| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 754 | | 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) | |
| 73.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 754 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "traced" | | 1 | "silk" | | 2 | "silence" | | 3 | "echoed" |
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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 | 78 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 88 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 747 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 88.46% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 650 | | uniqueNames | 13 | | maxNameDensity | 1.23 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Camden | 2 | | Quinn | 1 | | Raven | 1 | | Nest | 1 | | Morris | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Tomás | 4 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Harlow | 8 |
| | persons | | 0 | "Camden" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Market" | | 5 | "Tomás" | | 6 | "Herrera" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "Harlow" |
| | places | (empty) | | globalScore | 0.885 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 747 | | matches | (empty) | |
| 90.91% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 88 | | matches | | 0 | "traced that door" | | 1 | "promised that death" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 19.15 | | std | 20.32 | | cv | 1.061 | | sampleLengths | | 0 | 63 | | 1 | 6 | | 2 | 6 | | 3 | 1 | | 4 | 7 | | 5 | 6 | | 6 | 74 | | 7 | 20 | | 8 | 45 | | 9 | 16 | | 10 | 2 | | 11 | 40 | | 12 | 17 | | 13 | 30 | | 14 | 3 | | 15 | 51 | | 16 | 15 | | 17 | 11 | | 18 | 3 | | 19 | 62 | | 20 | 5 | | 21 | 53 | | 22 | 1 | | 23 | 9 | | 24 | 7 | | 25 | 4 | | 26 | 13 | | 27 | 6 | | 28 | 12 | | 29 | 52 | | 30 | 3 | | 31 | 3 | | 32 | 5 | | 33 | 19 | | 34 | 16 | | 35 | 41 | | 36 | 10 | | 37 | 5 | | 38 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 122 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 2 | | flaggedSentences | 7 | | totalSentences | 88 | | ratio | 0.08 | | matches | | 0 | "Old maps and black-and-white photographs watched from behind glass—frozen sailors, lost streets, dead cartographers." | | 1 | "Harlow knew the bar's secret room hid behind a bookshelf in the back; she had traced that door three months ago, found nothing but smoke and lies and the smell of cheap gin." | | 2 | "Her heart hammered—fresh and wild, a machine rebuilt for this exact hunt." | | 3 | "A bone token—pale, carved, wrong." | | 4 | "It moved every full moon; tonight the moon hid behind the rain, but the market remained, anchored in darkness." | | 5 | "His height—five foot ten—matched her nearly eye to eye." | | 6 | "The suspect—her suspect—had vanished into a crowd of buyers trading banned alchemy in silk pouches." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 661 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.01361573373676248 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.006051437216338881 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 88 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 88 | | mean | 8.49 | | std | 7.63 | | cv | 0.898 | | sampleLengths | | 0 | 6 | | 1 | 23 | | 2 | 19 | | 3 | 15 | | 4 | 6 | | 5 | 2 | | 6 | 2 | | 7 | 2 | | 8 | 1 | | 9 | 5 | | 10 | 2 | | 11 | 3 | | 12 | 3 | | 13 | 19 | | 14 | 14 | | 15 | 33 | | 16 | 8 | | 17 | 6 | | 18 | 14 | | 19 | 11 | | 20 | 1 | | 21 | 1 | | 22 | 14 | | 23 | 7 | | 24 | 11 | | 25 | 16 | | 26 | 2 | | 27 | 3 | | 28 | 12 | | 29 | 3 | | 30 | 7 | | 31 | 10 | | 32 | 5 | | 33 | 6 | | 34 | 8 | | 35 | 3 | | 36 | 7 | | 37 | 5 | | 38 | 6 | | 39 | 3 | | 40 | 9 | | 41 | 3 | | 42 | 15 | | 43 | 7 | | 44 | 2 | | 45 | 3 | | 46 | 5 | | 47 | 19 | | 48 | 4 | | 49 | 11 |
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| 55.30% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.38636363636363635 | | totalSentences | 88 | | uniqueOpeners | 34 | |
| 98.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 68 | | matches | | 0 | "Then the runner reached the" | | 1 | "Then she saw Tomás Herrera." |
| | ratio | 0.029 | |
| 67.06% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 68 | | matches | | 0 | "She moved with military precision," | | 1 | "He never turned." | | 2 | "He never slowed." | | 3 | "She chased him past the" | | 4 | "Her breath came in sharp" | | 5 | "Her brown eyes tracked every" | | 6 | "She had promised that death" | | 7 | "She pursued it now with" | | 8 | "Her legs burned." | | 9 | "Her heart hammered—fresh and wild," | | 10 | "She tasted iron." | | 11 | "He pressed it to the" | | 12 | "She knew the name from" | | 13 | "It moved every full moon;" | | 14 | "She stepped down." | | 15 | "He leaned against a cracked" | | 16 | "His Saint Christopher medallion caught" | | 17 | "His short curly dark brown" | | 18 | "His warm brown eyes held" | | 19 | "His height—five foot ten—matched her" |
| | ratio | 0.382 | |
| 4.12% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 68 | | matches | | 0 | "Harlow Quinn sprinted through the" | | 1 | "The worn leather watch on" | | 2 | "She moved with military precision," | | 3 | "The suspect ran ten yards" | | 4 | "Harlow's voice cut the storm." | | 5 | "He never turned." | | 6 | "He never slowed." | | 7 | "She chased him past the" | | 8 | "Harlow knew the bar's secret" | | 9 | "Tonight, the lies ran faster" | | 10 | "Her breath came in sharp" | | 11 | "Her brown eyes tracked every" | | 12 | "The word sat in her" | | 13 | "She had promised that death" | | 14 | "She pursued it now with" | | 15 | "The runner veered left, into" | | 16 | "Her legs burned." | | 17 | "Her heart hammered—fresh and wild," | | 18 | "The alley narrowed." | | 19 | "Brick walls pressed close, damp" |
| | ratio | 0.912 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 1 | | matches | | 0 | "She moved with military precision, boots slicing through black puddles that swallowed the streetlights whole." |
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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 | 12 | | tagDensity | 0.083 | | leniency | 0.167 | | rawRatio | 0 | | effectiveRatio | 0 | |