| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 72 | | tagDensity | 0.236 | | leniency | 0.472 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.98% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1654 | | totalAiIsmAdverbs | 1 | | 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) | |
| 81.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1654 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "flickered" | | 1 | "navigate" | | 2 | "tension" | | 3 | "silence" | | 4 | "pulse" |
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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 | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 106 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 161 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1654 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 1091 | | uniqueNames | 11 | | maxNameDensity | 0.73 | | worstName | "Eva" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Eva" | | discoveredNames | | British | 1 | | Library | 1 | | Eva | 8 | | Margaret | 1 | | Moreau | 2 | | Yu-Fei | 1 | | London | 2 | | Lucien | 6 | | Evan | 1 | | Ptolemy | 6 | | Malphora | 1 |
| | persons | | 0 | "Eva" | | 1 | "Margaret" | | 2 | "Moreau" | | 3 | "Lucien" | | 4 | "Evan" | | 5 | "Ptolemy" |
| | places | | 0 | "British" | | 1 | "Library" | | 2 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 1 | | matches | | 0 | "d Margaret who apparently owed the undergroun" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1654 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 161 | | matches | | 0 | "chose that moment" | | 1 | "given that the" | | 2 | "hated that her" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 88 | | mean | 18.8 | | std | 19.15 | | cv | 1.019 | | sampleLengths | | 0 | 18 | | 1 | 54 | | 2 | 3 | | 3 | 46 | | 4 | 25 | | 5 | 4 | | 6 | 3 | | 7 | 2 | | 8 | 37 | | 9 | 43 | | 10 | 4 | | 11 | 15 | | 12 | 1 | | 13 | 1 | | 14 | 61 | | 15 | 14 | | 16 | 3 | | 17 | 42 | | 18 | 16 | | 19 | 1 | | 20 | 3 | | 21 | 8 | | 22 | 35 | | 23 | 8 | | 24 | 2 | | 25 | 84 | | 26 | 4 | | 27 | 4 | | 28 | 35 | | 29 | 13 | | 30 | 14 | | 31 | 29 | | 32 | 6 | | 33 | 73 | | 34 | 5 | | 35 | 9 | | 36 | 6 | | 37 | 2 | | 38 | 37 | | 39 | 63 | | 40 | 4 | | 41 | 5 | | 42 | 22 | | 43 | 17 | | 44 | 43 | | 45 | 2 | | 46 | 12 | | 47 | 4 | | 48 | 49 | | 49 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 196 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 161 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1098 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.025500910746812388 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007285974499089253 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 161 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 161 | | mean | 10.27 | | std | 9.29 | | cv | 0.905 | | sampleLengths | | 0 | 5 | | 1 | 1 | | 2 | 1 | | 3 | 11 | | 4 | 17 | | 5 | 17 | | 6 | 10 | | 7 | 10 | | 8 | 3 | | 9 | 30 | | 10 | 16 | | 11 | 3 | | 12 | 2 | | 13 | 20 | | 14 | 4 | | 15 | 3 | | 16 | 2 | | 17 | 3 | | 18 | 4 | | 19 | 30 | | 20 | 24 | | 21 | 19 | | 22 | 4 | | 23 | 6 | | 24 | 6 | | 25 | 3 | | 26 | 1 | | 27 | 1 | | 28 | 6 | | 29 | 4 | | 30 | 13 | | 31 | 38 | | 32 | 14 | | 33 | 3 | | 34 | 15 | | 35 | 10 | | 36 | 17 | | 37 | 5 | | 38 | 11 | | 39 | 1 | | 40 | 3 | | 41 | 8 | | 42 | 10 | | 43 | 4 | | 44 | 5 | | 45 | 16 | | 46 | 8 | | 47 | 2 | | 48 | 6 | | 49 | 11 |
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| 55.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.36645962732919257 | | totalSentences | 161 | | uniqueOpeners | 59 | |
| 72.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 92 | | matches | | 0 | "Then the sound of something" | | 1 | "Just a single symbol drawn" |
| | ratio | 0.022 | |
| 72.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 92 | | matches | | 0 | "She crossed the cramped flat" | | 1 | "She didn't move." | | 2 | "He looked exactly as she" | | 3 | "He tilted his head" | | 4 | "It had been his thing," | | 5 | "He looked up" | | 6 | "He straightened, adjusting his cuff" | | 7 | "She studied him through the" | | 8 | "He carried tension the way" | | 9 | "She'd learned to read him" | | 10 | "She kept her face still." | | 11 | "His voice dropped" | | 12 | "She gripped the edge of" | | 13 | "He paused, watching her with" | | 14 | "She released the door chain." | | 15 | "He stepped inside like he" | | 16 | "His gaze swept across Eva's" | | 17 | "His expression didn't change, but" | | 18 | "He picked up a scroll," | | 19 | "He might have been reading" |
| | ratio | 0.37 | |
| 19.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 92 | | matches | | 0 | "The knock came three times." | | 1 | "The kind that didn't ask" | | 2 | "Rory set down the takeaway" | | 3 | "Ptolemy lifted his head from" | | 4 | "Eva wasn't due back from" | | 5 | "The curry house downstairs had" | | 6 | "Another three knocks." | | 7 | "She crossed the cramped flat" | | 8 | "The deadbolts turned one, two," | | 9 | "Both fixed on her with" | | 10 | "She didn't move." | | 11 | "The hallway behind him smelled" | | 12 | "Lucien Moreau leaned against the" | | 13 | "He looked exactly as she" | | 14 | "He tilted his head" | | 15 | "The platinum hair caught the" | | 16 | "The name landed like a" | | 17 | "Nobody called her that." | | 18 | "It had been his thing," | | 19 | "Ptolemy chose that moment to" |
| | ratio | 0.88 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 7 | | matches | | 0 | "The hallway behind him smelled of turmeric and old carpet, and the single bulb above the stairwell flickered in a rhythm that suggested the wiring had opinions …" | | 1 | "Lucien Moreau leaned against the doorframe in a charcoal suit that cost more than her annual rent, his ivory-handled cane tucked under one arm." | | 2 | "Ptolemy butted against it with the enthusiasm of a creature who'd never learned the meaning of self-preservation." | | 3 | "Behind her, Ptolemy's tail swished against a stack of Eva's scrolls, sending one sliding to the floor." | | 4 | "He stepped inside like he owned the place, which was offensive given that the entire flat was maybe twelve square metres and he was taking up a third of it." | | 5 | "The cane clicked against the floorboards as he planted it, and for a moment they stood facing each other across Eva's cramped living room, surrounded by the acc…" | | 6 | "Then the sound of something sliding through the letterbox, a thin envelope that landed on the threadbare welcome mat with a soft, deliberate thump." |
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| 95.59% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 1 | | matches | | 0 | "His voice had, the voice of a man who'd stopped asking" |
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| 94.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 4 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she corrected (correct)" | | 2 | "he repeated (repeat)" | | 3 | "she continued (continue)" |
| | dialogueSentences | 72 | | tagDensity | 0.056 | | leniency | 0.111 | | rawRatio | 1 | | effectiveRatio | 0.111 | |