| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 21 | | tagDensity | 0.476 | | leniency | 0.952 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.08% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1262 | | totalAiIsmAdverbs | 2 | | 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) | |
| 64.34% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1262 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "echoes" | | 1 | "silk" | | 2 | "lilt" | | 3 | "eyebrow" | | 4 | "glistening" | | 5 | "tension" | | 6 | "glinting" | | 7 | "warmth" |
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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 | 50 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 50 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 62 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 60 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1262 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 888 | | uniqueNames | 16 | | maxNameDensity | 2.25 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | Golden | 2 | | Empress | 2 | | Eva | 14 | | Cardiff | 2 | | Rory | 20 | | Evan | 1 | | Silas | 2 | | Thames | 1 | | Soho | 1 | | Blur | 1 | | Song | 1 | | Marlow | 1 | | Senior | 1 | | Associate | 1 | | Hale | 1 | | Croft | 1 |
| | persons | | 0 | "Eva" | | 1 | "Rory" | | 2 | "Evan" | | 3 | "Silas" | | 4 | "Marlow" |
| | places | | 0 | "Cardiff" | | 1 | "Thames" | | 2 | "Soho" | | 3 | "Blur" |
| | globalScore | 0.374 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | 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 | 1262 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 62 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 60.1 | | std | 33.03 | | cv | 0.55 | | sampleLengths | | 0 | 81 | | 1 | 160 | | 2 | 31 | | 3 | 35 | | 4 | 96 | | 5 | 17 | | 6 | 53 | | 7 | 34 | | 8 | 65 | | 9 | 70 | | 10 | 50 | | 11 | 84 | | 12 | 53 | | 13 | 71 | | 14 | 42 | | 15 | 113 | | 16 | 25 | | 17 | 50 | | 18 | 19 | | 19 | 57 | | 20 | 56 |
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| 91.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 50 | | matches | | 0 | "is pulled" | | 1 | "are printed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 149 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 62 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 599 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.02003338898163606 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.00333889816360601 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 62 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 62 | | mean | 20.35 | | std | 11.8 | | cv | 0.58 | | sampleLengths | | 0 | 27 | | 1 | 23 | | 2 | 31 | | 3 | 32 | | 4 | 34 | | 5 | 35 | | 6 | 17 | | 7 | 23 | | 8 | 19 | | 9 | 31 | | 10 | 17 | | 11 | 18 | | 12 | 24 | | 13 | 29 | | 14 | 36 | | 15 | 7 | | 16 | 8 | | 17 | 9 | | 18 | 27 | | 19 | 26 | | 20 | 12 | | 21 | 22 | | 22 | 5 | | 23 | 60 | | 24 | 27 | | 25 | 30 | | 26 | 13 | | 27 | 7 | | 28 | 43 | | 29 | 7 | | 30 | 42 | | 31 | 35 | | 32 | 18 | | 33 | 35 | | 34 | 12 | | 35 | 39 | | 36 | 20 | | 37 | 14 | | 38 | 28 | | 39 | 23 | | 40 | 16 | | 41 | 16 | | 42 | 34 | | 43 | 15 | | 44 | 9 | | 45 | 11 | | 46 | 10 | | 47 | 4 | | 48 | 17 | | 49 | 18 |
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| 38.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3064516129032258 | | totalSentences | 62 | | uniqueOpeners | 19 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 49 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 49 | | matches | | 0 | "She slings the crumpled Golden" | | 1 | "Her hair, once streaked with" | | 2 | "She stops just inside the" | | 3 | "Her knees brush Rory’s under" | | 4 | "She pauses, her fingers hovering" | | 5 | "She trails off, not needing" | | 6 | "She cuts herself off, her" | | 7 | "He nods at Eva, his" | | 8 | "Her tone is dry, but" | | 9 | "He gives Rory a quick," | | 10 | "She pauses, her gaze darting" | | 11 | "She reaches into her blazer" | | 12 | "She reaches down, fumbling with" |
| | ratio | 0.265 | |
| 0.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 49 | | matches | | 0 | "She slings the crumpled Golden" | | 1 | "The crescent scar on her" | | 2 | "The soft clink of ice" | | 3 | "The woman stepping through the" | | 4 | "This Eva wears a tailored" | | 5 | "Her hair, once streaked with" | | 6 | "She stops just inside the" | | 7 | "The voice is warm, laced" | | 8 | "Rory sets the glass down," | | 9 | "Eva laughs, a tight, bright" | | 10 | "Her knees brush Rory’s under" | | 11 | "She pauses, her fingers hovering" | | 12 | "Rory raises an eyebrow, nodding" | | 13 | "Eva’s smile fades a little," | | 14 | "Rory tugs her sleeve down," | | 15 | "She trails off, not needing" | | 16 | "Eva nods, her jaw tight." | | 17 | "She cuts herself off, her" | | 18 | "He nods at Eva, his" | | 19 | "Eva stands, extending a perfectly" |
| | ratio | 0.918 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 49 | | matches | | 0 | "Just as she reaches for" |
| | ratio | 0.02 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 2 | | matches | | 0 | "This Eva wears a tailored charcoal blazer over a cream silk blouse, her straight leather trousers pressed so sharp they could cut paper, heels that click on the…" | | 1 | "Her hair, once streaked with pink and blue, is pulled into a neat chestnut bun, her nails painted a muted beige that matches the tiny lapel pin of a university …" |
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| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 1 | | matches | | 0 | "She cuts, her eyes glistening, and she quickly blinks them away" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |