| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva leaned back [back]" |
| | dialogueSentences | 62 | | tagDensity | 0.081 | | leniency | 0.161 | | rawRatio | 0.2 | | effectiveRatio | 0.032 | |
| 82.52% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1430 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "perfectly" | | 1 | "very" | | 2 | "really" |
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| 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) | |
| 23.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1430 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "shattered" | | 1 | "crystalline" | | 2 | "reminder" | | 3 | "chaotic" | | 4 | "silk" | | 5 | "scanning" | | 6 | "crystal" | | 7 | "silence" | | 8 | "sanctuary" | | 9 | "variables" | | 10 | "variable" | | 11 | "predictable" | | 12 | "electric" | | 13 | "tension" | | 14 | "flicker" | | 15 | "weight" | | 16 | "predator" | | 17 | "trembled" |
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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 | 72 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 72 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 130 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 1429 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 802 | | uniqueNames | 7 | | maxNameDensity | 1.87 | | worstName | "Eva" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Cardiff | 1 | | Mayfair | 1 | | Eva | 15 | | Aurora | 15 | | Raven | 1 | | Nest | 1 | | London | 1 |
| | persons | | | places | | | globalScore | 0.565 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | 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 | 1429 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 130 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 85 | | mean | 16.81 | | std | 15.21 | | cv | 0.905 | | sampleLengths | | 0 | 19 | | 1 | 56 | | 2 | 11 | | 3 | 50 | | 4 | 2 | | 5 | 69 | | 6 | 1 | | 7 | 9 | | 8 | 23 | | 9 | 3 | | 10 | 9 | | 11 | 8 | | 12 | 27 | | 13 | 3 | | 14 | 13 | | 15 | 32 | | 16 | 4 | | 17 | 6 | | 18 | 7 | | 19 | 20 | | 20 | 19 | | 21 | 26 | | 22 | 2 | | 23 | 14 | | 24 | 44 | | 25 | 7 | | 26 | 21 | | 27 | 5 | | 28 | 5 | | 29 | 73 | | 30 | 6 | | 31 | 4 | | 32 | 22 | | 33 | 14 | | 34 | 8 | | 35 | 8 | | 36 | 8 | | 37 | 6 | | 38 | 18 | | 39 | 8 | | 40 | 20 | | 41 | 19 | | 42 | 43 | | 43 | 5 | | 44 | 2 | | 45 | 5 | | 46 | 33 | | 47 | 11 | | 48 | 7 | | 49 | 32 |
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| 95.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 72 | | matches | | 0 | "been told" | | 1 | "was preserved" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 126 | | matches | | 0 | "were following" | | 1 | "weren't looking" | | 2 | "were looking" | | 3 | "were looking" |
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| 98.90% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 130 | | ratio | 0.015 | | matches | | 0 | "She didn't walk; she glided, her movements economical and practiced." | | 1 | "Eva's expression faltered, a flicker of something raw and ancient crossing her face—regret, perhaps, or the simple, crushing weight of loneliness." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 801 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, crescent-shaped scar" |
| | adverbCount | 18 | | adverbRatio | 0.02247191011235955 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004993757802746567 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 130 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 130 | | mean | 10.99 | | std | 7.32 | | cv | 0.666 | | sampleLengths | | 0 | 19 | | 1 | 3 | | 2 | 16 | | 3 | 16 | | 4 | 21 | | 5 | 11 | | 6 | 17 | | 7 | 2 | | 8 | 4 | | 9 | 27 | | 10 | 2 | | 11 | 30 | | 12 | 23 | | 13 | 16 | | 14 | 1 | | 15 | 9 | | 16 | 3 | | 17 | 10 | | 18 | 10 | | 19 | 3 | | 20 | 9 | | 21 | 8 | | 22 | 2 | | 23 | 4 | | 24 | 21 | | 25 | 3 | | 26 | 13 | | 27 | 23 | | 28 | 9 | | 29 | 4 | | 30 | 6 | | 31 | 7 | | 32 | 2 | | 33 | 5 | | 34 | 13 | | 35 | 19 | | 36 | 8 | | 37 | 10 | | 38 | 8 | | 39 | 2 | | 40 | 14 | | 41 | 26 | | 42 | 18 | | 43 | 7 | | 44 | 21 | | 45 | 5 | | 46 | 5 | | 47 | 14 | | 48 | 17 | | 49 | 30 |
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| 37.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.24615384615384617 | | totalSentences | 130 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 65 | | matches | (empty) | | ratio | 0 | |
| 60.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 65 | | matches | | 0 | "She simply watched a single" | | 1 | "She reached for her napkin," | | 2 | "It felt tight, a ghost" | | 3 | "She knew that cadence." | | 4 | "It was a melody she" | | 5 | "Her hair, once a chaotic" | | 6 | "She wore a tailored navy" | | 7 | "She didn't walk; she glided," | | 8 | "She smelled of expensive jasmine" | | 9 | "She didn't ask permission." | | 10 | "She occupied the space with" | | 11 | "It wasn't a warm expression." | | 12 | "It was a display of" | | 13 | "She gripped her glass, the" | | 14 | "She set the glass down" | | 15 | "It was a sanctuary of" | | 16 | "She smoothed the silk of" | | 17 | "She turned and walked away," | | 18 | "She took a long, slow" | | 19 | "He didn't look at the" |
| | ratio | 0.4 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 65 | | matches | | 0 | "The glass shattered against the" | | 1 | "Aurora didn't flinch." | | 2 | "She simply watched a single" | | 3 | "She reached for her napkin," | | 4 | "It felt tight, a ghost" | | 5 | "The voice cut through the" | | 6 | "She knew that cadence." | | 7 | "It was a melody she" | | 8 | "Eva stood by the mahogany" | | 9 | "Her hair, once a chaotic" | | 10 | "She wore a tailored navy" | | 11 | "Eva moved closer." | | 12 | "She didn't walk; she glided," | | 13 | "She smelled of expensive jasmine" | | 14 | "Aurora gestured to the empty" | | 15 | "She didn't ask permission." | | 16 | "She occupied the space with" | | 17 | "Eva leaned in, her eyes" | | 18 | "It wasn't a warm expression." | | 19 | "It was a display of" |
| | ratio | 0.938 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 65 | | matches | (empty) | | ratio | 0 | |
| 46.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 5 | | matches | | 0 | "The glass shattered against the floor, a crystalline explosion that died instantly in the low hum of the pub." | | 1 | "Eva stood by the mahogany pillar, looking less like the girl who had once shared cheap cider and whispered secrets in a university library and more like a polis…" | | 2 | "Her hair, once a chaotic nest of blonde curls, was now a sleek, expensive bob that caught the amber light of the bar." | | 3 | "She wore a tailored navy silk blouse that screamed of Mayfair boutiques and silent, predatory confidence." | | 4 | "Aurora watched her go, watching the way the crowd parted for her, as if she were a predator moving through a herd of grazers." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva reached out, her manicured finger hovering near Aurora's wrist, near the scar" | | 1 | "Eva leaned back, her posture returning to that impenetrable, elegant shield" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |