| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 2 | | adverbTags | | 0 | "He turned back [back]" | | 1 | "Aurora said finally [finally]" |
| | dialogueSentences | 83 | | tagDensity | 0.181 | | leniency | 0.361 | | rawRatio | 0.133 | | effectiveRatio | 0.048 | |
| 75.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1446 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "very" | | 1 | "carefully" | | 2 | "precisely" | | 3 | "really" | | 4 | "completely" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 61.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1446 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "silk" | | 1 | "depths" | | 2 | "affection" | | 3 | "flicker" | | 4 | "throbbed" | | 5 | "symphony" | | 6 | "silence" | | 7 | "constructed" | | 8 | "sense of" | | 9 | "perfect" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 74 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 141 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 1432 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 23 | | unquotedAttributions | 0 | | matches | (empty) | |
| 58.98% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 824 | | uniqueNames | 9 | | maxNameDensity | 1.82 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 15 | | French | 1 | | Lucien | 11 | | Eva | 8 | | Christ | 1 | | Three | 4 | | Evan | 1 | | London | 1 | | Ptolemy | 2 |
| | persons | | 0 | "Aurora" | | 1 | "French" | | 2 | "Lucien" | | 3 | "Eva" | | 4 | "Evan" | | 5 | "Ptolemy" |
| | places | | | globalScore | 0.59 | | windowScore | 0.833 | |
| 51.96% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like a promise and a threat wrappe" | | 1 | "tasted like at three in the morning" |
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| 60.34% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.397 | | wordCount | 1432 | | matches | | 0 | "Not inside, not yet, but close enough" | | 1 | "not yet, but close enough" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 141 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 91 | | mean | 15.74 | | std | 16.46 | | cv | 1.046 | | sampleLengths | | 0 | 28 | | 1 | 7 | | 2 | 4 | | 3 | 48 | | 4 | 2 | | 5 | 28 | | 6 | 3 | | 7 | 35 | | 8 | 2 | | 9 | 2 | | 10 | 8 | | 11 | 8 | | 12 | 31 | | 13 | 3 | | 14 | 3 | | 15 | 51 | | 16 | 17 | | 17 | 5 | | 18 | 3 | | 19 | 46 | | 20 | 5 | | 21 | 10 | | 22 | 8 | | 23 | 5 | | 24 | 20 | | 25 | 4 | | 26 | 40 | | 27 | 5 | | 28 | 41 | | 29 | 22 | | 30 | 2 | | 31 | 1 | | 32 | 1 | | 33 | 34 | | 34 | 3 | | 35 | 3 | | 36 | 3 | | 37 | 50 | | 38 | 6 | | 39 | 14 | | 40 | 56 | | 41 | 1 | | 42 | 2 | | 43 | 6 | | 44 | 21 | | 45 | 8 | | 46 | 6 | | 47 | 19 | | 48 | 1 | | 49 | 55 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 74 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 144 | | matches | | 0 | "was memorising" | | 1 | "was sitting" |
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| 21.28% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 141 | | ratio | 0.043 | | matches | | 0 | "His mismatched eyes—amber and black—swept over her with the precision of a man cataloguing weaknesses." | | 1 | "Books cascaded from every surface—grimoires bound in cracked leather, university texts on comparative mythology, takeaway menus written in languages that predated Christ." | | 2 | "Outside, the evening rush hour continued—buses grinding through gears, tourists laughing their way toward pubs, the eternal symphony of London after dark." | | 3 | "Three months of silence, of hurt feelings and abandoned conversations, and it came down to this—two broken people standing in a cluttered flat above a curry house, talking about murder like other people discussed the weather." | | 4 | "Aurora looked around Eva's flat—at the books and scrolls and carefully organised chaos of a brilliant mind trying to make sense of an impossible world." | | 5 | "She took in the scene—Aurora pressed against the kitchen counter, Lucien standing by the window like he owned the place, Ptolemy purring at his feet." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 836 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 32 | | adverbRatio | 0.03827751196172249 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007177033492822967 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 141 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 141 | | mean | 10.16 | | std | 9.12 | | cv | 0.898 | | sampleLengths | | 0 | 15 | | 1 | 13 | | 2 | 7 | | 3 | 4 | | 4 | 14 | | 5 | 19 | | 6 | 15 | | 7 | 2 | | 8 | 8 | | 9 | 20 | | 10 | 3 | | 11 | 5 | | 12 | 12 | | 13 | 6 | | 14 | 10 | | 15 | 2 | | 16 | 2 | | 17 | 2 | | 18 | 8 | | 19 | 4 | | 20 | 4 | | 21 | 20 | | 22 | 11 | | 23 | 3 | | 24 | 3 | | 25 | 15 | | 26 | 22 | | 27 | 14 | | 28 | 10 | | 29 | 7 | | 30 | 5 | | 31 | 3 | | 32 | 4 | | 33 | 22 | | 34 | 2 | | 35 | 18 | | 36 | 5 | | 37 | 10 | | 38 | 8 | | 39 | 5 | | 40 | 12 | | 41 | 8 | | 42 | 4 | | 43 | 10 | | 44 | 11 | | 45 | 19 | | 46 | 5 | | 47 | 10 | | 48 | 31 | | 49 | 4 |
| |
| 65.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.41843971631205673 | | totalSentences | 141 | | uniqueOpeners | 59 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 67 | | matches | | 0 | "His mismatched eyes—amber and black—swept" | | 1 | "She'd forgotten how he said" | | 2 | "He stepped forward" | | 3 | "His smile was knife-thin." | | 4 | "She stepped aside." | | 5 | "He picked up a scroll" | | 6 | "He set it down carefully" | | 7 | "He turned back to her" | | 8 | "His amber eye caught the" | | 9 | "He stepped closer" | | 10 | "She slid down the kitchen" | | 11 | "He reached out, almost touched" | | 12 | "His smile was sharp as" | | 13 | "His cane clicked against the" | | 14 | "She could see her reflection" | | 15 | "Her friend was out there" | | 16 | "He straightened his tie, adjusted" | | 17 | "She took in the scene—Aurora" | | 18 | "she said into the phone," |
| | ratio | 0.284 | |
| 19.70% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 67 | | matches | | 0 | "The deadbolts clicked one by" | | 1 | "Eva's voice carried through the" | | 2 | "The door swung open." | | 3 | "Lucien stood in the cramped" | | 4 | "The ivory-handled cane rested against" | | 5 | "His mismatched eyes—amber and black—swept" | | 6 | "The French rolled off his" | | 7 | "She'd forgotten how he said" | | 8 | "He stepped forward" | | 9 | "Something that reminded her of" | | 10 | "His smile was knife-thin." | | 11 | "Ptolemy appeared from the depths" | | 12 | "The tabby cat had always" | | 13 | "She stepped aside." | | 14 | "Lucien moved through Eva's flat" | | 15 | "Books cascaded from every surface—grimoires" | | 16 | "He picked up a scroll" | | 17 | "He set it down carefully" | | 18 | "Aurora crossed her arms." | | 19 | "The movement pulled her delivery" |
| | ratio | 0.881 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 61.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 4 | | matches | | 0 | "Something that reminded her of burnt offerings and old churches." | | 1 | "The small crescent-shaped scar always ached when she was stressed, a childhood accident that had never properly healed." | | 2 | "*People who matter.* Three months ago, she'd mattered enough for late-night conversations in languages she didn't speak." | | 3 | "She could see her reflection in the black pupil of his inhuman eye, could smell the sulfur that clung to his clothes like expensive aftershave." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "Aurora muttered (mutter)" | | 1 | "he murmured (murmur)" |
| | dialogueSentences | 83 | | tagDensity | 0.06 | | leniency | 0.12 | | rawRatio | 0.4 | | effectiveRatio | 0.048 | |