| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1412 | | 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) | |
| 92.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1412 | | totalAiIsms | 2 | | found | | | highlights | | |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 104 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 163 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 5 | | totalWords | 1416 | | ratio | 0.004 | | matches | | 0 | "Demonology for the Modern Idiot" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 76.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 946 | | uniqueNames | 13 | | maxNameDensity | 1.48 | | worstName | "Lucien" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | Brick | 2 | | Lane | 2 | | Moreau | 1 | | Eva | 7 | | French | 1 | | Lucien | 14 | | Tube | 1 | | Lucien- | 1 | | Modern | 1 | | Marseille | 1 | | Avaros | 2 | | Rory | 11 | | Ptolemy | 3 |
| | persons | | 0 | "Moreau" | | 1 | "Eva" | | 2 | "French" | | 3 | "Lucien" | | 4 | "Rory" | | 5 | "Ptolemy" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "Marseille" | | 3 | "Avaros" |
| | globalScore | 0.76 | | windowScore | 0.833 | |
| 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 | 1416 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 163 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 100 | | mean | 14.16 | | std | 14.91 | | cv | 1.053 | | sampleLengths | | 0 | 17 | | 1 | 5 | | 2 | 33 | | 3 | 3 | | 4 | 4 | | 5 | 73 | | 6 | 2 | | 7 | 34 | | 8 | 4 | | 9 | 3 | | 10 | 10 | | 11 | 45 | | 12 | 4 | | 13 | 6 | | 14 | 38 | | 15 | 12 | | 16 | 5 | | 17 | 6 | | 18 | 2 | | 19 | 14 | | 20 | 86 | | 21 | 26 | | 22 | 7 | | 23 | 2 | | 24 | 5 | | 25 | 36 | | 26 | 1 | | 27 | 7 | | 28 | 1 | | 29 | 11 | | 30 | 10 | | 31 | 37 | | 32 | 8 | | 33 | 18 | | 34 | 24 | | 35 | 2 | | 36 | 2 | | 37 | 25 | | 38 | 11 | | 39 | 5 | | 40 | 7 | | 41 | 10 | | 42 | 41 | | 43 | 9 | | 44 | 5 | | 45 | 5 | | 46 | 8 | | 47 | 19 | | 48 | 25 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 158 | | matches | (empty) | |
| 90.27% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 163 | | ratio | 0.018 | | matches | | 0 | "Lucien bent - not much, just enough to let the cat sniff his glove." | | 1 | "He turned and for a second his face did everything wrong - mouth tight, that black eye wider than the other." | | 2 | "Eva's handwriting stared up at her from a yellow legal pad - *Avaros gate, requires half-demon blood, do NOT let Lucien- * The rest smudged." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 945 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.015873015873015872 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0010582010582010583 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 163 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 163 | | mean | 8.69 | | std | 6.81 | | cv | 0.784 | | sampleLengths | | 0 | 17 | | 1 | 5 | | 2 | 33 | | 3 | 3 | | 4 | 4 | | 5 | 13 | | 6 | 19 | | 7 | 7 | | 8 | 16 | | 9 | 6 | | 10 | 12 | | 11 | 2 | | 12 | 5 | | 13 | 29 | | 14 | 4 | | 15 | 3 | | 16 | 5 | | 17 | 2 | | 18 | 3 | | 19 | 11 | | 20 | 14 | | 21 | 14 | | 22 | 6 | | 23 | 4 | | 24 | 6 | | 25 | 8 | | 26 | 4 | | 27 | 20 | | 28 | 6 | | 29 | 12 | | 30 | 5 | | 31 | 2 | | 32 | 4 | | 33 | 2 | | 34 | 14 | | 35 | 5 | | 36 | 22 | | 37 | 13 | | 38 | 27 | | 39 | 19 | | 40 | 4 | | 41 | 17 | | 42 | 5 | | 43 | 7 | | 44 | 2 | | 45 | 5 | | 46 | 15 | | 47 | 21 | | 48 | 1 | | 49 | 7 |
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| 44.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.2147239263803681 | | totalSentences | 163 | | uniqueOpeners | 35 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 97 | | matches | (empty) | | ratio | 0 | |
| 46.80% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 97 | | matches | | 0 | "Her throat closed." | | 1 | "She undid the chain." | | 2 | "His cane tipped against the" | | 3 | "His French edged the vowel." | | 4 | "He looked past her into" | | 5 | "He did not step inside." | | 6 | "His suit pulled across his" | | 7 | "She walked into the kitchenette" | | 8 | "She cleared a space on" | | 9 | "He set his cane against" | | 10 | "He draped the coat over" | | 11 | "He turned and for a" | | 12 | "She grabbed the kettle." | | 13 | "He did not touch her." | | 14 | "He braced both hands on" | | 15 | "His sleeve brushed her wrist." | | 16 | "He reached past her and" | | 17 | "His knuckles almost touched her" | | 18 | "She stared at the heterochromatic" | | 19 | "He stepped back." |
| | ratio | 0.433 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 93 | | totalSentences | 97 | | matches | | 0 | "The third deadbolt gave under" | | 1 | "Rory left the chain on." | | 2 | "The hallway on Brick Lane" | | 3 | "Her throat closed." | | 4 | "She undid the chain." | | 5 | "Lucien Moreau filled the doorway" | | 6 | "Platinum hair combed back from" | | 7 | "The left eye black from" | | 8 | "His cane tipped against the" | | 9 | "The books stacked in Eva's" | | 10 | "His French edged the vowel." | | 11 | "He looked past her into" | | 12 | "He did not step inside." | | 13 | "That was new." | | 14 | "Rory stepped back and the" | | 15 | "Ptolemy shot between her ankles," | | 16 | "Lucien bent - not much," | | 17 | "His suit pulled across his" | | 18 | "The words came out sharper" | | 19 | "Rory folded her arms." |
| | ratio | 0.959 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 97 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 1 | | matches | | 0 | "He looked past her into the flat, at the towers of paperbacks, the scrolls that unrolled over the kitchen counter, the open boxes that still smelled of Eva's sa…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |