| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.317 | | leniency | 0.634 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.38% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1380 | | 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) | |
| 31.16% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1380 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "dancing" | | 1 | "predictable" | | 2 | "crystal" | | 3 | "rhythmic" | | 4 | "pristine" | | 5 | "weight" | | 6 | "tension" | | 7 | "navigate" | | 8 | "navigated" | | 9 | "chill" | | 10 | "traced" | | 11 | "silence" | | 12 | "flawless" | | 13 | "whisper" | | 14 | "sanctuary" | | 15 | "mechanical" | | 16 | "vibrated" |
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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 | 77 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 77 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1380 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 914 | | uniqueNames | 13 | | maxNameDensity | 2.08 | | worstName | "Chloe" | | maxWindowNameDensity | 4 | | worstWindowName | "Chloe" | | discoveredNames | | Raven | 1 | | Nest | 1 | | London | 2 | | Rolex | 2 | | Rory | 16 | | Cardiff | 1 | | Silas | 5 | | Irish | 1 | | Chloe | 19 | | Savile | 1 | | Row | 1 | | Welsh | 1 | | Bakerloo | 1 |
| | persons | | 0 | "Rory" | | 1 | "Silas" | | 2 | "Chloe" | | 3 | "Row" |
| | places | | 0 | "Raven" | | 1 | "London" | | 2 | "Cardiff" |
| | globalScore | 0.461 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | 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 | 1380 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 27.6 | | std | 22.24 | | cv | 0.806 | | sampleLengths | | 0 | 116 | | 1 | 60 | | 2 | 13 | | 3 | 24 | | 4 | 20 | | 5 | 1 | | 6 | 32 | | 7 | 1 | | 8 | 34 | | 9 | 7 | | 10 | 4 | | 11 | 25 | | 12 | 61 | | 13 | 15 | | 14 | 33 | | 15 | 34 | | 16 | 7 | | 17 | 45 | | 18 | 19 | | 19 | 21 | | 20 | 35 | | 21 | 34 | | 22 | 39 | | 23 | 43 | | 24 | 7 | | 25 | 36 | | 26 | 4 | | 27 | 59 | | 28 | 34 | | 29 | 47 | | 30 | 12 | | 31 | 7 | | 32 | 20 | | 33 | 5 | | 34 | 39 | | 35 | 52 | | 36 | 2 | | 37 | 28 | | 38 | 65 | | 39 | 19 | | 40 | 11 | | 41 | 68 | | 42 | 16 | | 43 | 36 | | 44 | 7 | | 45 | 3 | | 46 | 40 | | 47 | 13 | | 48 | 16 | | 49 | 11 |
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| 96.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 77 | | matches | | 0 | "was cropped" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 154 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 105 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 920 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 14 | | adverbRatio | 0.015217391304347827 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006521739130434782 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 13.14 | | std | 8.6 | | cv | 0.655 | | sampleLengths | | 0 | 15 | | 1 | 19 | | 2 | 20 | | 3 | 9 | | 4 | 21 | | 5 | 13 | | 6 | 19 | | 7 | 8 | | 8 | 13 | | 9 | 17 | | 10 | 10 | | 11 | 12 | | 12 | 3 | | 13 | 10 | | 14 | 3 | | 15 | 11 | | 16 | 6 | | 17 | 4 | | 18 | 5 | | 19 | 15 | | 20 | 1 | | 21 | 7 | | 22 | 25 | | 23 | 1 | | 24 | 11 | | 25 | 23 | | 26 | 7 | | 27 | 4 | | 28 | 19 | | 29 | 6 | | 30 | 6 | | 31 | 11 | | 32 | 20 | | 33 | 24 | | 34 | 6 | | 35 | 9 | | 36 | 16 | | 37 | 17 | | 38 | 14 | | 39 | 20 | | 40 | 7 | | 41 | 28 | | 42 | 17 | | 43 | 9 | | 44 | 10 | | 45 | 6 | | 46 | 15 | | 47 | 18 | | 48 | 17 | | 49 | 34 |
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| 36.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.26666666666666666 | | totalSentences | 105 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 72.63% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 76 | | matches | | 0 | "Her bright blue eyes tracked" | | 1 | "His silver signet ring clinked" | | 2 | "She shook a pristine beige" | | 3 | "Her blonde hair was cropped" | | 4 | "Her gaze swept over the" | | 5 | "Her eyes landed on the" | | 6 | "They locked onto Rory." | | 7 | "She took a step forward," | | 8 | "Her thumb rubbed the small" | | 9 | "She looked at the scuffed" | | 10 | "He picked up a bottle" | | 11 | "His slight limp barely registered" | | 12 | "He placed the glasses between" | | 13 | "She kept her hands clasped" | | 14 | "She took a sip, wincing" | | 15 | "She picked up her own" | | 16 | "She rubbed her temples, smudging" | | 17 | "He carried a fresh bottle" | | 18 | "He set them on the" | | 19 | "He gave Rory a subtle" |
| | ratio | 0.368 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 76 | | matches | | 0 | "The green neon sign buzzed," | | 1 | "Rory dragged a damp cloth" | | 2 | "Her bright blue eyes tracked" | | 3 | "Silas stood at the far" | | 4 | "His silver signet ring clinked" | | 5 | "The heavy oak door groaned" | | 6 | "A woman stepped out of" | | 7 | "She shook a pristine beige" | | 8 | "Her blonde hair was cropped" | | 9 | "A gold Rolex caught the" | | 10 | "Rory stopped wiping." | | 11 | "The cloth slipped from her" | | 12 | "The woman turned." | | 13 | "Her gaze swept over the" | | 14 | "Her eyes landed on the" | | 15 | "They locked onto Rory." | | 16 | "The stranger’s jaw went slack." | | 17 | "She took a step forward," | | 18 | "Rory gripped the edge of" | | 19 | "Her thumb rubbed the small" |
| | ratio | 0.974 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 1 | | matches | | 0 | "The fierce, idealistic girl who used to eat instant noodles and draft policy briefs until dawn was gone, eroded by billable hours and boardroom compromises." |
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| 9.62% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 3 | | matches | | 0 | "Chloe gestured, her manicured hand slicing through the dim air" | | 1 | "Chloe leaned, the polished veneer cracking just a fraction" | | 2 | "Chloe reached out, her fingers hovering over Rory’s hand before pulling back" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 41 | | tagDensity | 0.049 | | leniency | 0.098 | | rawRatio | 0.5 | | effectiveRatio | 0.049 | |