| 87.50% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 3 | | adverbTags | | 0 | "The correction came automatically [automatically]" | | 1 | "he said finally [finally]" | | 2 | "Marcus said quietly [quietly]" |
| | dialogueSentences | 48 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.167 | | effectiveRatio | 0.125 | |
| 96.18% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1309 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 60.00% | 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) | |
| 57.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1309 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "familiar" | | 1 | "traced" | | 2 | "comfortable" | | 3 | "unspoken" | | 4 | "could feel" | | 5 | "perfect" | | 6 | "footsteps" | | 7 | "weight" |
| |
| 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 | 76 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 76 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1295 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1073 | | uniqueNames | 14 | | maxNameDensity | 1.21 | | worstName | "Marcus" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Marcus" | | discoveredNames | | Rory | 11 | | Raven | 1 | | Nest | 1 | | October | 1 | | Soho | 2 | | Marcus | 13 | | Donnelly | 1 | | Cardiff | 2 | | Chinese | 1 | | Seven | 1 | | Silas | 3 | | Evan | 1 | | London | 2 | | Eva | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Marcus" | | 4 | "Donnelly" | | 5 | "Silas" | | 6 | "Evan" | | 7 | "Eva" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0.894 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1295 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 24.9 | | std | 20.35 | | cv | 0.817 | | sampleLengths | | 0 | 72 | | 1 | 21 | | 2 | 23 | | 3 | 47 | | 4 | 1 | | 5 | 32 | | 6 | 4 | | 7 | 67 | | 8 | 8 | | 9 | 34 | | 10 | 50 | | 11 | 39 | | 12 | 13 | | 13 | 27 | | 14 | 5 | | 15 | 47 | | 16 | 31 | | 17 | 9 | | 18 | 28 | | 19 | 12 | | 20 | 40 | | 21 | 27 | | 22 | 1 | | 23 | 14 | | 24 | 26 | | 25 | 7 | | 26 | 10 | | 27 | 64 | | 28 | 9 | | 29 | 16 | | 30 | 2 | | 31 | 15 | | 32 | 2 | | 33 | 4 | | 34 | 17 | | 35 | 40 | | 36 | 9 | | 37 | 11 | | 38 | 18 | | 39 | 42 | | 40 | 10 | | 41 | 22 | | 42 | 2 | | 43 | 2 | | 44 | 19 | | 45 | 6 | | 46 | 16 | | 47 | 60 | | 48 | 47 | | 49 | 59 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 76 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 180 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 11 | | totalSentences | 106 | | ratio | 0.104 | | matches | | 0 | "The Raven's Nest wrapped around her like an old coat—comfortable, worn, smelling faintly of leather and smoke despite the ban years ago." | | 1 | "Rory didn't look up—she'd learned that making eye contact in bars invited conversation, and tonight she wanted nothing more than to dissolve into the worn leather of her barstool." | | 2 | "But no—there stood Marcus Donnelly, all six feet of him, broader now through the shoulders, his university softness hardened into something more deliberate." | | 3 | "He moved closer, and she caught his scent—different now, expensive cologne replacing the cheap body spray he'd favored at nineteen." | | 4 | "Not forceful—Marcus had never been forceful—but heavy with unspoken please." | | 5 | "Because that's what she'd been then—playing at being the dutiful law student, the perfect fiancée, the daughter following in her father's footsteps." | | 6 | "No spark, no electricity—just the warm, sad touch of shared history." | | 7 | "That was the thing about Marcus—he'd always meant what he said." | | 8 | "Six years ago, she'd run from the life he represented—the mapped-out future, the weight of expectations, the suffocating predictability of it all." | | 9 | "But she'd keep the card anyway, tucked into the drawer with her father's old letters and the crescent moon scar on her wrist—reminders that the past, no matter how far you ran, always found a way to circle back." | | 10 | "Through her window, she could see the London she'd dreamed of at university—lights stretching endlessly, promises humming in the autumn air." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1087 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 43 | | adverbRatio | 0.03955841766329347 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.012879484820607176 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 12.22 | | std | 8.97 | | cv | 0.734 | | sampleLengths | | 0 | 25 | | 1 | 22 | | 2 | 25 | | 3 | 21 | | 4 | 13 | | 5 | 10 | | 6 | 18 | | 7 | 29 | | 8 | 1 | | 9 | 7 | | 10 | 18 | | 11 | 7 | | 12 | 4 | | 13 | 16 | | 14 | 23 | | 15 | 28 | | 16 | 8 | | 17 | 20 | | 18 | 14 | | 19 | 45 | | 20 | 5 | | 21 | 5 | | 22 | 34 | | 23 | 4 | | 24 | 9 | | 25 | 17 | | 26 | 10 | | 27 | 5 | | 28 | 16 | | 29 | 25 | | 30 | 6 | | 31 | 16 | | 32 | 6 | | 33 | 9 | | 34 | 9 | | 35 | 7 | | 36 | 21 | | 37 | 10 | | 38 | 2 | | 39 | 29 | | 40 | 11 | | 41 | 4 | | 42 | 23 | | 43 | 1 | | 44 | 9 | | 45 | 5 | | 46 | 8 | | 47 | 16 | | 48 | 2 | | 49 | 7 |
| |
| 60.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4056603773584906 | | totalSentences | 106 | | uniqueOpeners | 43 | |
| 43.86% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 76 | | matches | | | ratio | 0.013 | |
| 83.16% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 76 | | matches | | 0 | "She'd been living upstairs for" | | 1 | "She knew it instantly, though" | | 2 | "Her fingers tightened around the" | | 3 | "She turned slowly, as if" | | 4 | "He moved closer, and she" | | 5 | "His suit probably cost more" | | 6 | "He smiled, and for a" | | 7 | "She settled back onto her" | | 8 | "He had the grace to" | | 9 | "He studied her, and she" | | 10 | "He paused, rolling his glass" | | 11 | "She took a long pull" | | 12 | "he said finally" | | 13 | "His voice held no recrimination," | | 14 | "He finished his whiskey in" | | 15 | "She'd worn those roles like" | | 16 | "She took the card, her" | | 17 | "He stood as well, towering" | | 18 | "She believed him." | | 19 | "It had been one of" |
| | ratio | 0.342 | |
| 65.26% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 76 | | matches | | 0 | "The familiar burn of whiskey" | | 1 | "The Raven's Nest wrapped around" | | 2 | "She'd been living upstairs for" | | 3 | "Silas wiped down the bar" | | 4 | "Rory pushed the glass away," | | 5 | "The door chimed, bringing with" | | 6 | "Rory didn't look up—she'd learned" | | 7 | "The voice hit her like" | | 8 | "She knew it instantly, though" | | 9 | "Her fingers tightened around the" | | 10 | "She turned slowly, as if" | | 11 | "The boyish face she remembered" | | 12 | "The name felt foreign on" | | 13 | "He moved closer, and she" | | 14 | "His suit probably cost more" | | 15 | "He smiled, and for a" | | 16 | "The correction came automatically" | | 17 | "Marcus flagged Silas down." | | 18 | "Rory started to rise, but" | | 19 | "Silas set down their glasses" |
| | ratio | 0.789 | |
| 65.79% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 76 | | matches | | 0 | "Because that's what she'd been" |
| | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 48 | | tagDensity | 0.042 | | leniency | 0.083 | | rawRatio | 0 | | effectiveRatio | 0 | |