| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 6 | | adverbTags | | 0 | "The endearment stung more [more]" | | 1 | "She gestured vaguely [vaguely]" | | 2 | "she said finally [finally]" | | 3 | "she said instead [instead]" | | 4 | "He moved close [close]" | | 5 | "Lucien said quietly [quietly]" |
| | dialogueSentences | 58 | | tagDensity | 0.448 | | leniency | 0.897 | | rawRatio | 0.231 | | effectiveRatio | 0.207 | |
| 70.78% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1369 | | totalAiIsmAdverbs | 8 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | |
| | highlights | | 0 | "deliberately" | | 1 | "carefully" | | 2 | "truly" | | 3 | "softly" | | 4 | "slowly" | | 5 | "completely" |
| |
| 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) | |
| 63.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1369 | | totalAiIsms | 10 | | found | | 0 | | | 1 | | word | "the last thing" | | count | 1 |
| | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | |
| | highlights | | 0 | "lilt" | | 1 | "the last thing" | | 2 | "echoed" | | 3 | "stomach" | | 4 | "disrupting" | | 5 | "tension" | | 6 | "could feel" | | 7 | "intensity" | | 8 | "constructed" | | 9 | "silence" |
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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 | 88 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1384 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 1 | | matches | | 0 | "Then, to her surprise, he laughed — a short, bitter sound." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 972 | | uniqueNames | 12 | | maxNameDensity | 0.72 | | worstName | "Lucien" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | Rory | 3 | | Lucien | 7 | | Moreau | 3 | | Eva | 3 | | French | 1 | | Golden | 1 | | Empress | 1 | | Books | 1 | | London | 2 | | Guilt | 1 | | Evan | 2 | | Cardiff | 1 |
| | persons | | 0 | "Rory" | | 1 | "Lucien" | | 2 | "Moreau" | | 3 | "Eva" | | 4 | "Guilt" | | 5 | "Evan" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like it cost more than her entire" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.723 | | wordCount | 1384 | | matches | | 0 | "not—\" But the words died in her throat because there was truth in what" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 118 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 24.28 | | std | 22.63 | | cv | 0.932 | | sampleLengths | | 0 | 17 | | 1 | 82 | | 2 | 16 | | 3 | 57 | | 4 | 15 | | 5 | 55 | | 6 | 20 | | 7 | 4 | | 8 | 34 | | 9 | 5 | | 10 | 5 | | 11 | 63 | | 12 | 5 | | 13 | 8 | | 14 | 8 | | 15 | 3 | | 16 | 1 | | 17 | 47 | | 18 | 57 | | 19 | 5 | | 20 | 25 | | 21 | 8 | | 22 | 35 | | 23 | 18 | | 24 | 5 | | 25 | 57 | | 26 | 56 | | 27 | 6 | | 28 | 14 | | 29 | 60 | | 30 | 4 | | 31 | 47 | | 32 | 7 | | 33 | 29 | | 34 | 8 | | 35 | 3 | | 36 | 1 | | 37 | 19 | | 38 | 59 | | 39 | 8 | | 40 | 2 | | 41 | 41 | | 42 | 7 | | 43 | 5 | | 44 | 3 | | 45 | 72 | | 46 | 37 | | 47 | 52 | | 48 | 33 | | 49 | 7 |
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| 97.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 88 | | matches | | 0 | "was slicked" | | 1 | "was exhausted" |
| |
| 46.74% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 174 | | matches | | 0 | "was landing" | | 1 | "wasn't going" | | 2 | "was catching" | | 3 | "was falling" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 10 | | totalSentences | 118 | | ratio | 0.085 | | matches | | 0 | "His heterochromatic eyes — that unnerving amber-and-black combination — swept over her face with the same careful assessment she remembered from six months ago." | | 1 | "Books and scrolls covered every available surface — the coffee table, the kitchen counter, even stacked in precarious towers along the baseboards." | | 2 | "Lucien Moreau, the information broker, the fixer, the man who moved through London's supernatural underworld like he owned it — he never showed more than he wanted to show." | | 3 | "Except for those few hours when they'd both let their guards down and—" | | 4 | "In the cramped flat, it brought him near enough that she could smell his cologne — something expensive and woodsy that reminded her of dark forests and dangerous promises." | | 5 | "Then, to her surprise, he laughed — a short, bitter sound." | | 6 | "The crescent-shaped scar on her left wrist caught the lamplight as she unconsciously rubbed it — an old habit from childhood, a nervous tell Lucien had learned to read months ago." | | 7 | "Those eyes — amber and black, human and demon — studied her with an intensity that made her breath catch." | | 8 | "She should protect herself because that's what survivors did — they learned, they adapted, they built walls." | | 9 | "Something shifted in his expression — relief, hope, something tender and dangerous all at once." |
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| 96.09% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 967 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 43 | | adverbRatio | 0.04446742502585315 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.01344364012409514 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 11.73 | | std | 8.84 | | cv | 0.753 | | sampleLengths | | 0 | 17 | | 1 | 26 | | 2 | 24 | | 3 | 18 | | 4 | 14 | | 5 | 16 | | 6 | 13 | | 7 | 24 | | 8 | 20 | | 9 | 15 | | 10 | 18 | | 11 | 22 | | 12 | 15 | | 13 | 7 | | 14 | 13 | | 15 | 4 | | 16 | 18 | | 17 | 11 | | 18 | 5 | | 19 | 5 | | 20 | 5 | | 21 | 6 | | 22 | 10 | | 23 | 29 | | 24 | 5 | | 25 | 13 | | 26 | 1 | | 27 | 4 | | 28 | 8 | | 29 | 8 | | 30 | 3 | | 31 | 1 | | 32 | 5 | | 33 | 29 | | 34 | 13 | | 35 | 12 | | 36 | 9 | | 37 | 6 | | 38 | 3 | | 39 | 27 | | 40 | 5 | | 41 | 22 | | 42 | 3 | | 43 | 8 | | 44 | 6 | | 45 | 29 | | 46 | 6 | | 47 | 10 | | 48 | 2 | | 49 | 4 |
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| 66.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4491525423728814 | | totalSentences | 118 | | uniqueOpeners | 53 | |
| 40.65% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 82 | | matches | | 0 | "Then, to her surprise, he" |
| | ratio | 0.012 | |
| 24.88% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 82 | | matches | | 0 | "He stood framed in the" | | 1 | "His heterochromatic eyes — that" | | 2 | "His platinum hair was slicked" | | 3 | "His voice carried that faint" | | 4 | "She'd been running deliveries for" | | 5 | "He gestured with his cane" | | 6 | "She pushed past him, deliberately" | | 7 | "She heard the door close" | | 8 | "She crossed her arms" | | 9 | "It was the only sign" | | 10 | "She wasn't going there." | | 11 | "He took a step closer." | | 12 | "She turned away, busying herself" | | 13 | "Her hands were steady, at" | | 14 | "She gestured vaguely with one" | | 15 | "His tone was dry, almost" | | 16 | "She whirled back to face" | | 17 | "He moved closer again, and" | | 18 | "His mismatched eyes searched her" | | 19 | "He stopped himself, jaw tightening" |
| | ratio | 0.488 | |
| 39.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 82 | | matches | | 0 | "The door swung open on" | | 1 | "He stood framed in the" | | 2 | "His heterochromatic eyes — that" | | 3 | "His platinum hair was slicked" | | 4 | "The charcoal suit he wore" | | 5 | "His voice carried that faint" | | 6 | "The words came out sharper" | | 7 | "She'd been running deliveries for" | | 8 | "The last thing she'd expected" | | 9 | "He gestured with his cane" | | 10 | "She pushed past him, deliberately" | | 11 | "Books and scrolls covered every" | | 12 | "Ptolemy, Eva's tabby cat, watched" | | 13 | "She heard the door close" | | 14 | "The sound of all three" | | 15 | "Rory dropped her delivery bag" | | 16 | "She crossed her arms" | | 17 | "A muscle twitched in his" | | 18 | "It was the only sign" | | 19 | "Lucien Moreau, the information broker," |
| | ratio | 0.841 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 82 | | matches | | 0 | "Because I was falling for" | | 1 | "Because for the first time" | | 2 | "Because you're half-demon and I'm" | | 3 | "Because I knew I'd get" |
| | ratio | 0.049 | |
| 27.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 5 | | matches | | 0 | "She'd been running deliveries for the Golden Empress all evening and was exhausted, her feet aching, her delivery bag still slung across her chest." | | 1 | "Lucien Moreau, the information broker, the fixer, the man who moved through London's supernatural underworld like he owned it — he never showed more than he wan…" | | 2 | "In the cramped flat, it brought him near enough that she could smell his cologne — something expensive and woodsy that reminded her of dark forests and dangerou…" | | 3 | "Those eyes — amber and black, human and demon — studied her with an intensity that made her breath catch." | | 4 | "She looked up at him, at this impossible man who was half-demon and half-human and somehow completely himself." |
| |
| 67.31% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 3 | | matches | | 0 | "He stopped, jaw tightening" | | 1 | "he said, his voice softer now" | | 2 | "The word came out, defensive" |
| |
| 98.28% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 3 | | fancyTags | | 0 | "He repeated (repeat)" | | 1 | "he interrupted (interrupt)" | | 2 | "she whispered (whisper)" |
| | dialogueSentences | 58 | | tagDensity | 0.155 | | leniency | 0.31 | | rawRatio | 0.333 | | effectiveRatio | 0.103 | |