| 75.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 2 | | adverbTags | | 0 | "he said softly [softly]" | | 1 | "Silas said softly [softly]" |
| | dialogueSentences | 30 | | tagDensity | 0.533 | | leniency | 1 | | rawRatio | 0.125 | | effectiveRatio | 0.125 | |
| 81.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1364 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "quickly" | | 1 | "really" | | 2 | "very" | | 3 | "softly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 30.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1364 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "flickered" | | 1 | "rhythmic" | | 2 | "mechanical" | | 3 | "scanning" | | 4 | "weight" | | 5 | "tension" | | 6 | "navigated" | | 7 | "flicker" | | 8 | "silence" | | 9 | "oppressive" | | 10 | "familiar" | | 11 | "effortless" | | 12 | "unspoken" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "the air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 74 | | matches | (empty) | |
| 84.94% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 74 | | filterMatches | (empty) | | hedgeMatches | | 0 | "seemed to" | | 1 | "managed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 88 | | 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 | 1358 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1063 | | uniqueNames | 10 | | maxNameDensity | 1.03 | | worstName | "Julian" | | maxWindowNameDensity | 3 | | worstWindowName | "Julian" | | discoveredNames | | Raven | 1 | | Nest | 2 | | Soho | 2 | | Golden | 1 | | Empress | 1 | | Silas | 7 | | London | 1 | | Aurora | 9 | | Julian | 11 | | Cardiff | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Empress" | | 3 | "Silas" | | 4 | "Aurora" | | 5 | "Julian" |
| | places | | 0 | "Soho" | | 1 | "Golden" | | 2 | "London" | | 3 | "Cardiff" |
| | globalScore | 0.983 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like they had seen too much" |
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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 | 1358 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 88 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 36.7 | | std | 21.39 | | cv | 0.583 | | sampleLengths | | 0 | 66 | | 1 | 87 | | 2 | 55 | | 3 | 18 | | 4 | 43 | | 5 | 32 | | 6 | 62 | | 7 | 10 | | 8 | 11 | | 9 | 47 | | 10 | 57 | | 11 | 26 | | 12 | 15 | | 13 | 52 | | 14 | 50 | | 15 | 19 | | 16 | 36 | | 17 | 82 | | 18 | 20 | | 19 | 37 | | 20 | 53 | | 21 | 25 | | 22 | 44 | | 23 | 29 | | 24 | 44 | | 25 | 21 | | 26 | 7 | | 27 | 76 | | 28 | 7 | | 29 | 47 | | 30 | 41 | | 31 | 9 | | 32 | 41 | | 33 | 45 | | 34 | 11 | | 35 | 12 | | 36 | 21 |
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| 81.56% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 74 | | matches | | 0 | "was dressed" | | 1 | "been groomed" | | 2 | "being asked" | | 3 | "were narrowed" | | 4 | "been terrified" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 186 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 4 | | flaggedSentences | 9 | | totalSentences | 88 | | ratio | 0.102 | | matches | | 0 | "She didn’t order a drink immediately; she just watched the room." | | 1 | "\"The worst,\" she replied, rubbing the small crescent-shaped scar on her left wrist—a nervous habit she hadn't managed to kill in a decade." | | 2 | "The clink of glasses, the distant laughter, the hum of the neon sign—all of it fell away, leaving only the two of them in a vacuum of shared history." | | 3 | "He looked at her—really looked at her—taking in the delivery uniform, the tired shadows under her bright blue eyes, and the way she held herself, guarded and coiled." | | 4 | "It wasn't a compliment; it was an observation of a failed investment." | | 5 | "And then, the crash had come—a messy, jagged break involving secrets and betrayals that neither of them had ever properly named." | | 6 | "For a second, she wasn't twenty-five and exhausted; she was twenty, sitting in a rain-slicked courtyard in Cardiff, believing that they were the only two people in the world who understood the architecture of ambition." | | 7 | "For a heartbeat, the mask slipped, and she saw the hollowed-out boy beneath the charcoal suit—the one who had been terrified of being ordinary, the one who had pushed her away the moment she became a mirror reflecting his own insecurities." | | 8 | "He remembered the day she’d gotten it; he remembered the way he had held her hand while she cried, promising her that they would always be the exception to the rule." |
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| 93.32% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1079 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 44 | | adverbRatio | 0.04077849860982391 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.013901760889712697 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 88 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 88 | | mean | 15.43 | | std | 8.52 | | cv | 0.552 | | sampleLengths | | 0 | 23 | | 1 | 20 | | 2 | 23 | | 3 | 18 | | 4 | 24 | | 5 | 11 | | 6 | 34 | | 7 | 20 | | 8 | 19 | | 9 | 16 | | 10 | 18 | | 11 | 23 | | 12 | 20 | | 13 | 6 | | 14 | 26 | | 15 | 8 | | 16 | 28 | | 17 | 26 | | 18 | 2 | | 19 | 8 | | 20 | 11 | | 21 | 3 | | 22 | 15 | | 23 | 29 | | 24 | 9 | | 25 | 6 | | 26 | 15 | | 27 | 13 | | 28 | 14 | | 29 | 3 | | 30 | 9 | | 31 | 14 | | 32 | 10 | | 33 | 5 | | 34 | 10 | | 35 | 14 | | 36 | 28 | | 37 | 9 | | 38 | 12 | | 39 | 29 | | 40 | 11 | | 41 | 8 | | 42 | 13 | | 43 | 23 | | 44 | 16 | | 45 | 27 | | 46 | 18 | | 47 | 21 | | 48 | 8 | | 49 | 12 |
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| 42.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3068181818181818 | | totalSentences | 88 | | uniqueOpeners | 27 | |
| 50.51% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 66 | | matches | | 0 | "Instead, he had become a" |
| | ratio | 0.015 | |
| 44.24% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 66 | | matches | | 0 | "She leaned against the mahogany" | | 1 | "Her delivery bag, smelling faintly" | | 2 | "She didn’t order a drink" | | 3 | "He didn’t look up as" | | 4 | "she replied, rubbing the small" | | 5 | "He was dressed in a" | | 6 | "she whispered, the name tasting" | | 7 | "His gaze locked onto hers," | | 8 | "He hadn't grown soft or" | | 9 | "His eyes, once wide with" | | 10 | "His voice was a polished" | | 11 | "she said, her voice regaining" | | 12 | "He stopped a few feet" | | 13 | "He looked at her—really looked" | | 14 | "It wasn't a compliment; it" | | 15 | "she replied, shifting her weight" | | 16 | "He had been the bridge" | | 17 | "he said, glancing around the" | | 18 | "He didn't speak, but his" | | 19 | "He stepped a fraction closer," |
| | ratio | 0.439 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 66 | | matches | | 0 | "The green neon sign of" | | 1 | "Aurora stepped through the door," | | 2 | "She leaned against the mahogany" | | 3 | "Her delivery bag, smelling faintly" | | 4 | "She didn’t order a drink" | | 5 | "The walls were a gallery" | | 6 | "Silas was behind the bar," | | 7 | "He didn’t look up as" | | 8 | "The silver signet ring on" | | 9 | "Silas asked, his voice a" | | 10 | "she replied, rubbing the small" | | 11 | "Silas offered a thin, dry" | | 12 | "Aurora laughed, but the sound" | | 13 | "The door opened again, admitting" | | 14 | "He was dressed in a" | | 15 | "The air in her lungs" | | 16 | "she whispered, the name tasting" | | 17 | "The man stopped." | | 18 | "His gaze locked onto hers," | | 19 | "The clink of glasses, the" |
| | ratio | 0.909 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 66 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 9 | | matches | | 0 | "Aurora stepped through the door, the bell chiming a lonely note that was quickly swallowed by the low murmur of the after-work crowd." | | 1 | "The door opened again, admitting a gust of cold London air and a man who looked entirely out of place in the dim, lived-in comfort of the Nest." | | 2 | "He was dressed in a charcoal suit that cost more than Aurora’s yearly rent, his posture rigid, his eyes scanning the room with a predatory precision." | | 3 | "He stopped a few feet away, maintaining a distance that felt intentional, almost clinical." | | 4 | "For years, Julian had been the golden boy, the one who had navigated the social hierarchies of their youth with an ease that bordered on the sociopathic." | | 5 | "He didn't speak, but his hazel eyes were narrowed, observing Julian with the detached scrutiny of a man who had spent his life reading people for weaknesses." | | 6 | "For a second, she wasn't twenty-five and exhausted; she was twenty, sitting in a rain-slicked courtyard in Cardiff, believing that they were the only two people…" | | 7 | "It was the silence of years spent pretending the other didn't exist, of regrets that had fermented into something bitter and permanent." | | 8 | "She felt a sudden, sharp lightness in her chest, as if a tether she hadn't known she was still dragging had finally snapped." |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 2 | | matches | | 0 | "she said, her voice regaining its edge" | | 1 | "Aurora said, her voice steady despite the hammering of her heart" |
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| 56.25% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 3 | | fancyTags | | 0 | "she whispered (whisper)" | | 1 | "he lied (lie)" | | 2 | "she admitted (admit)" |
| | dialogueSentences | 30 | | tagDensity | 0.533 | | leniency | 1 | | rawRatio | 0.188 | | effectiveRatio | 0.188 | |