| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 55 | | tagDensity | 0.236 | | leniency | 0.473 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 80.60% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1031 | | totalAiIsmAdverbs | 4 | | 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) | |
| 95.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1031 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 75 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 75 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 116 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1038 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 16 | | wordCount | 698 | | uniqueNames | 7 | | maxNameDensity | 0.72 | | worstName | "Ptolemy" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Ptolemy" | | discoveredNames | | Rory | 3 | | Moreau | 1 | | Ptolemy | 5 | | Eva | 1 | | Old | 1 | | Lucien | 4 | | Tuesday | 1 |
| | persons | | 0 | "Rory" | | 1 | "Moreau" | | 2 | "Ptolemy" | | 3 | "Eva" | | 4 | "Lucien" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | 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 | 1038 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 68 | | mean | 15.26 | | std | 15.88 | | cv | 1.041 | | sampleLengths | | 0 | 29 | | 1 | 47 | | 2 | 7 | | 3 | 26 | | 4 | 5 | | 5 | 34 | | 6 | 16 | | 7 | 4 | | 8 | 3 | | 9 | 17 | | 10 | 25 | | 11 | 7 | | 12 | 32 | | 13 | 59 | | 14 | 14 | | 15 | 1 | | 16 | 54 | | 17 | 5 | | 18 | 3 | | 19 | 2 | | 20 | 16 | | 21 | 24 | | 22 | 20 | | 23 | 1 | | 24 | 5 | | 25 | 2 | | 26 | 20 | | 27 | 41 | | 28 | 27 | | 29 | 11 | | 30 | 6 | | 31 | 3 | | 32 | 3 | | 33 | 1 | | 34 | 1 | | 35 | 60 | | 36 | 12 | | 37 | 1 | | 38 | 3 | | 39 | 6 | | 40 | 2 | | 41 | 37 | | 42 | 6 | | 43 | 5 | | 44 | 10 | | 45 | 10 | | 46 | 4 | | 47 | 6 | | 48 | 4 | | 49 | 53 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 75 | | matches | | |
| 96.64% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 129 | | matches | | 0 | "was weighing" | | 1 | "was letting" |
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| 68.97% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 116 | | ratio | 0.026 | | matches | | 0 | "He put the mug down on the stack of books beside him — carefully, a coaster of paperbacks — and looked at her properly for the first time since she'd opened the door." | | 1 | "The crescent scar on her wrist caught the lamplight — pale little moon — and she covered it with her thumb without meaning to." | | 2 | "A flicker of something — amusement, or the older, quieter thing underneath it — crossed his mouth." |
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| 95.91% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 694 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.0446685878962536 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.008645533141210375 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 116 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 116 | | mean | 8.95 | | std | 8.23 | | cv | 0.92 | | sampleLengths | | 0 | 24 | | 1 | 5 | | 2 | 26 | | 3 | 12 | | 4 | 5 | | 5 | 4 | | 6 | 7 | | 7 | 7 | | 8 | 16 | | 9 | 3 | | 10 | 5 | | 11 | 12 | | 12 | 22 | | 13 | 10 | | 14 | 6 | | 15 | 4 | | 16 | 3 | | 17 | 14 | | 18 | 3 | | 19 | 14 | | 20 | 3 | | 21 | 8 | | 22 | 4 | | 23 | 3 | | 24 | 16 | | 25 | 8 | | 26 | 8 | | 27 | 37 | | 28 | 13 | | 29 | 7 | | 30 | 2 | | 31 | 10 | | 32 | 2 | | 33 | 2 | | 34 | 1 | | 35 | 22 | | 36 | 3 | | 37 | 7 | | 38 | 6 | | 39 | 3 | | 40 | 13 | | 41 | 5 | | 42 | 3 | | 43 | 2 | | 44 | 10 | | 45 | 6 | | 46 | 24 | | 47 | 5 | | 48 | 15 | | 49 | 1 |
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| 56.61% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.39655172413793105 | | totalSentences | 116 | | uniqueOpeners | 46 | |
| 56.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 59 | | matches | | 0 | "Then she stepped back and" |
| | ratio | 0.017 | |
| 23.39% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 59 | | matches | | 0 | "She shoved the chain back" | | 1 | "she said into Ptolemy's fur" | | 2 | "He tilted his head, that" | | 3 | "Her full name, from him," | | 4 | "She hated that." | | 5 | "She hated more that he'd" | | 6 | "She held the door another" | | 7 | "He set his cane against" | | 8 | "She put Ptolemy on the" | | 9 | "He never paced." | | 10 | "He just existed in a" | | 11 | "She set two mugs down" | | 12 | "She poured, watching the water" | | 13 | "She passed him the mug." | | 14 | "Their fingers brushed on the" | | 15 | "She meant for the warning" | | 16 | "She meant for the care" | | 17 | "She meant to sound less" | | 18 | "He was letting her keep" | | 19 | "He put the mug down" |
| | ratio | 0.492 | |
| 2.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 59 | | matches | | 0 | "The third deadbolt gave with" | | 1 | "That was her first mistake." | | 2 | "Lucien Moreau stood on the" | | 3 | "Platinum hair slicked back as" | | 4 | "Both fixed on her." | | 5 | "She shoved the chain back" | | 6 | "Ptolemy threaded between her ankles" | | 7 | "Rory scooped him up, the" | | 8 | "she said into Ptolemy's fur" | | 9 | "He tilted his head, that" | | 10 | "Her full name, from him," | | 11 | "She hated that." | | 12 | "She hated more that he'd" | | 13 | "She held the door another" | | 14 | "The flat was at its" | | 15 | "Lucien took it in without" | | 16 | "He set his cane against" | | 17 | "The word left her before" | | 18 | "She put Ptolemy on the" | | 19 | "The tap coughed." |
| | ratio | 0.915 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 59 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 1 | | matches | | 0 | "Lucien took it in without remark, which was its own species of remark." |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "He tilted, that half-smile doing the work his mouth refused to" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 55 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0 | | effectiveRatio | 0 | |