| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 21 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 81.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1084 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 58.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1084 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "unreadable" | | 1 | "pulse" | | 2 | "silence" | | 3 | "whisper" | | 4 | "flicked" | | 5 | "echoing" | | 6 | "footsteps" | | 7 | "shimmered" | | 8 | "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 | 95 | | matches | (empty) | |
| 82.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1077 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 78.93% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 14 | | wordCount | 985 | | uniqueNames | 1 | | maxNameDensity | 1.42 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | | persons | | | places | (empty) | | globalScore | 0.789 | | windowScore | 1 | |
| 44.37% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 3 | | matches | | 0 | "something between fear and recognition" | | 1 | "looked like any other back room" | | 2 | "felt like she was breathing in the very" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1077 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 102 | | matches | (empty) | |
| 89.57% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 43.08 | | std | 19.97 | | cv | 0.464 | | sampleLengths | | 0 | 93 | | 1 | 79 | | 2 | 80 | | 3 | 48 | | 4 | 8 | | 5 | 41 | | 6 | 49 | | 7 | 58 | | 8 | 45 | | 9 | 27 | | 10 | 36 | | 11 | 16 | | 12 | 59 | | 13 | 14 | | 14 | 40 | | 15 | 39 | | 16 | 38 | | 17 | 35 | | 18 | 46 | | 19 | 63 | | 20 | 29 | | 21 | 39 | | 22 | 36 | | 23 | 28 | | 24 | 31 |
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| 83.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 95 | | matches | | 0 | "was swallowed" | | 1 | "was lost" | | 2 | "been held" | | 3 | "were lined" | | 4 | "been moved" | | 5 | "were lined" |
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| 88.89% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 180 | | matches | | 0 | "was watching" | | 1 | "wasn’t leaving" | | 2 | "was breathing" |
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| 58.82% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 102 | | ratio | 0.029 | | matches | | 0 | "The suspect—some low-level dealer, probably—had cut through the alley behind the pub, his hood pulled tight against the downpour." | | 1 | "She turned the corner just as he vanished into the dim glow of a neon sign—green, jagged letters spelling out *Raven’s Nest*." | | 2 | "The way he’d bolted, the way he’d chosen this place—it all pointed to one thing." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 992 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.025201612903225805 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006048387096774193 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 10.56 | | std | 6.11 | | cv | 0.578 | | sampleLengths | | 0 | 16 | | 1 | 23 | | 2 | 19 | | 3 | 6 | | 4 | 4 | | 5 | 25 | | 6 | 22 | | 7 | 21 | | 8 | 21 | | 9 | 4 | | 10 | 11 | | 11 | 12 | | 12 | 21 | | 13 | 14 | | 14 | 5 | | 15 | 5 | | 16 | 4 | | 17 | 19 | | 18 | 7 | | 19 | 9 | | 20 | 18 | | 21 | 14 | | 22 | 6 | | 23 | 2 | | 24 | 8 | | 25 | 4 | | 26 | 15 | | 27 | 8 | | 28 | 6 | | 29 | 7 | | 30 | 3 | | 31 | 6 | | 32 | 33 | | 33 | 10 | | 34 | 13 | | 35 | 19 | | 36 | 12 | | 37 | 4 | | 38 | 16 | | 39 | 4 | | 40 | 7 | | 41 | 7 | | 42 | 11 | | 43 | 7 | | 44 | 7 | | 45 | 3 | | 46 | 10 | | 47 | 11 | | 48 | 13 | | 49 | 12 |
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| 43.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.23529411764705882 | | totalSentences | 102 | | uniqueOpeners | 24 | |
| 70.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 94 | | matches | | 0 | "Then, without warning, the bookshelf" | | 1 | "Then, the stairs ended." |
| | ratio | 0.021 | |
| 49.79% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 94 | | matches | | 0 | "She kept her pace steady," | | 1 | "She didn’t need one." | | 2 | "She turned the corner just" | | 3 | "He pushed through the door" | | 4 | "They knew the rules here." | | 5 | "He froze mid-step, his shoulders" | | 6 | "She stepped forward, her boots" | | 7 | "she said, her voice cutting" | | 8 | "he breathed, his voice raw" | | 9 | "She didn’t wait for an" | | 10 | "She didn’t need one." | | 11 | "He was deeper in than" | | 12 | "They didn’t breathe." | | 13 | "They just watched, their expressions" | | 14 | "She grabbed his arm, her" | | 15 | "He yelped, but the sound" | | 16 | "she said, her voice low," | | 17 | "It looked like any other" | | 18 | "she said, her grip tightening" | | 19 | "She didn’t know what was" |
| | ratio | 0.426 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 94 | | matches | | 0 | "The rain came down in" | | 1 | "She kept her pace steady," | | 2 | "The suspect—some low-level dealer, probably—had" | | 3 | "Quinn didn’t bother with a" | | 4 | "She didn’t need one." | | 5 | "The rain washed away the" | | 6 | "She turned the corner just" | | 7 | "The bar’s entrance yawned open," | | 8 | "The suspect didn’t hesitate." | | 9 | "He pushed through the door" | | 10 | "Quinn followed, her gloved fingers" | | 11 | "The bar was a relic," | | 12 | "None of them looked up." | | 13 | "They knew the rules here." | | 14 | "The suspect, however, didn’t." | | 15 | "He froze mid-step, his shoulders" | | 16 | "Quinn didn’t give him time" | | 17 | "She stepped forward, her boots" | | 18 | "she said, her voice cutting" | | 19 | "The suspect’s head snapped up," |
| | ratio | 0.926 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 57.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 5 | | matches | | 0 | "She kept her pace steady, the heel of her shoe clicking against the wet asphalt like a metronome counting down to something inevitable." | | 1 | "Inside, the air smelled of stale beer and something older, something that clung to the back of her throat like smoke." | | 2 | "Quinn kept her eyes on the suspect, but her peripheral vision picked up the way the shadows seemed to shift, just slightly, as if something was watching from th…" | | 3 | "The air inside was cooler, thicker, like the breath of something that had been held too long." | | 4 | "The suspect hesitated, his eyes darting to the left, where a single book seemed to shift slightly, as if it had been moved." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 9 | | matches | | 0 | "he breathed, his voice raw" | | 1 | "she said, her voice low, dangerous" | | 2 | "she said, her grip tightening" | | 3 | "he whispered, his voice trembling" | | 4 | "he said, his voice barely a whisper" | | 5 | "she said, her voice steady, unyielding" | | 6 | "she said, her voice low, dangerous" | | 7 | "he said, his voice smooth, dangerous" | | 8 | "she said, her voice steady, unyielding" |
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| 73.08% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 2 | | fancyTags | | 0 | "he breathed (breathe)" | | 1 | "he whispered (whisper)" |
| | dialogueSentences | 21 | | tagDensity | 0.619 | | leniency | 1 | | rawRatio | 0.154 | | effectiveRatio | 0.154 | |