| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 126 | | tagDensity | 0.151 | | leniency | 0.302 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.76% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2072 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "precisely" | | 1 | "truly" | | 2 | "quickly" |
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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) | |
| 78.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2072 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "affection" | | 1 | "eyebrow" | | 2 | "uncanny" | | 3 | "could feel" | | 4 | "silence" | | 5 | "footsteps" | | 6 | "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 | 2 | | narrationSentences | 187 | | matches | | 0 | "e with determination" | | 1 | "was afraid" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 187 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 294 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2067 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 34 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 126 | | wordCount | 1512 | | uniqueNames | 12 | | maxNameDensity | 3.24 | | worstName | "Eva" | | maxWindowNameDensity | 7.5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Prague | 1 | | Silas | 17 | | Rory | 47 | | Eva | 49 | | Europe | 1 | | Evan | 2 | | Cardiff | 1 | | London | 2 | | Rain | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Rory" | | 4 | "Eva" | | 5 | "Evan" | | 6 | "Rain" |
| | places | | 0 | "Soho" | | 1 | "Prague" | | 2 | "Europe" | | 3 | "Cardiff" | | 4 | "London" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 103 | | glossingSentenceCount | 1 | | matches | | 0 | "as if daring the world to make her less visible" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.484 | | wordCount | 2067 | | matches | | 0 | "not dramatically, but enough" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 4 | | totalSentences | 294 | | matches | | 0 | "learned that haste" | | 1 | "written that she" | | 2 | "hated that she" | | 3 | "Hated that some" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 191 | | mean | 10.82 | | std | 12.68 | | cv | 1.172 | | sampleLengths | | 0 | 45 | | 1 | 59 | | 2 | 4 | | 3 | 3 | | 4 | 5 | | 5 | 5 | | 6 | 6 | | 7 | 47 | | 8 | 18 | | 9 | 24 | | 10 | 11 | | 11 | 57 | | 12 | 9 | | 13 | 1 | | 14 | 11 | | 15 | 4 | | 16 | 51 | | 17 | 4 | | 18 | 1 | | 19 | 22 | | 20 | 2 | | 21 | 5 | | 22 | 62 | | 23 | 5 | | 24 | 8 | | 25 | 5 | | 26 | 6 | | 27 | 10 | | 28 | 6 | | 29 | 7 | | 30 | 18 | | 31 | 23 | | 32 | 4 | | 33 | 6 | | 34 | 1 | | 35 | 1 | | 36 | 1 | | 37 | 57 | | 38 | 16 | | 39 | 12 | | 40 | 7 | | 41 | 2 | | 42 | 1 | | 43 | 4 | | 44 | 8 | | 45 | 4 | | 46 | 3 | | 47 | 5 | | 48 | 15 | | 49 | 14 |
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| 99.63% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 187 | | matches | | 0 | "been told" | | 1 | "been nineteen" | | 2 | "was creased" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 299 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 294 | | ratio | 0.014 | | matches | | 0 | "Rain darkened the shoulders of her camel coat, and her blond hair—once long, once perpetually escaping whatever tie she put it in—had been cut close around her jaw." | | 1 | "Rory had seen him move through crowded rooms in other ways, too—never fast, never careless, always arriving precisely where he meant to be." | | 2 | "Eva’s face broke—not dramatically, but enough." | | 3 | "There it was—the old glance, the silent plea, the promise of a secret shared later." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1521 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 57 | | adverbRatio | 0.03747534516765286 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.007232084155161078 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 294 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 294 | | mean | 7.03 | | std | 5.55 | | cv | 0.789 | | sampleLengths | | 0 | 17 | | 1 | 28 | | 2 | 5 | | 3 | 18 | | 4 | 14 | | 5 | 22 | | 6 | 4 | | 7 | 3 | | 8 | 5 | | 9 | 5 | | 10 | 6 | | 11 | 7 | | 12 | 25 | | 13 | 15 | | 14 | 4 | | 15 | 8 | | 16 | 6 | | 17 | 4 | | 18 | 6 | | 19 | 14 | | 20 | 11 | | 21 | 7 | | 22 | 28 | | 23 | 22 | | 24 | 9 | | 25 | 1 | | 26 | 11 | | 27 | 4 | | 28 | 19 | | 29 | 8 | | 30 | 24 | | 31 | 4 | | 32 | 1 | | 33 | 9 | | 34 | 13 | | 35 | 2 | | 36 | 5 | | 37 | 9 | | 38 | 4 | | 39 | 16 | | 40 | 17 | | 41 | 16 | | 42 | 5 | | 43 | 8 | | 44 | 5 | | 45 | 3 | | 46 | 3 | | 47 | 5 | | 48 | 5 | | 49 | 6 |
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| 45.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.24149659863945577 | | totalSentences | 294 | | uniqueOpeners | 71 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 10 | | totalSentences | 157 | | matches | | 0 | "Then Eva’s expression changed." | | 1 | "Once, Rory would have noticed" | | 2 | "Then Evan had become impossible" | | 3 | "Then a month." | | 4 | "Then the silence had become" | | 5 | "Perhaps because she had spent" | | 6 | "Somewhere in the back room," | | 7 | "Only there would be no" | | 8 | "Then she placed the letter" | | 9 | "Then Eva stepped through the" |
| | ratio | 0.064 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 157 | | matches | | 0 | "Her straight black hair had" | | 1 | "His hazel eyes softened by" | | 2 | "She smiled despite herself." | | 3 | "He could make affection sound" | | 4 | "She had just turned toward" | | 5 | "She paused beneath the green" | | 6 | "Her face had sharpened." | | 7 | "She had once dressed in" | | 8 | "He had the uncanny stillness" | | 9 | "His slight limp showed when" | | 10 | "She chose the stool beside" | | 11 | "She accepted the pain without" | | 12 | "His eyes lingered on Rory." | | 13 | "She gave the smallest shake" | | 14 | "He always did." | | 15 | "He moved toward the bookshelves" | | 16 | "They had been nineteen when" | | 17 | "They had built a private" | | 18 | "She had pressed it into" | | 19 | "I’ll follow you." |
| | ratio | 0.217 | |
| 45.99% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 130 | | totalSentences | 157 | | matches | | 0 | "Rain silvered the windows of" | | 1 | "Rory came in through the" | | 2 | "The bar was almost empty." | | 3 | "A man in a charcoal" | | 4 | "Rory set the bag on" | | 5 | "Her straight black hair had" | | 6 | "Silas glanced at her." | | 7 | "His hazel eyes softened by" | | 8 | "She smiled despite herself." | | 9 | "That was the trouble with" | | 10 | "He could make affection sound" | | 11 | "She had just turned toward" | | 12 | "The woman who entered carried" | | 13 | "Rain darkened the shoulders of" | | 14 | "She paused beneath the green" | | 15 | "Rory knew her before the" | | 16 | "The name struck with the" | | 17 | "Eva saw her too." | | 18 | "Rory felt, absurdly, the old" | | 19 | "Eva had always appeared at" |
| | ratio | 0.828 | |
| 95.54% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 157 | | matches | | 0 | "Now everything about her was" | | 1 | "Now she noticed and kept" | | 2 | "Before the shelf closed, he" |
| | ratio | 0.019 | |
| 86.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 4 | | matches | | 0 | "She paused beneath the green neon, looking into the room as though she had arrived somewhere she remembered but no longer trusted." | | 1 | "Eva had always appeared at the exact moment a room became unbearable, flashing that crooked smile and announcing a solution nobody had asked for." | | 2 | "He had the uncanny stillness of a man who could hear the machinery beneath a conversation." | | 3 | "She recognized it immediately: slanted, impatient, every letter leaning forward as if it might outrun the sentence." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 19 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 126 | | tagDensity | 0.151 | | leniency | 0.302 | | rawRatio | 0 | | effectiveRatio | 0 | |