| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "Harper's eyebrows arched skeptically [skeptically]" |
| | dialogueSentences | 53 | | tagDensity | 0.358 | | leniency | 0.717 | | rawRatio | 0.053 | | effectiveRatio | 0.038 | |
| 79.08% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1195 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "really" | | 1 | "softly" | | 2 | "carefully" | | 3 | "lightly" | | 4 | "very" |
| |
| 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) | |
| 24.69% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1195 | | totalAiIsms | 18 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | word | "scratched his head" | | count | 1 |
| | 14 | | | 15 | | | 16 | |
| | highlights | | 0 | "footsteps" | | 1 | "echoed" | | 2 | "sense of" | | 3 | "flickered" | | 4 | "familiar" | | 5 | "furrowed" | | 6 | "tinged" | | 7 | "scanning" | | 8 | "etched" | | 9 | "shattered" | | 10 | "scanned" | | 11 | "tracing" | | 12 | "intrigue" | | 13 | "scratched his head" | | 14 | "surreal" | | 15 | "unraveling" | | 16 | "flicker" |
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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 | 69 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 69 | | 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 | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1192 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 661 | | uniqueNames | 13 | | maxNameDensity | 2.42 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 1 | | Quinn | 16 | | Tube | 1 | | Camden | 1 | | Veil | 3 | | Market | 4 | | Detective | 2 | | Sergeant | 1 | | Harper | 14 | | Eva | 9 | | Kowalski | 1 | | Compass | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Market" | | 4 | "Sergeant" | | 5 | "Harper" | | 6 | "Eva" | | 7 | "Kowalski" | | 8 | "Morris" |
| | places | | | globalScore | 0.29 | | windowScore | 0.167 | |
| 33.72% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 2 | | matches | | 0 | "body, and she visibly paled" | | 1 | "something akin to hope" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1192 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 102 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 23.84 | | std | 14.86 | | cv | 0.623 | | sampleLengths | | 0 | 64 | | 1 | 34 | | 2 | 39 | | 3 | 9 | | 4 | 33 | | 5 | 43 | | 6 | 14 | | 7 | 18 | | 8 | 17 | | 9 | 34 | | 10 | 11 | | 11 | 14 | | 12 | 24 | | 13 | 16 | | 14 | 25 | | 15 | 8 | | 16 | 29 | | 17 | 37 | | 18 | 22 | | 19 | 14 | | 20 | 37 | | 21 | 8 | | 22 | 18 | | 23 | 28 | | 24 | 15 | | 25 | 19 | | 26 | 32 | | 27 | 8 | | 28 | 36 | | 29 | 1 | | 30 | 44 | | 31 | 14 | | 32 | 29 | | 33 | 14 | | 34 | 34 | | 35 | 11 | | 36 | 22 | | 37 | 7 | | 38 | 33 | | 39 | 16 | | 40 | 29 | | 41 | 26 | | 42 | 27 | | 43 | 17 | | 44 | 19 | | 45 | 15 | | 46 | 28 | | 47 | 13 | | 48 | 3 | | 49 | 84 |
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| 95.09% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 69 | | matches | | 0 | "was filled" | | 1 | "was tinged" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 131 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 102 | | ratio | 0.01 | | matches | | 0 | "She moved back to the body, her eyes narrowing on an overlooked detail—a faint set of symbols near the man's neck." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 664 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.03463855421686747 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.01957831325301205 | |
| 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 | 11.68 | | std | 5.94 | | cv | 0.509 | | sampleLengths | | 0 | 23 | | 1 | 13 | | 2 | 14 | | 3 | 14 | | 4 | 7 | | 5 | 17 | | 6 | 6 | | 7 | 4 | | 8 | 11 | | 9 | 14 | | 10 | 14 | | 11 | 9 | | 12 | 11 | | 13 | 22 | | 14 | 9 | | 15 | 12 | | 16 | 12 | | 17 | 10 | | 18 | 8 | | 19 | 6 | | 20 | 4 | | 21 | 14 | | 22 | 2 | | 23 | 15 | | 24 | 10 | | 25 | 10 | | 26 | 14 | | 27 | 11 | | 28 | 4 | | 29 | 10 | | 30 | 9 | | 31 | 15 | | 32 | 3 | | 33 | 13 | | 34 | 5 | | 35 | 20 | | 36 | 3 | | 37 | 5 | | 38 | 8 | | 39 | 21 | | 40 | 12 | | 41 | 15 | | 42 | 10 | | 43 | 22 | | 44 | 14 | | 45 | 12 | | 46 | 25 | | 47 | 8 | | 48 | 5 | | 49 | 13 |
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| 86.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.5196078431372549 | | totalSentences | 102 | | uniqueOpeners | 53 | |
| 55.56% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 60 | | matches | | 0 | "Maybe, just maybe, they were" |
| | ratio | 0.017 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 60 | | matches | | 0 | "Her footsteps echoed through the" | | 1 | "She made out the figure" | | 2 | "His voice was tinged with" | | 3 | "She crouched beside the body," | | 4 | "His skin was pale, unnaturally" | | 5 | "Her gaze moved to his" | | 6 | "she pointed out" | | 7 | "She stood, her mind sifting" | | 8 | "She picked it up, noticing" | | 9 | "she asked Harper, holding it" | | 10 | "He shook his head." | | 11 | "She locked eyes with him." | | 12 | "She turned her attention to" | | 13 | "she stated, carefully picking up" | | 14 | "she pointed towards the booth" | | 15 | "She moved back to the" | | 16 | "She roved the area one" | | 17 | "She pushed her round glasses" | | 18 | "Her gaze fell on the" | | 19 | "They had a long night" |
| | ratio | 0.333 | |
| 1.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 60 | | matches | | 0 | "Detective Harlow Quinn arrived at" | | 1 | "Her footsteps echoed through the" | | 2 | "A harsh fluorescent light flickered" | | 3 | "The air was filled with" | | 4 | "She made out the figure" | | 5 | "His voice was tinged with" | | 6 | "Quinn nodded and approached, her" | | 7 | "The Market, usually bustling with" | | 8 | "Booths, once filled with forbidden" | | 9 | "Quinn asked, her voice steady" | | 10 | "Harper gestured to a scene" | | 11 | "She crouched beside the body," | | 12 | "The victim lay with eyes" | | 13 | "His skin was pale, unnaturally" | | 14 | "Her gaze moved to his" | | 15 | "she pointed out" | | 16 | "Harper rubbed his chin." | | 17 | "She stood, her mind sifting" | | 18 | "She picked it up, noticing" | | 19 | "she asked Harper, holding it" |
| | ratio | 0.917 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 60 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 2 | | matches | | 0 | "Quinn studied the worn leather satchel slung over Eva’s shoulder, filled with tomes that held secrets most people couldn’t even fathom." | | 1 | "Maybe, just maybe, they were edging closer to the truth behind DS Morris's death, and the shadows that lurked just beyond the edge of the known world." |
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| 72.37% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn asked, her voice steady" | | 1 | "Quinn muttered, more to herself" |
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| 17.92% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 7 | | fancyTags | | 0 | "Quinn muttered (mutter)" | | 1 | "she stated (state)" | | 2 | "she summarized (summarize)" | | 3 | "she murmured (murmur)" | | 4 | "Quinn interjected (interject)" | | 5 | "Eva explained (explain)" | | 6 | "Eva interjected (interject)" |
| | dialogueSentences | 53 | | tagDensity | 0.226 | | leniency | 0.453 | | rawRatio | 0.583 | | effectiveRatio | 0.264 | |