | | 5 | "She knew him well enough" | | 6 | "she replied, her voice steady" | | 7 | "She turned her glass in" | | 8 | "He studied her for a" | | 9 | "he said finally" | | 10 | "she countered, though the words" | | 11 | "She didn’t deny it." | | 12 | "She hadn’t been." | | 13 | "She worked nights, delivering food" | | 14 | "It was the life she’d" | | 15 | "he repeated, softer this time" | | 16 | "he said, stepping closer" | | 17 | "She didn’t answer." | | 18 | "She didn’t have to." | | 19 | "She didn’t take it." |
| | ratio | 0.475 | |
| 2.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 59 | | matches | | 0 | "The Raven’s Nest hummed with" | | 1 | "The neon green sign above" | | 2 | "Aurora Carter—Rory, as she preferred—stood" | | 3 | "She hadn’t been here in" | | 4 | "Silas Blackwood stepped inside, his" | | 5 | "He moved with the slow," | | 6 | "His grey-streaked auburn hair was" | | 7 | "A beat of silence stretched" | | 8 | "he said, his voice low," | | 9 | "Rory didn’t flinch." | | 10 | "She didn’t need to." | | 11 | "She knew him well enough" | | 12 | "she replied, her voice steady" | | 13 | "Silas exhaled through his nose," | | 14 | "She turned her glass in" | | 15 | "The bar fell silent again," | | 16 | "Silas leaned against the counter," | | 17 | "Rory’s fingers tightened around the" | | 18 | "He studied her for a" | | 19 | "he said finally" |
| | ratio | 0.915 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 93.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 2 | | matches | | 0 | "He moved with the slow, deliberate grace of someone who had spent too many years watching others move too quickly." | | 1 | "She worked nights, delivering food to offices and apartments, her hands always sweaty, her mind racing with the weight of what she’d left behind." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, his voice low, rough with something like amusement" | | 1 | "she countered, though the words tasted bitter" |
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| 75.93% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 2 | | fancyTags | | 0 | "he repeated (repeat)" | | 1 | "she observed (observe)" |
| | dialogueSentences | 27 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.222 | | effectiveRatio | 0.148 | |