| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1103 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 27.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1103 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "fractured" | | 1 | "flickered" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "pulse" | | 5 | "wavering" | | 6 | "etched" | | 7 | "scanning" | | 8 | "glint" | | 9 | "weight" | | 10 | "pounding" | | 11 | "stomach" | | 12 | "unreadable" | | 13 | "could feel" |
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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 | 88 | | matches | (empty) | |
| 77.92% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 1093 | | ratio | 0.003 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1013 | | uniqueNames | 18 | | maxNameDensity | 1.48 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 15 | | Tomás | 1 | | Herrera | 13 | | Saint | 1 | | Christopher | 1 | | Greek | 1 | | Street | 1 | | Raven | 1 | | Tube | 1 | | Camden | 1 | | Veil | 3 | | Market | 3 | | Morris | 1 | | Vey | 2 | | Silas | 6 | | Protocol | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Raven" | | 7 | "Tube" | | 8 | "Market" | | 9 | "Morris" | | 10 | "Vey" | | 11 | "Silas" | | 12 | "Protocol" |
| | places | | 0 | "London" | | 1 | "Greek" | | 2 | "Street" |
| | globalScore | 0.76 | | windowScore | 0.667 | |
| 73.08% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 2 | | matches | | 0 | "something like terror" | | 1 | "looked like liquid shadow" |
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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 | 1093 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 96 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 40.48 | | std | 30.3 | | cv | 0.749 | | sampleLengths | | 0 | 84 | | 1 | 60 | | 2 | 12 | | 3 | 41 | | 4 | 79 | | 5 | 85 | | 6 | 35 | | 7 | 89 | | 8 | 73 | | 9 | 85 | | 10 | 72 | | 11 | 60 | | 12 | 81 | | 13 | 12 | | 14 | 12 | | 15 | 23 | | 16 | 18 | | 17 | 8 | | 18 | 22 | | 19 | 24 | | 20 | 17 | | 21 | 5 | | 22 | 45 | | 23 | 37 | | 24 | 3 | | 25 | 7 | | 26 | 4 |
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| 89.31% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 88 | | matches | | 0 | "was swallowed" | | 1 | "was swallowed" | | 2 | "been found" | | 3 | "was said" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 175 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 96 | | ratio | 0.083 | | matches | | 0 | "The suspect—Tomás Herrera—was fast, his dark curls plastered to his forehead, his Saint Christopher medallion bouncing against his chest with every stride." | | 1 | "She knew what lay down there—the Veil Market." | | 2 | "Protocol hadn’t explained the way his body had been found—pale, drained, his eyes wide with something like terror." | | 3 | "A bone token hung from a chain around her neck—she didn’t remember picking it up, but she must have grabbed it from Herrera during the chase." | | 4 | "Ahead, she caught a flash of movement—Herrera, slipping between two stalls." | | 5 | "A child—if it was a child—held out a handful of teeth, each one humming with a quiet, discordant note." | | 6 | "Behind him, a figure emerged from the shadows—a man with a face like a skull, his eyes burning with a cold, blue fire." | | 7 | "She had a choice to make—turn back now, or push forward into the unknown." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1024 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.0185546875 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.00390625 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 11.39 | | std | 6.83 | | cv | 0.6 | | sampleLengths | | 0 | 15 | | 1 | 25 | | 2 | 22 | | 3 | 22 | | 4 | 3 | | 5 | 14 | | 6 | 22 | | 7 | 3 | | 8 | 18 | | 9 | 12 | | 10 | 30 | | 11 | 5 | | 12 | 4 | | 13 | 2 | | 14 | 15 | | 15 | 17 | | 16 | 11 | | 17 | 17 | | 18 | 8 | | 19 | 11 | | 20 | 2 | | 21 | 6 | | 22 | 13 | | 23 | 9 | | 24 | 18 | | 25 | 19 | | 26 | 18 | | 27 | 6 | | 28 | 1 | | 29 | 5 | | 30 | 18 | | 31 | 5 | | 32 | 11 | | 33 | 14 | | 34 | 19 | | 35 | 19 | | 36 | 26 | | 37 | 6 | | 38 | 24 | | 39 | 20 | | 40 | 23 | | 41 | 10 | | 42 | 18 | | 43 | 16 | | 44 | 22 | | 45 | 13 | | 46 | 6 | | 47 | 11 | | 48 | 12 | | 49 | 16 |
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| 50.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.34375 | | totalSentences | 96 | | uniqueOpeners | 33 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 82 | | matches | | 0 | "He cut left down a" | | 1 | "She followed, her sharp jaw" | | 2 | "He shouldered past a group" | | 3 | "She wasn’t losing him." | | 4 | "She knew what lay down" | | 5 | "Her fingers twitched toward her" | | 6 | "She had to move now." | | 7 | "She kept her gaze sharp," | | 8 | "She turned a corner and" | | 9 | "It didn’t speak, but its" | | 10 | "She sidestepped it, her breath" | | 11 | "She broke into a run," | | 12 | "she whispered, her voice like" | | 13 | "She rounded another corner and" | | 14 | "She knew that face." | | 15 | "she said, her voice steady" | | 16 | "She didn’t have to." | | 17 | "She’d spent her life facing" | | 18 | "He raised a hand, and" | | 19 | "She could feel the weight" |
| | ratio | 0.28 | |
| 2.68% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 82 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn’s breath fogged" | | 2 | "The suspect—Tomás Herrera—was fast, his" | | 3 | "He cut left down a" | | 4 | "Quinn didn’t hesitate." | | 5 | "She followed, her sharp jaw" | | 6 | "The alley spat them out" | | 7 | "Herrera didn’t slow." | | 8 | "He shouldered past a group" | | 9 | "Quinn barked, but the word" | | 10 | "Herrera glanced back, his warm" | | 11 | "Quinn cursed under her breath." | | 12 | "She wasn’t losing him." | | 13 | "The alley was a gullet" | | 14 | "Herrera’s footsteps echoed ahead, but" | | 15 | "Quinn skidded to a halt" | | 16 | "The stairs led down into" | | 17 | "She knew what lay down" | | 18 | "A place where the rules" | | 19 | "Protocol said to call for" |
| | ratio | 0.915 | |
| 60.98% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 82 | | matches | | 0 | "By the time they arrived," |
| | ratio | 0.012 | |
| 96.27% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn’s breath fogged in front of her as she sprinted, her boots splashing through puddles that reflected the fractured glow of neon signs." | | 1 | "The tunnel opened up into a cavernous space, the walls lined with flickering lanterns that cast long, wavering shadows." | | 2 | "Another stall displayed rows of knives, their blades etched with symbols that made her eyes ache if she looked at them too long." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 3 | | matches | | 0 | "Quinn barked, but the word was swallowed by the drumming rain" | | 1 | "she said, her voice steady" | | 2 | "she said, her voice low" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "Quinn barked (bark)" | | 1 | "she whispered (whisper)" | | 2 | "he muttered (mutter)" |
| | dialogueSentences | 16 | | tagDensity | 0.438 | | leniency | 0.875 | | rawRatio | 0.429 | | effectiveRatio | 0.375 | |