| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.621 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1215 | | 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) | |
| 62.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1215 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "weight" | | 1 | "pulse" | | 2 | "calculated" | | 3 | "flicker" | | 4 | "warmth" | | 5 | "whisper" | | 6 | "facade" | | 7 | "chaotic" |
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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 | 55 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 55 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 67 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1184 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 930 | | uniqueNames | 7 | | maxNameDensity | 0.54 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Eva" | | discoveredNames | | Eva | 4 | | Yu-Fei | 3 | | Lucien | 5 | | Ptolemy | 5 | | Cardiff | 1 | | London | 1 | | French | 1 |
| | persons | | 0 | "Eva" | | 1 | "Yu-Fei" | | 2 | "Lucien" | | 3 | "Ptolemy" |
| | places | | 0 | "Cardiff" | | 1 | "London" | | 2 | "French" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | 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 | 1184 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 67 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 10 | | mean | 118.4 | | std | 68.93 | | cv | 0.582 | | sampleLengths | | 0 | 13 | | 1 | 109 | | 2 | 70 | | 3 | 134 | | 4 | 108 | | 5 | 255 | | 6 | 76 | | 7 | 212 | | 8 | 59 | | 9 | 148 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 55 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 157 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 67 | | ratio | 0.119 | | matches | | 0 | "His heterochromatic eyes—one amber, one black—pin me to the door frame, and I freeze, hand still curled around the brass knob." | | 1 | "\"You’re the one who vanished without a word. Why show up now?\" I keep my voice steady—cool-headed, like my father taught me—even as my pulse races." | | 2 | "The move is calculated—cool-headed thinking to assess his intentions, not a surrender." | | 3 | "The cramped one-bedroom swallows us whole, every surface stacked with Eva’s leather-bound books and rolled scrolls—some marked with faded red symbols I don’t recognize." | | 4 | "A loud clatter makes us both jump—Ptolemy has knocked over a stack of books, their leather covers thudding against the floor." | | 5 | "\"I’m not here to flatter you. I’m here to keep you alive.\" His voice is low, almost a whisper, and I can feel the weight of his secret—his demon side, the reason he vanished—hanging between us." | | 6 | "A clawed hand—black, scaled, oozing a viscous green liquid—snakes through the gap, grabbing the edge of the windowsill." | | 7 | "Another hand pushes through the gap, followed by a scaled arm that bulges with sinew, and I realize the demon is bigger than I thought—bigger than the imps we fended off before." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 610 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.02622950819672131 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.003278688524590164 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 67 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 67 | | mean | 17.67 | | std | 7.44 | | cv | 0.421 | | sampleLengths | | 0 | 13 | | 1 | 26 | | 2 | 25 | | 3 | 37 | | 4 | 21 | | 5 | 16 | | 6 | 29 | | 7 | 25 | | 8 | 4 | | 9 | 4 | | 10 | 20 | | 11 | 12 | | 12 | 22 | | 13 | 18 | | 14 | 26 | | 15 | 19 | | 16 | 9 | | 17 | 16 | | 18 | 12 | | 19 | 24 | | 20 | 20 | | 21 | 17 | | 22 | 19 | | 23 | 13 | | 24 | 24 | | 25 | 5 | | 26 | 19 | | 27 | 14 | | 28 | 8 | | 29 | 23 | | 30 | 14 | | 31 | 16 | | 32 | 21 | | 33 | 24 | | 34 | 10 | | 35 | 30 | | 36 | 11 | | 37 | 15 | | 38 | 8 | | 39 | 21 | | 40 | 21 | | 41 | 21 | | 42 | 13 | | 43 | 20 | | 44 | 12 | | 45 | 21 | | 46 | 37 | | 47 | 10 | | 48 | 9 | | 49 | 13 |
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| 58.71% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3880597014925373 | | totalSentences | 67 | | uniqueOpeners | 26 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 23.64% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 55 | | matches | | 0 | "I yank Eva’s flat door" | | 1 | "I’ve ducked in to grab" | | 2 | "His heterochromatic eyes—one amber, one" | | 3 | "He vanished before I could" | | 4 | "I shift my weight, crinkling" | | 5 | "His cane taps once more," | | 6 | "I keep my voice steady—cool—headed," | | 7 | "He nods at my left" | | 8 | "I step aside before he" | | 9 | "I glance at the kitchen" | | 10 | "He says the name like" | | 11 | "My abusive ex left Cardiff" | | 12 | "I step closer, my delivery" | | 13 | "He drops his hand, his" | | 14 | "I grab the delivery bag’s" | | 15 | "He picks up his ivory" | | 16 | "I set the note on" | | 17 | "I glance at the window," | | 18 | "He tucks the scroll into" | | 19 | "I grab the scuffed baseball" |
| | ratio | 0.491 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 55 | | matches | | 0 | "I yank Eva’s flat door" | | 1 | "I’ve ducked in to grab" | | 2 | "Ptolemy’s tabby fur brushes my" | | 3 | "His heterochromatic eyes—one amber, one" | | 4 | "Copper stings my nostrils, a" | | 5 | "The crescent scar on my" | | 6 | "He vanished before I could" | | 7 | "I shift my weight, crinkling" | | 8 | "The brittle paper cracks, and" | | 9 | "His cane taps once more," | | 10 | "The curry house’s spicy steam" | | 11 | "I keep my voice steady—cool—headed," | | 12 | "He nods at my left" | | 13 | "I step aside before he" | | 14 | "The move is calculated—cool—headed thinking" | | 15 | "The cramped one-bedroom swallows us" | | 16 | "A chipped ceramic mug of" | | 17 | "Ptolemy hisses again from under" | | 18 | "I glance at the kitchen" | | 19 | "He says the name like" |
| | ratio | 0.964 | |
| 90.91% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 55 | | matches | | | ratio | 0.018 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 2 | | matches | | 0 | "Tailored charcoal suit clinging to his broad shoulders, slicked platinum blond hair catching the faint neon glow from the curry house below, he holds an ivory-h…" | | 1 | "The paper glows faintly, a pale blue light that makes his amber eye flicker." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 5 | | matches | | 0 | "Lucien picks up, his fingers brushing the faded symbols" | | 1 | "I step, my delivery bag crinkling against my hip" | | 2 | "He drops, his jaw tightening again" | | 3 | "He tucks, his blade sliding back into the cane" | | 4 | "He nods, a faint smile tugging at his lips" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.034 | | leniency | 0.069 | | rawRatio | 0 | | effectiveRatio | 0 | |