| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.60% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1190 | | totalAiIsmAdverbs | 2 | | 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) | |
| 45.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1190 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "weight" | | 1 | "porcelain" | | 2 | "glint" | | 3 | "pulse" | | 4 | "footsteps" | | 5 | "trembled" | | 6 | "stomach" | | 7 | "lurched" | | 8 | "echoed" | | 9 | "familiar" | | 10 | "glinting" | | 11 | "silence" |
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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 | 114 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 114 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 11 | | totalWords | 1184 | | ratio | 0.009 | | matches | | 0 | "Damn it." | | 1 | "No humans past this point." | | 2 | "rippled" | | 3 | "duck" | | 4 | "Detective" | | 5 | "always" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.75% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 1086 | | uniqueNames | 8 | | maxNameDensity | 1.1 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 12 | | Quinn | 1 | | Veil | 2 | | Market | 2 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Tomás" |
| | places | (empty) | | globalScore | 0.948 | | windowScore | 1 | |
| 51.32% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like jars of preserved nightmares" | | 1 | "smelled like dried blood in neat little pa" | | 2 | "something like excitement" |
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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 | 1184 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 23.22 | | std | 18.77 | | cv | 0.809 | | sampleLengths | | 0 | 56 | | 1 | 50 | | 2 | 47 | | 3 | 48 | | 4 | 60 | | 5 | 3 | | 6 | 45 | | 7 | 77 | | 8 | 44 | | 9 | 31 | | 10 | 11 | | 11 | 15 | | 12 | 2 | | 13 | 49 | | 14 | 7 | | 15 | 60 | | 16 | 4 | | 17 | 37 | | 18 | 5 | | 19 | 5 | | 20 | 35 | | 21 | 40 | | 22 | 3 | | 23 | 32 | | 24 | 22 | | 25 | 14 | | 26 | 7 | | 27 | 29 | | 28 | 5 | | 29 | 4 | | 30 | 10 | | 31 | 2 | | 32 | 36 | | 33 | 25 | | 34 | 9 | | 35 | 19 | | 36 | 23 | | 37 | 16 | | 38 | 2 | | 39 | 10 | | 40 | 15 | | 41 | 5 | | 42 | 25 | | 43 | 36 | | 44 | 15 | | 45 | 10 | | 46 | 22 | | 47 | 13 | | 48 | 31 | | 49 | 7 |
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| 89.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 114 | | matches | | 0 | "were lined" | | 1 | "was gone" | | 2 | "was gone" | | 3 | "was raised" | | 4 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 193 | | matches | (empty) | |
| 7.87% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 127 | | ratio | 0.047 | | matches | | 0 | "Then—gone." | | 1 | "Then—light." | | 2 | "Too many civilians—if that’s what they were." | | 3 | "A sound—footsteps, light but hurried." | | 4 | "Then they stepped backward—and the ground beneath them *rippled*, as if the stone itself were water." | | 5 | "And then, from the darkness, a new voice—warm, accented, familiar." |
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| 94.17% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1093 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 51 | | adverbRatio | 0.04666056724611162 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006404391582799634 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 9.32 | | std | 5.67 | | cv | 0.609 | | sampleLengths | | 0 | 24 | | 1 | 10 | | 2 | 22 | | 3 | 14 | | 4 | 9 | | 5 | 1 | | 6 | 6 | | 7 | 20 | | 8 | 11 | | 9 | 7 | | 10 | 16 | | 11 | 13 | | 12 | 2 | | 13 | 16 | | 14 | 15 | | 15 | 15 | | 16 | 1 | | 17 | 14 | | 18 | 17 | | 19 | 11 | | 20 | 17 | | 21 | 3 | | 22 | 10 | | 23 | 15 | | 24 | 5 | | 25 | 15 | | 26 | 16 | | 27 | 8 | | 28 | 11 | | 29 | 2 | | 30 | 7 | | 31 | 15 | | 32 | 18 | | 33 | 9 | | 34 | 19 | | 35 | 16 | | 36 | 3 | | 37 | 15 | | 38 | 13 | | 39 | 11 | | 40 | 3 | | 41 | 8 | | 42 | 4 | | 43 | 2 | | 44 | 17 | | 45 | 13 | | 46 | 10 | | 47 | 9 | | 48 | 3 | | 49 | 4 |
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| 51.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.33070866141732286 | | totalSentences | 127 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 10 | | totalSentences | 103 | | matches | | 0 | "Just a rusted metal grate" | | 1 | "Too many civilians—if that’s what" | | 2 | "Then they turned, just slightly," | | 3 | "Too late for that." | | 4 | "Then the ground trembled." | | 5 | "Then they stepped backward—and the" | | 6 | "Then, from the shadows behind" | | 7 | "Just kept coming." | | 8 | "Then, as one, they turned" | | 9 | "Then she turned and stepped" |
| | ratio | 0.097 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 103 | | matches | | 0 | "She hit the alley’s narrow" | | 1 | "She pried it up with" | | 2 | "She swung her legs over" | | 3 | "Her worn leather watch ticked" | | 4 | "She hit the bottom and" | | 5 | "She’d heard whispers of it" | | 6 | "They slipped between stalls, their" | | 7 | "She ignored them, her gaze" | | 8 | "They paused at a stall" | | 9 | "Her pulse spiked." | | 10 | "She ducked behind a stall" | | 11 | "His voice was a rasp," | | 12 | "She peeked around the edge" | | 13 | "She moved again, faster now," | | 14 | "She almost laughed." | | 15 | "She broke into a jog," | | 16 | "She drew her weapon." | | 17 | "They sank into the floor," | | 18 | "She’d seen enough." | | 19 | "Her hand went to her" |
| | ratio | 0.282 | |
| 66.80% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 103 | | matches | | 0 | "The pavement slick beneath her" | | 1 | "Rain slashed sideways, needling her" | | 2 | "She hit the alley’s narrow" | | 3 | "Harlow dropped to a crouch," | | 4 | "The grate’s bolts were fresh," | | 5 | "She pried it up with" | | 6 | "A ladder descended into blackness," | | 7 | "She swung her legs over" | | 8 | "The air thickened, the scent" | | 9 | "Her worn leather watch ticked" | | 10 | "She hit the bottom and" | | 11 | "The walls were lined with" | | 12 | "A bone token lay at" | | 13 | "The Veil Market." | | 14 | "She’d heard whispers of it" | | 15 | "A black market for things" | | 16 | "The air hummed with the" | | 17 | "A figure moved ahead, their" | | 18 | "They slipped between stalls, their" | | 19 | "Harlow followed, her hand resting" |
| | ratio | 0.786 | |
| 48.54% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 103 | | matches | | | ratio | 0.01 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 3 | | matches | | 0 | "A bone token lay at her feet, its surface carved with symbols that made her eyes ache." | | 1 | "A black market for things that shouldn’t exist, moving beneath the city like a parasite." | | 2 | "They paused at a stall selling what looked like jars of preserved nightmares, their fingers brushing against the glass." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 18 | | tagDensity | 0.056 | | leniency | 0.111 | | rawRatio | 1 | | effectiveRatio | 0.111 | |