| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 76 | | tagDensity | 0.276 | | leniency | 0.553 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.82% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1392 | | 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) | |
| 85.63% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1392 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "traced" | | 2 | "pulse" | | 3 | "flicked" |
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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 | 79 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 79 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 5 | | totalWords | 1392 | | ratio | 0.004 | | matches | | 0 | "follow" | | 1 | "chérie" | | 2 | "chérie" | | 3 | "told" | | 4 | "concern" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 729 | | uniqueNames | 7 | | maxNameDensity | 0.96 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Rory | 7 | | Moreau | 1 | | Lucien | 7 | | Sumerian | 1 | | Ptolemy | 5 | | Close | 2 | | Eva | 2 |
| | persons | | 0 | "Rory" | | 1 | "Moreau" | | 2 | "Lucien" | | 3 | "Ptolemy" | | 4 | "Eva" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | 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 | 1392 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 73 | | mean | 19.07 | | std | 16.64 | | cv | 0.873 | | sampleLengths | | 0 | 39 | | 1 | 53 | | 2 | 5 | | 3 | 28 | | 4 | 33 | | 5 | 9 | | 6 | 34 | | 7 | 6 | | 8 | 14 | | 9 | 30 | | 10 | 31 | | 11 | 2 | | 12 | 8 | | 13 | 24 | | 14 | 9 | | 15 | 43 | | 16 | 3 | | 17 | 31 | | 18 | 16 | | 19 | 6 | | 20 | 42 | | 21 | 1 | | 22 | 3 | | 23 | 5 | | 24 | 65 | | 25 | 9 | | 26 | 10 | | 27 | 8 | | 28 | 40 | | 29 | 11 | | 30 | 4 | | 31 | 12 | | 32 | 25 | | 33 | 2 | | 34 | 17 | | 35 | 5 | | 36 | 42 | | 37 | 19 | | 38 | 6 | | 39 | 4 | | 40 | 3 | | 41 | 62 | | 42 | 20 | | 43 | 7 | | 44 | 11 | | 45 | 28 | | 46 | 4 | | 47 | 48 | | 48 | 36 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 79 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 134 | | matches | | 0 | "was watching" | | 1 | "were approaching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 134 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 730 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.030136986301369864 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.00410958904109589 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 10.39 | | std | 7.68 | | cv | 0.739 | | sampleLengths | | 0 | 8 | | 1 | 18 | | 2 | 13 | | 3 | 16 | | 4 | 17 | | 5 | 9 | | 6 | 11 | | 7 | 5 | | 8 | 19 | | 9 | 9 | | 10 | 8 | | 11 | 19 | | 12 | 6 | | 13 | 9 | | 14 | 15 | | 15 | 19 | | 16 | 6 | | 17 | 14 | | 18 | 3 | | 19 | 22 | | 20 | 5 | | 21 | 31 | | 22 | 2 | | 23 | 8 | | 24 | 13 | | 25 | 11 | | 26 | 9 | | 27 | 7 | | 28 | 18 | | 29 | 18 | | 30 | 3 | | 31 | 14 | | 32 | 10 | | 33 | 7 | | 34 | 16 | | 35 | 6 | | 36 | 24 | | 37 | 13 | | 38 | 5 | | 39 | 1 | | 40 | 3 | | 41 | 5 | | 42 | 7 | | 43 | 7 | | 44 | 7 | | 45 | 6 | | 46 | 26 | | 47 | 5 | | 48 | 7 | | 49 | 6 |
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| 55.97% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3805970149253731 | | totalSentences | 134 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 62.25% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 71 | | matches | | 0 | "He leaned on the ivory-handled" | | 1 | "He glanced at the deadbolts" | | 2 | "She kept the chain on" | | 3 | "She set her shoulder against" | | 4 | "His jaw shifted." | | 5 | "He didn't wipe it away." | | 6 | "He sat on the landing," | | 7 | "She heard her own voice" | | 8 | "She shut the door, slid" | | 9 | "He stepped over the threshold" | | 10 | "He shifted his foot." | | 11 | "She scooped Ptolemy off the" | | 12 | "She dropped Ptolemy on the" | | 13 | "She crossed her arms" | | 14 | "he said at last" | | 15 | "He said it flat, as" | | 16 | "He drew a breath" | | 17 | "She always did." | | 18 | "She nodded at the cane" | | 19 | "He looked at the floor," |
| | ratio | 0.394 | |
| 9.30% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 71 | | matches | | 0 | "The first deadbolt slid back" | | 1 | "The second stuck, as it" | | 2 | "The third gave way, and" | | 3 | "Lucien Moreau stood on the" | | 4 | "The stairwell bulb buzzed above" | | 5 | "He leaned on the ivory-handled" | | 6 | "He glanced at the deadbolts" | | 7 | "She kept the chain on" | | 8 | "The smell of turmeric and" | | 9 | "She set her shoulder against" | | 10 | "His jaw shifted." | | 11 | "Water ran from the brim" | | 12 | "He didn't wipe it away." | | 13 | "Rory laughed, short and dry" | | 14 | "Something small and warm nudged" | | 15 | "Ptolemy slid past her shin," | | 16 | "He sat on the landing," | | 17 | "Lucien lowered himself with care," | | 18 | "Ptolemy butted them hard enough" | | 19 | "She heard her own voice" |
| | ratio | 0.901 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 1 | | matches | | 0 | "Lucien lifted his free hand, slowly, as if he were approaching a skittish animal, and let it hover beside her wrist." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 3 | | fancyTags | | 0 | "Rory laughed (laugh)" | | 1 | "Rory muttered (mutter)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 76 | | tagDensity | 0.118 | | leniency | 0.237 | | rawRatio | 0.333 | | effectiveRatio | 0.079 | |