| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1123 | | totalAiIsmAdverbs | 1 | | 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) | |
| 33.21% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1123 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "perfect" | | 1 | "intricate" | | 2 | "etched" | | 3 | "scanned" | | 4 | "pristine" | | 5 | "weight" | | 6 | "rhythmic" | | 7 | "trembled" | | 8 | "silence" | | 9 | "oppressive" | | 10 | "firmly" | | 11 | "echoed" |
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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 | 78 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | 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 | 0 | | markdownWords | 0 | | totalWords | 1123 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 43.11% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 842 | | uniqueNames | 4 | | maxNameDensity | 2.14 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Sergeant | 1 | | Miller | 8 | | London | 1 | | Quinn | 18 |
| | persons | | 0 | "Sergeant" | | 1 | "Miller" | | 2 | "Quinn" |
| | places | | | globalScore | 0.431 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a heartbeat in the soles of h" |
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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 | 1123 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 17.83 | | std | 17.42 | | cv | 0.977 | | sampleLengths | | 0 | 50 | | 1 | 5 | | 2 | 51 | | 3 | 2 | | 4 | 21 | | 5 | 28 | | 6 | 38 | | 7 | 6 | | 8 | 10 | | 9 | 49 | | 10 | 10 | | 11 | 13 | | 12 | 3 | | 13 | 56 | | 14 | 3 | | 15 | 2 | | 16 | 4 | | 17 | 8 | | 18 | 59 | | 19 | 4 | | 20 | 5 | | 21 | 4 | | 22 | 1 | | 23 | 35 | | 24 | 6 | | 25 | 5 | | 26 | 25 | | 27 | 17 | | 28 | 33 | | 29 | 19 | | 30 | 11 | | 31 | 18 | | 32 | 4 | | 33 | 11 | | 34 | 9 | | 35 | 6 | | 36 | 33 | | 37 | 3 | | 38 | 2 | | 39 | 11 | | 40 | 57 | | 41 | 4 | | 42 | 2 | | 43 | 2 | | 44 | 6 | | 45 | 10 | | 46 | 4 | | 47 | 58 | | 48 | 5 | | 49 | 5 |
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| 96.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 78 | | matches | | 0 | "been closed" | | 1 | "been placed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 138 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 116 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 843 | | adjectiveStacks | 1 | | stackExamples | | 0 | "distorted, high-pitched wail" |
| | adverbCount | 20 | | adverbRatio | 0.02372479240806643 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.005931198102016607 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 9.68 | | std | 7.16 | | cv | 0.739 | | sampleLengths | | 0 | 13 | | 1 | 17 | | 2 | 20 | | 3 | 5 | | 4 | 4 | | 5 | 7 | | 6 | 19 | | 7 | 8 | | 8 | 13 | | 9 | 2 | | 10 | 10 | | 11 | 11 | | 12 | 28 | | 13 | 2 | | 14 | 7 | | 15 | 5 | | 16 | 3 | | 17 | 21 | | 18 | 6 | | 19 | 10 | | 20 | 3 | | 21 | 10 | | 22 | 12 | | 23 | 5 | | 24 | 19 | | 25 | 10 | | 26 | 13 | | 27 | 3 | | 28 | 12 | | 29 | 4 | | 30 | 10 | | 31 | 5 | | 32 | 25 | | 33 | 3 | | 34 | 2 | | 35 | 4 | | 36 | 8 | | 37 | 6 | | 38 | 4 | | 39 | 9 | | 40 | 2 | | 41 | 2 | | 42 | 21 | | 43 | 4 | | 44 | 11 | | 45 | 4 | | 46 | 5 | | 47 | 4 | | 48 | 1 | | 49 | 8 |
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| 38.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.3103448275862069 | | totalSentences | 116 | | uniqueOpeners | 36 | |
| 45.66% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 73 | | matches | | 0 | "Pale, faceless shapes crowding the" |
| | ratio | 0.014 | |
| 50.14% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 73 | | matches | | 0 | "She didn't look at the" | | 1 | "She marched straight toward the" | | 2 | "She stopped three feet from" | | 3 | "He wore a cheap, oversized" | | 4 | "His eyes stared upward, devoid" | | 5 | "He gestured toward the pool" | | 6 | "She didn't look at the" | | 7 | "She looked at the blood." | | 8 | "It didn't pool." | | 9 | "It formed a perfect, concentric" | | 10 | "She noticed a small, metallic" | | 11 | "Its casing had a heavy" | | 12 | "She snapped them on." | | 13 | "She gently pried the compass" | | 14 | "It spun in a frantic," | | 15 | "She saw the inconsistencies." | | 16 | "He hadn't walked here." | | 17 | "He had been placed here," | | 18 | "She spotted a faint, shimmering" | | 19 | "It wasn't biological." |
| | ratio | 0.425 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 73 | | matches | | 0 | "Quinn stepped over the yellow" | | 1 | "She didn't look at the" | | 2 | "She marched straight toward the" | | 3 | "Quinn ignored the warning." | | 4 | "She stopped three feet from" | | 5 | "A man lay splayed across" | | 6 | "He wore a cheap, oversized" | | 7 | "His eyes stared upward, devoid" | | 8 | "Detective Sergeant Miller stood beside" | | 9 | "He gestured toward the pool" | | 10 | "She didn't look at the" | | 11 | "She looked at the blood." | | 12 | "It didn't pool." | | 13 | "It formed a perfect, concentric" | | 14 | "Quinn leaned closer." | | 15 | "The scent of ozone and" | | 16 | "She noticed a small, metallic" | | 17 | "A small, old brass compass." | | 18 | "Its casing had a heavy" | | 19 | "Quinn reached into her pocket" |
| | ratio | 0.932 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 73 | | matches | (empty) | | ratio | 0 | |
| 17.86% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 7 | | matches | | 0 | "A man lay splayed across the platform, his limbs twisted into a grotesque, geometric shape that defied skeletal logic." | | 1 | "It formed a perfect, concentric circle around the man, the edges sharp and defined, as if held by an invisible wall." | | 2 | "She spotted a faint, shimmering residue on the floor, a trail of iridescent slime that vanished into the wall." | | 3 | "A low, guttural vibration began to shake the platform, a rhythmic thrum that felt like a heartbeat in the soles of her boots." | | 4 | "The slit opened, revealing rows of needle-like teeth and a void that smelled of ancient dust and cold iron." | | 5 | "The sound was a distorted, high-pitched wail that knocked Miller backward, his radio flying from his hand and shattering against the tiles." | | 6 | "Quinn fired, the roar of the service weapon shattering the silence of the station, but the bullet passed clean through the entity's chest, leaving a hole that c…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |