| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 3 | | adverbTags | | 0 | "she said quietly [quietly]" | | 1 | "Wallace said slowly [slowly]" | | 2 | "Harlow knelt again [again]" |
| | dialogueSentences | 36 | | tagDensity | 0.306 | | leniency | 0.611 | | rawRatio | 0.273 | | effectiveRatio | 0.167 | |
| 89.37% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 941 | | 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) | |
| 78.75% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 941 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "etched" | | 1 | "weight" | | 2 | "traced" | | 3 | "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 | 80 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 80 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 4 | | totalWords | 941 | | ratio | 0.004 | | matches | | 0 | "Palm Readings & Wishes" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.16% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 737 | | uniqueNames | 10 | | maxNameDensity | 1.36 | | worstName | "Wallace" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 8 | | Quinn | 1 | | Readings | 1 | | Wallace | 10 | | Departures | 1 | | Market | 4 | | Camden | 2 | | Morris | 5 | | Kowalski | 2 | | Veil | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Wallace" | | 3 | "Market" | | 4 | "Morris" | | 5 | "Kowalski" |
| | places | | | globalScore | 0.822 | | windowScore | 1 | |
| 96.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | glossingSentenceCount | 1 | | matches | | |
| 93.73% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.063 | | wordCount | 941 | | matches | | 0 | "not in a grimace but in something like relief" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 105 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 20.91 | | std | 19.05 | | cv | 0.911 | | sampleLengths | | 0 | 70 | | 1 | 5 | | 2 | 35 | | 3 | 6 | | 4 | 3 | | 5 | 5 | | 6 | 43 | | 7 | 49 | | 8 | 11 | | 9 | 23 | | 10 | 17 | | 11 | 4 | | 12 | 5 | | 13 | 45 | | 14 | 38 | | 15 | 19 | | 16 | 1 | | 17 | 49 | | 18 | 29 | | 19 | 6 | | 20 | 2 | | 21 | 40 | | 22 | 11 | | 23 | 33 | | 24 | 31 | | 25 | 19 | | 26 | 28 | | 27 | 1 | | 28 | 3 | | 29 | 47 | | 30 | 7 | | 31 | 63 | | 32 | 2 | | 33 | 10 | | 34 | 28 | | 35 | 14 | | 36 | 29 | | 37 | 32 | | 38 | 6 | | 39 | 4 | | 40 | 1 | | 41 | 57 | | 42 | 6 | | 43 | 1 | | 44 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 80 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 122 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 105 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 739 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 21 | | adverbRatio | 0.028416779431664412 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.010825439783491205 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 8.96 | | std | 7.46 | | cv | 0.833 | | sampleLengths | | 0 | 10 | | 1 | 27 | | 2 | 2 | | 3 | 2 | | 4 | 29 | | 5 | 5 | | 6 | 20 | | 7 | 1 | | 8 | 1 | | 9 | 4 | | 10 | 9 | | 11 | 6 | | 12 | 3 | | 13 | 5 | | 14 | 10 | | 15 | 6 | | 16 | 4 | | 17 | 1 | | 18 | 12 | | 19 | 10 | | 20 | 2 | | 21 | 18 | | 22 | 7 | | 23 | 22 | | 24 | 4 | | 25 | 7 | | 26 | 12 | | 27 | 11 | | 28 | 9 | | 29 | 3 | | 30 | 2 | | 31 | 3 | | 32 | 4 | | 33 | 5 | | 34 | 7 | | 35 | 17 | | 36 | 21 | | 37 | 11 | | 38 | 14 | | 39 | 13 | | 40 | 11 | | 41 | 8 | | 42 | 1 | | 43 | 4 | | 44 | 5 | | 45 | 2 | | 46 | 18 | | 47 | 2 | | 48 | 2 | | 49 | 16 |
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| 86.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5619047619047619 | | totalSentences | 105 | | uniqueOpeners | 59 | |
| 52.08% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 64 | | matches | | 0 | "Still pointing west, towards the" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 64 | | matches | | 0 | "Its needle skittered left, past" | | 1 | "His left arm ended in" | | 2 | "Her eyes stayed on the" | | 3 | "She'd seen that expression once" | | 4 | "Her watch caught the light" | | 5 | "She read the receipt under" | | 6 | "She pocketed the paper." | | 7 | "she said quietly" | | 8 | "She stood, knees popping" | | 9 | "She pulled out the brass" | | 10 | "It held steady, humming with" | | 11 | "Her fingers traced the tiles" | | 12 | "She touched the satchel." | | 13 | "It was a thing that" |
| | ratio | 0.219 | |
| 45.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 64 | | matches | | 0 | "The brass compass casing bit" | | 1 | "Its needle skittered left, past" | | 2 | "The verdigris flaked under her" | | 3 | "DC Wallace stood by the" | | 4 | "His left arm ended in" | | 5 | "Wallace's jaw tightened, but he" | | 6 | "Her eyes stayed on the" | | 7 | "The cut was surgical." | | 8 | "The victim's face held no" | | 9 | "The station's damp breath, thick" | | 10 | "The compass needle shivered." | | 11 | "Wallace's voice came from behind" | | 12 | "Harlow rolled the victim's collar" | | 13 | "Wallace stepped closer, his torch" | | 14 | "The dead man's lips curled" | | 15 | "She'd seen that expression once" | | 16 | "Her watch caught the light" | | 17 | "The worn leather band was" | | 18 | "She read the receipt under" | | 19 | "She pocketed the paper." |
| | ratio | 0.828 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 64 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 1 | | matches | | 0 | "Beyond it lay a narrow corridor, its floor scattered with fresh salt and something darker that caught the light like wet ink." |
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| 34.09% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 2 | | matches | | 0 | "She stood, knees popping" | | 1 | "Harlow knelt again, though her body wanted to lock, to freeze, to do anything but look at the ruin of the girl's throat" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 36 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0 | | effectiveRatio | 0 | |