| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.029 | | leniency | 0.057 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1576 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "sharply" | | 1 | "completely" | | 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) | |
| 33.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1576 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "electric" | | 1 | "familiar" | | 2 | "scanned" | | 3 | "rhythmic" | | 4 | "weight" | | 5 | "amidst" | | 6 | "crystal" | | 7 | "flicker" | | 8 | "tension" | | 9 | "fleeting" | | 10 | "pulse" | | 11 | "unreadable" | | 12 | "disrupt" | | 13 | "flicked" | | 14 | "calculating" | | 15 | "silence" | | 16 | "reminder" | | 17 | "traced" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "sent a shiver through" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 53 | | matches | (empty) | |
| 88.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 53 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 87 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1572 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.58% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 812 | | uniqueNames | 8 | | maxNameDensity | 1.11 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | Julian | 3 | | Cardiff | 1 | | Silas | 8 | | Aurora | 9 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Julian" | | 3 | "Silas" | | 4 | "Aurora" |
| | places | | | globalScore | 0.946 | | windowScore | 1 | |
| 96.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like someone who had spent his adu" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.636 | | wordCount | 1572 | | matches | | 0 | "Not that he's looking, but the questions circulate" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 29.66 | | std | 19.3 | | cv | 0.651 | | sampleLengths | | 0 | 83 | | 1 | 16 | | 2 | 59 | | 3 | 15 | | 4 | 13 | | 5 | 113 | | 6 | 5 | | 7 | 71 | | 8 | 20 | | 9 | 37 | | 10 | 38 | | 11 | 21 | | 12 | 46 | | 13 | 26 | | 14 | 35 | | 15 | 30 | | 16 | 53 | | 17 | 17 | | 18 | 21 | | 19 | 19 | | 20 | 35 | | 21 | 29 | | 22 | 29 | | 23 | 14 | | 24 | 22 | | 25 | 13 | | 26 | 34 | | 27 | 31 | | 28 | 19 | | 29 | 26 | | 30 | 12 | | 31 | 28 | | 32 | 57 | | 33 | 42 | | 34 | 40 | | 35 | 29 | | 36 | 15 | | 37 | 23 | | 38 | 22 | | 39 | 20 | | 40 | 28 | | 41 | 42 | | 42 | 24 | | 43 | 36 | | 44 | 17 | | 45 | 8 | | 46 | 22 | | 47 | 22 | | 48 | 14 | | 49 | 16 |
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| 98.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 53 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 116 | | matches | (empty) | |
| 44.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 87 | | ratio | 0.034 | | matches | | 0 | "He didn't look like the man who played rugby in the quad; he looked like someone who had spent his adult life erasing people from existence for the highest bidder." | | 1 | "The ambient noise of the bar—the hum of the fridge, the distant murmur of street traffic—seemed to drop away." | | 2 | "The bell above the door chimed—a bright, incongruous sound—as he stepped out into the night." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 820 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.015853658536585366 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007317073170731708 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 87 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 87 | | mean | 18.07 | | std | 9.13 | | cv | 0.505 | | sampleLengths | | 0 | 26 | | 1 | 15 | | 2 | 7 | | 3 | 15 | | 4 | 20 | | 5 | 16 | | 6 | 15 | | 7 | 17 | | 8 | 27 | | 9 | 15 | | 10 | 13 | | 11 | 24 | | 12 | 24 | | 13 | 14 | | 14 | 22 | | 15 | 29 | | 16 | 5 | | 17 | 9 | | 18 | 30 | | 19 | 16 | | 20 | 16 | | 21 | 20 | | 22 | 37 | | 23 | 11 | | 24 | 11 | | 25 | 16 | | 26 | 21 | | 27 | 46 | | 28 | 26 | | 29 | 35 | | 30 | 30 | | 31 | 5 | | 32 | 21 | | 33 | 27 | | 34 | 17 | | 35 | 21 | | 36 | 19 | | 37 | 18 | | 38 | 17 | | 39 | 29 | | 40 | 29 | | 41 | 3 | | 42 | 11 | | 43 | 22 | | 44 | 13 | | 45 | 34 | | 46 | 5 | | 47 | 8 | | 48 | 18 | | 49 | 19 |
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| 39.66% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.26436781609195403 | | totalSentences | 87 | | uniqueOpeners | 23 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 46.42% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 53 | | matches | | 0 | "She pushed the heavy oak" | | 1 | "She scanned the room, looking" | | 2 | "He dragged his left leg," | | 3 | "He twirled the heavy silver" | | 4 | "He wore a crisp, tailored" | | 5 | "He turned, his face caught" | | 6 | "It was Julian, her former" | | 7 | "He stood, the chair scraping" | | 8 | "He didn't look like the" | | 9 | "She took a half-step back," | | 10 | "He set his glass down," | | 11 | "He didn't blink when he" | | 12 | "He took a step closer." | | 13 | "He looked down at the" | | 14 | "He leaned off the bar," | | 15 | "He adjusted his cuff, the" | | 16 | "She forced herself to stay" | | 17 | "She moved past him, her" | | 18 | "She walked to the bar," | | 19 | "He flicked a speck of" |
| | ratio | 0.434 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 53 | | matches | | 0 | "The green neon hum of" | | 1 | "Aurora shifted the heat-sealed bag" | | 2 | "She pushed the heavy oak" | | 3 | "She scanned the room, looking" | | 4 | "Silas leaned against the mahogany," | | 5 | "He dragged his left leg," | | 6 | "He twirled the heavy silver" | | 7 | "Aurora moved toward the counter," | | 8 | "He wore a crisp, tailored" | | 9 | "He turned, his face caught" | | 10 | "The angular lines of his" | | 11 | "It was Julian, her former" | | 12 | "He stood, the chair scraping" | | 13 | "He didn't look like the" | | 14 | "Aurora felt the familiar, dull" | | 15 | "She took a half-step back," | | 16 | "He set his glass down," | | 17 | "He didn't blink when he" | | 18 | "The air between them thickened" | | 19 | "Aurora gestured to the bar," |
| | ratio | 0.981 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 69.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 4 | | matches | | 0 | "He twirled the heavy silver ring on his finger, his gaze heavy with the weight of someone who spent decades reading rooms instead of living in them." | | 1 | "He wore a crisp, tailored suit that looked entirely out of place amidst the peeling wallpaper and framed black-and-white photos of forgotten intelligence ops." | | 2 | "He didn't look like the man who played rugby in the quad; he looked like someone who had spent his adult life erasing people from existence for the highest bidd…" | | 3 | "Aurora gestured to the bar, toward Silas, who stood observing them with his arms crossed, a neutral expression masking the calculations surely running behind hi…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Silas admitted (admit)" |
| | dialogueSentences | 35 | | tagDensity | 0.029 | | leniency | 0.057 | | rawRatio | 1 | | effectiveRatio | 0.057 | |