| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 25 | | adverbTagCount | 1 | | adverbTags | | 0 | "Harlow said again [again]" |
| | dialogueSentences | 57 | | tagDensity | 0.439 | | leniency | 0.877 | | rawRatio | 0.04 | | effectiveRatio | 0.035 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1380 | | 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) | |
| 67.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1380 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "perfect" | | 1 | "shimmered" | | 2 | "etched" | | 3 | "trembled" | | 4 | "traced" | | 5 | "silence" | | 6 | "silk" | | 7 | "resolved" |
| |
| 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 | 118 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 118 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 150 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1380 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 36 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 877 | | uniqueNames | 7 | | maxNameDensity | 3.31 | | worstName | "Harlow" | | maxWindowNameDensity | 6 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 29 | | Quinn | 1 | | Tube | 1 | | Camden | 2 | | Kowalski | 1 | | Eva | 15 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Market" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | 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 | 1380 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 150 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 74 | | mean | 18.65 | | std | 16.51 | | cv | 0.885 | | sampleLengths | | 0 | 64 | | 1 | 20 | | 2 | 2 | | 3 | 48 | | 4 | 14 | | 5 | 8 | | 6 | 52 | | 7 | 13 | | 8 | 11 | | 9 | 52 | | 10 | 7 | | 11 | 11 | | 12 | 67 | | 13 | 2 | | 14 | 13 | | 15 | 14 | | 16 | 39 | | 17 | 6 | | 18 | 10 | | 19 | 41 | | 20 | 22 | | 21 | 11 | | 22 | 14 | | 23 | 12 | | 24 | 24 | | 25 | 17 | | 26 | 4 | | 27 | 13 | | 28 | 29 | | 29 | 6 | | 30 | 25 | | 31 | 22 | | 32 | 6 | | 33 | 10 | | 34 | 45 | | 35 | 6 | | 36 | 36 | | 37 | 3 | | 38 | 5 | | 39 | 11 | | 40 | 10 | | 41 | 36 | | 42 | 7 | | 43 | 7 | | 44 | 42 | | 45 | 9 | | 46 | 16 | | 47 | 42 | | 48 | 2 | | 49 | 3 |
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| 93.37% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 118 | | matches | | 0 | "was darkened" | | 1 | "been dragged" | | 2 | "was etched" | | 3 | "was found" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 162 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 150 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 879 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.01478953356086462 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0034129692832764505 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 150 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 150 | | mean | 9.2 | | std | 6.88 | | cv | 0.748 | | sampleLengths | | 0 | 29 | | 1 | 15 | | 2 | 12 | | 3 | 8 | | 4 | 20 | | 5 | 2 | | 6 | 19 | | 7 | 12 | | 8 | 10 | | 9 | 7 | | 10 | 9 | | 11 | 5 | | 12 | 5 | | 13 | 3 | | 14 | 3 | | 15 | 9 | | 16 | 11 | | 17 | 17 | | 18 | 9 | | 19 | 3 | | 20 | 9 | | 21 | 4 | | 22 | 6 | | 23 | 5 | | 24 | 2 | | 25 | 7 | | 26 | 18 | | 27 | 5 | | 28 | 2 | | 29 | 7 | | 30 | 11 | | 31 | 7 | | 32 | 7 | | 33 | 4 | | 34 | 2 | | 35 | 15 | | 36 | 4 | | 37 | 21 | | 38 | 13 | | 39 | 12 | | 40 | 2 | | 41 | 6 | | 42 | 7 | | 43 | 4 | | 44 | 10 | | 45 | 5 | | 46 | 8 | | 47 | 7 | | 48 | 15 | | 49 | 4 |
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| 47.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.30666666666666664 | | totalSentences | 150 | | uniqueOpeners | 46 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 89 | | matches | | 0 | "Her sharp jaw set under" | | 1 | "She stopped at the threshold" | | 2 | "She held a worn leather" | | 3 | "She tucked hair behind her" | | 4 | "She moved past the token" | | 5 | "His pockets held a wallet," | | 6 | "It was dry, and the" | | 7 | "It caught light and threw" | | 8 | "She swept her torch along" | | 9 | "She picked it up with" | | 10 | "It was warm." | | 11 | "She walked the circle, counting" | | 12 | "She knelt again and examined" | | 13 | "It smelled faintly of iron" | | 14 | "She walked to the far" | | 15 | "She traced them with a" | | 16 | "She pulled out her phone" | | 17 | "She lifted one shoe and" | | 18 | "She stopped at the north" | | 19 | "It faced outward." |
| | ratio | 0.258 | |
| 44.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 74 | | totalSentences | 89 | | matches | | 0 | "Detective Harlow Quinn descended the" | | 1 | "The air held the damp" | | 2 | "Salt-and-pepper hair cropped close to" | | 3 | "Her sharp jaw set under" | | 4 | "She stopped at the threshold" | | 5 | "Eva Kowalski stood by the" | | 6 | "She held a worn leather" | | 7 | "Freckles stood out against her" | | 8 | "She tucked hair behind her" | | 9 | "Harlow didn’t answer." | | 10 | "She moved past the token" | | 11 | "Uniforms kept a line with" | | 12 | "The body lay on the" | | 13 | "A man in his thirties," | | 14 | "Crime scene techs knelt around" | | 15 | "the tech said" | | 16 | "The man’s shoes were polished," | | 17 | "His pockets held a wallet," | | 18 | "The phone screen was dark." | | 19 | "The neck showed a faint" |
| | ratio | 0.831 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 99.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 2 | | matches | | 0 | "Uniforms kept a line with tape that sagged in the damp." | | 1 | "Inside, stitched into the lining, a small tag of vellum with a sigil written in ink that had faded to brown." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 25 | | uselessAdditionCount | 1 | | matches | | 0 | "Harlow said again, quieter" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 25 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 57 | | tagDensity | 0.439 | | leniency | 0.877 | | rawRatio | 0 | | effectiveRatio | 0 | |