| 95.21% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 27 | | adverbTagCount | 4 | | adverbTags | | 0 | "He laughed softly [softly]" | | 1 | "she said instead [instead]" | | 2 | "he said quietly [quietly]" | | 3 | "she said slowly [slowly]" |
| | dialogueSentences | 73 | | tagDensity | 0.37 | | leniency | 0.74 | | rawRatio | 0.148 | | effectiveRatio | 0.11 | |
| 78.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1860 | | totalAiIsmAdverbs | 8 | | found | | | highlights | | 0 | "slightly" | | 1 | "really" | | 2 | "softly" | | 3 | "lightly" | | 4 | "slowly" | | 5 | "very" |
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| 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) | |
| 56.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1860 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "echoed" | | 1 | "familiar" | | 2 | "lilt" | | 3 | "calculating" | | 4 | "tension" | | 5 | "wavered" | | 6 | "silence" | | 7 | "weight" | | 8 | "resolved" | | 9 | "could feel" | | 10 | "warmth" | | 11 | "intensity" | | 12 | "eyebrow" | | 13 | "traced" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 118 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 118 | | filterMatches | | | 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 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1861 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 1311 | | uniqueNames | 12 | | maxNameDensity | 0.38 | | worstName | "Aurora" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Lucien" | | discoveredNames | | Ptolemy | 3 | | Shoreditch | 1 | | Eva | 4 | | French | 1 | | London | 2 | | Moreau | 1 | | Hated | 1 | | January | 1 | | Aurora | 5 | | Lucien | 5 | | Brick | 1 | | Lane | 1 |
| | persons | | 0 | "Ptolemy" | | 1 | "Eva" | | 2 | "Moreau" | | 3 | "Aurora" | | 4 | "Lucien" |
| | places | | 0 | "Shoreditch" | | 1 | "London" | | 2 | "January" | | 3 | "Brick" | | 4 | "Lane" |
| | globalScore | 1 | | windowScore | 1 | |
| 18.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 4 | | matches | | 0 | "not quite a question, not quite a greeting" | | 1 | "not quite a greeting" | | 2 | "looked like a man standing on the edge of" | | 3 | "looked like home" | | 4 | "the suit he'd apparently worn to come beg on" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.537 | | wordCount | 1861 | | matches | | 0 | "Not touching, but close" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 161 | | matches | | 0 | "known that face" | | 1 | "hated that she" | | 2 | "Hated that she" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 71 | | mean | 26.21 | | std | 22.47 | | cv | 0.857 | | sampleLengths | | 0 | 70 | | 1 | 36 | | 2 | 6 | | 3 | 81 | | 4 | 26 | | 5 | 3 | | 6 | 39 | | 7 | 10 | | 8 | 51 | | 9 | 7 | | 10 | 1 | | 11 | 3 | | 12 | 2 | | 13 | 10 | | 14 | 66 | | 15 | 56 | | 16 | 14 | | 17 | 24 | | 18 | 40 | | 19 | 8 | | 20 | 18 | | 21 | 63 | | 22 | 31 | | 23 | 33 | | 24 | 9 | | 25 | 43 | | 26 | 65 | | 27 | 9 | | 28 | 47 | | 29 | 41 | | 30 | 5 | | 31 | 3 | | 32 | 45 | | 33 | 4 | | 34 | 51 | | 35 | 4 | | 36 | 5 | | 37 | 66 | | 38 | 62 | | 39 | 11 | | 40 | 12 | | 41 | 7 | | 42 | 18 | | 43 | 1 | | 44 | 44 | | 45 | 31 | | 46 | 2 | | 47 | 11 | | 48 | 80 | | 49 | 2 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 118 | | matches | | |
| 84.06% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 230 | | matches | | 0 | "wasn't expecting" | | 1 | "was working" | | 2 | "was calculating" | | 3 | "wasn't coming" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 161 | | ratio | 0.056 | | matches | | 0 | "The knock came at half past nine — three sharp raps that echoed through the narrow hallway and into the cluttered sitting room." | | 1 | "The impossible eyes — one amber, one black — catching the dim light of the corridor." | | 2 | "Aurora looked at him — really looked — and saw the tension in his shoulders, the slight tremor in the hand that gripped his cane." | | 3 | "She knew without looking that his hand would be resting on it — not gripping, just resting." | | 4 | "The words she wanted to say tangled in her throat — that she'd thought about him every day, that leaving had been the hardest thing she'd ever done, that she'd lain awake in Eva's guest room and replayed every conversation until she could quote them backwards." | | 5 | "He laughed — a real laugh this time, surprised out of him." | | 6 | "Considered him — the sharp suit, the elegant hands, the demon-eyes that had once frightened her and now just looked like home." | | 7 | "\"I am.\" He smiled, and it was the smile she'd been trying to forget for five months — the one that made him look younger, less guarded, almost human." | | 8 | "\"Neither am I.\" Lucien lifted her hand, pressed a kiss to her knuckles — courtly, old-fashioned, entirely him." |
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| 92.22% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1309 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 64 | | adverbRatio | 0.04889228418640183 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.012987012987012988 | |
| 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 | 11.56 | | std | 9.12 | | cv | 0.789 | | sampleLengths | | 0 | 23 | | 1 | 18 | | 2 | 4 | | 3 | 25 | | 4 | 16 | | 5 | 20 | | 6 | 6 | | 7 | 15 | | 8 | 3 | | 9 | 16 | | 10 | 15 | | 11 | 2 | | 12 | 30 | | 13 | 20 | | 14 | 6 | | 15 | 3 | | 16 | 2 | | 17 | 29 | | 18 | 8 | | 19 | 9 | | 20 | 1 | | 21 | 4 | | 22 | 23 | | 23 | 4 | | 24 | 3 | | 25 | 17 | | 26 | 7 | | 27 | 1 | | 28 | 3 | | 29 | 2 | | 30 | 3 | | 31 | 7 | | 32 | 13 | | 33 | 35 | | 34 | 7 | | 35 | 11 | | 36 | 5 | | 37 | 25 | | 38 | 5 | | 39 | 10 | | 40 | 11 | | 41 | 10 | | 42 | 4 | | 43 | 12 | | 44 | 12 | | 45 | 6 | | 46 | 22 | | 47 | 12 | | 48 | 4 | | 49 | 4 |
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| 41.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.32919254658385094 | | totalSentences | 161 | | uniqueOpeners | 53 | |
| 33.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 101 | | matches | | 0 | "Then whatever came after." |
| | ratio | 0.01 | |
| 14.06% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 52 | | totalSentences | 101 | | matches | | 0 | "She wasn't expecting anyone." | | 1 | "She crossed the room barefoot," | | 2 | "Her hand stopped on the" | | 3 | "She said it through the" | | 4 | "He wore charcoal, as always," | | 5 | "His voice carried that familiar" | | 6 | "She could picture him on" | | 7 | "She pressed her forehead against" | | 8 | "She opened the door just" | | 9 | "He looked the same." | | 10 | "He looked older." | | 11 | "Her eyes narrowed." | | 12 | "He held up a hand" | | 13 | "He stopped, and something shifted" | | 14 | "He looked like a man" | | 15 | "she said, and hated that" | | 16 | "He laughed softly, a sound" | | 17 | "She should have closed the" | | 18 | "he said, stepping inside" | | 19 | "She moved past him toward" |
| | ratio | 0.515 | |
| 34.26% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 101 | | matches | | 0 | "The knock came at half" | | 1 | "Aurora looked up from the" | | 2 | "She wasn't expecting anyone." | | 3 | "Eva was working the late" | | 4 | "She crossed the room barefoot," | | 5 | "The distortion made the figure" | | 6 | "Her hand stopped on the" | | 7 | "She said it through the" | | 8 | "The platinum hair." | | 9 | "The impossible eyes — one" | | 10 | "He wore charcoal, as always," | | 11 | "His voice carried that familiar" | | 12 | "She could picture him on" | | 13 | "She pressed her forehead against" | | 14 | "The nickname hit her somewhere" | | 15 | "The deadbolt slid back." | | 16 | "She opened the door just" | | 17 | "He looked the same." | | 18 | "He looked older." | | 19 | "Her eyes narrowed." |
| | ratio | 0.851 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 101 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 8 | | matches | | 0 | "The knock came at half past nine — three sharp raps that echoed through the narrow hallway and into the cluttered sitting room." | | 1 | "Maps of London with pins and annotations covered one entire wall, connected by red string in a web that would have looked paranoid if it weren't so meticulously…" | | 2 | "The words she wanted to say tangled in her throat — that she'd thought about him every day, that leaving had been the hardest thing she'd ever done, that she'd …" | | 3 | "She could feel the warmth of him, the almost-there presence that had haunted her dreams for months." | | 4 | "Considered him — the sharp suit, the elegant hands, the demon-eyes that had once frightened her and now just looked like home." | | 5 | "Ptolemy appeared in the kitchen doorway, stretched, and began washing his face as if two strangers holding hands in his territory was nothing unusual." | | 6 | "Inside, in a cramped flat above a curry house, two people who had walked away from each other sat with their hands intertwined and their cups of tea growing col…" | | 7 | "At the half-smile that had once made her angry and now just made her feel something she hadn't felt in a very long time." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 27 | | uselessAdditionCount | 3 | | matches | | 0 | "She said, not quite a question, not quite a greeting" | | 1 | "she said, not turning around" | | 2 | "She set, steam rising between them" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 1 | | fancyTags | | 0 | "He laughed softly (laugh)" |
| | dialogueSentences | 73 | | tagDensity | 0.178 | | leniency | 0.356 | | rawRatio | 0.077 | | effectiveRatio | 0.027 | |