| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.154 | | leniency | 0.308 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1499 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | | 2 | | adverb | "reluctantly" | | count | 1 |
| | 3 | |
| | highlights | | 0 | "sharply" | | 1 | "slowly" | | 2 | "reluctantly" | | 3 | "very" |
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| 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) | |
| 33.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1499 | | totalAiIsms | 20 | | found | | | highlights | | 0 | "shattered" | | 1 | "weight" | | 2 | "scanned" | | 3 | "silence" | | 4 | "spectral" | | 5 | "resonated" | | 6 | "gloom" | | 7 | "potential" | | 8 | "echoed" | | 9 | "whisper" | | 10 | "intricate" | | 11 | "etched" | | 12 | "pulsed" | | 13 | "standard" | | 14 | "mechanical" | | 15 | "calculated" | | 16 | "flickered" |
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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 | 195 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 195 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 206 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1499 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1380 | | uniqueNames | 7 | | maxNameDensity | 1.81 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 25 | | Camden | 1 | | Victorian | 1 | | Morris | 4 | | Saint | 1 | | Christopher | 1 | | Tomás | 5 |
| | persons | | 0 | "Quinn" | | 1 | "Camden" | | 2 | "Morris" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Tomás" |
| | places | | | globalScore | 0.594 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 109 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like diseased skin" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.667 | | wordCount | 1499 | | matches | | 0 | "not as a thought, but as a phantom weight pressing against her lungs" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 206 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 45.42 | | std | 26.25 | | cv | 0.578 | | sampleLengths | | 0 | 103 | | 1 | 58 | | 2 | 8 | | 3 | 6 | | 4 | 11 | | 5 | 13 | | 6 | 54 | | 7 | 72 | | 8 | 30 | | 9 | 75 | | 10 | 79 | | 11 | 59 | | 12 | 55 | | 13 | 48 | | 14 | 52 | | 15 | 67 | | 16 | 71 | | 17 | 47 | | 18 | 7 | | 19 | 75 | | 20 | 77 | | 21 | 47 | | 22 | 63 | | 23 | 24 | | 24 | 9 | | 25 | 37 | | 26 | 9 | | 27 | 57 | | 28 | 16 | | 29 | 43 | | 30 | 34 | | 31 | 75 | | 32 | 18 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 195 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 275 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 206 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1383 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.015184381778741865 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.006507592190889371 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 206 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 206 | | mean | 7.28 | | std | 4.2 | | cv | 0.578 | | sampleLengths | | 0 | 5 | | 1 | 17 | | 2 | 13 | | 3 | 7 | | 4 | 15 | | 5 | 13 | | 6 | 12 | | 7 | 5 | | 8 | 5 | | 9 | 11 | | 10 | 1 | | 11 | 10 | | 12 | 7 | | 13 | 16 | | 14 | 13 | | 15 | 11 | | 16 | 8 | | 17 | 6 | | 18 | 11 | | 19 | 13 | | 20 | 2 | | 21 | 3 | | 22 | 11 | | 23 | 7 | | 24 | 9 | | 25 | 8 | | 26 | 6 | | 27 | 8 | | 28 | 3 | | 29 | 10 | | 30 | 8 | | 31 | 10 | | 32 | 8 | | 33 | 13 | | 34 | 4 | | 35 | 16 | | 36 | 4 | | 37 | 9 | | 38 | 5 | | 39 | 1 | | 40 | 4 | | 41 | 7 | | 42 | 2 | | 43 | 5 | | 44 | 16 | | 45 | 5 | | 46 | 13 | | 47 | 5 | | 48 | 3 | | 49 | 11 |
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| 63.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.42718446601941745 | | totalSentences | 206 | | uniqueOpeners | 88 | |
| 18.83% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 177 | | matches | | 0 | "Pale, eager eyes fixed on" |
| | ratio | 0.006 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 177 | | matches | | 0 | "She pivoted hard, boots skidding" | | 1 | "She kept her breathing shallow," | | 2 | "Her finger rested on the" | | 3 | "She wiped the sidearm on" | | 4 | "She took the left flank," | | 5 | "Her earpiece crackled with static," | | 6 | "She accelerated, boots finding purchase" | | 7 | "Her gaze tracked scuff marks" | | 8 | "She checked her watch." | | 9 | "She drew her service pistol" | | 10 | "Her torch beam cut through" | | 11 | "She extended a gloved finger" | | 12 | "She retracted her hand instantly." | | 13 | "She noted the smell, acrid" | | 14 | "She stood and continued down." | | 15 | "Her eyes seemed to follow" | | 16 | "She held her breath." | | 17 | "She counted two paces." | | 18 | "She burst from cover and" | | 19 | "He fumbled at his belt" |
| | ratio | 0.237 | |
| 13.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 158 | | totalSentences | 177 | | matches | | 0 | "Brickwork shattered against Quinn's shoulder." | | 1 | "She pivoted hard, boots skidding" | | 2 | "The ceramic fragment buried itself" | | 3 | "She kept her breathing shallow," | | 4 | "Her finger rested on the" | | 5 | "Polymer grip slick with moisture." | | 6 | "She wiped the sidearm on" | | 7 | "The target had vanished into" | | 8 | "Quinn pushed off the bin" | | 9 | "She took the left flank," | | 10 | "The scent of rotting cabbage" | | 11 | "Her earpiece crackled with static," | | 12 | "The radio died." | | 13 | "Quinn tapped the device once" | | 14 | "Military precision dictated she adapt" | | 15 | "She accelerated, boots finding purchase" | | 16 | "Her gaze tracked scuff marks" | | 17 | "The architecture shifted." | | 18 | "Georgian facades gave way to" | | 19 | "A crumbling archway led into" |
| | ratio | 0.893 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 177 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 3 | | matches | | 0 | "The ambient city hum faded, replaced by a low, throbbing vibration that resonated in Quinn's teeth." | | 1 | "The residue on her glove mirrored the substance that had stopped Morris's heart." | | 2 | "The man wore a Saint Christopher medallion that swung against his chest, catching the green light." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0 | | effectiveRatio | 0 | |