| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 3 | | adverbTags | | 0 | "She looked around [around]" | | 1 | "Then she glanced [Then]" | | 2 | "The man glanced nervously [nervously]" |
| | dialogueSentences | 62 | | tagDensity | 0.339 | | leniency | 0.677 | | rawRatio | 0.143 | | effectiveRatio | 0.097 | |
| 79.13% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1198 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "softly" | | 1 | "slightly" | | 2 | "nervously" | | 3 | "very" |
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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) | |
| 37.40% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1198 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "familiar" | | 1 | "weight" | | 2 | "intensity" | | 3 | "reminder" | | 4 | "flicker" | | 5 | "enigmatic" | | 6 | "unreadable" | | 7 | "eyebrow" | | 8 | "silence" | | 9 | "determined" | | 10 | "stomach" | | 11 | "fleeting" | | 12 | "chill" | | 13 | "lurch" |
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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 | 89 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 89 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1202 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 843 | | uniqueNames | 9 | | maxNameDensity | 0.83 | | worstName | "Silas" | | maxWindowNameDensity | 2 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Thursday | 1 | | Laila | 1 | | Fifteen | 1 | | Silas | 7 | | Brendan | 1 | | Jennifer | 1 | | London | 1 |
| | persons | | 0 | "Raven" | | 1 | "Laila" | | 2 | "Silas" | | 3 | "Brendan" | | 4 | "Jennifer" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed shadowed carrying a quiet intensity he hadn’t recognised before" |
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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 | 1202 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 129 | | matches | | 0 | "knew that silhouette" | | 1 | "seen that look" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 21.85 | | std | 17.52 | | cv | 0.801 | | sampleLengths | | 0 | 75 | | 1 | 1 | | 2 | 55 | | 3 | 11 | | 4 | 8 | | 5 | 19 | | 6 | 63 | | 7 | 17 | | 8 | 18 | | 9 | 38 | | 10 | 45 | | 11 | 34 | | 12 | 16 | | 13 | 10 | | 14 | 52 | | 15 | 15 | | 16 | 2 | | 17 | 16 | | 18 | 2 | | 19 | 13 | | 20 | 8 | | 21 | 30 | | 22 | 14 | | 23 | 35 | | 24 | 6 | | 25 | 33 | | 26 | 24 | | 27 | 7 | | 28 | 3 | | 29 | 20 | | 30 | 63 | | 31 | 18 | | 32 | 8 | | 33 | 2 | | 34 | 14 | | 35 | 31 | | 36 | 10 | | 37 | 3 | | 38 | 2 | | 39 | 15 | | 40 | 4 | | 41 | 20 | | 42 | 40 | | 43 | 13 | | 44 | 28 | | 45 | 28 | | 46 | 46 | | 47 | 39 | | 48 | 12 | | 49 | 10 |
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| 97.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 89 | | matches | | 0 | "been determined" | | 1 | "was gone" |
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| 64.86% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 148 | | matches | | 0 | "was beginning" | | 1 | "was using" | | 2 | "was referring" |
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| 32.12% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 129 | | ratio | 0.039 | | matches | | 0 | "The crescent-shaped scar on her left wrist – a childhood accident, a reminder of a reckless summer – was still there, peeking out from beneath the cuff of her coat." | | 1 | "A flicker of something – amusement?" | | 2 | "– crossed her face." | | 3 | "“Did they? Or did you just enjoy playing the hero?” She’s been watching him closely - and knew exactly how to push." | | 4 | "Silas stared at the empty doorway, the scent of rain and something else – something expensive, something foreign – lingering in the air." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 841 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 33 | | adverbRatio | 0.039239001189060645 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.0178359096313912 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 9.32 | | std | 6.43 | | cv | 0.69 | | sampleLengths | | 0 | 13 | | 1 | 10 | | 2 | 26 | | 3 | 7 | | 4 | 7 | | 5 | 12 | | 6 | 1 | | 7 | 18 | | 8 | 7 | | 9 | 16 | | 10 | 9 | | 11 | 5 | | 12 | 11 | | 13 | 6 | | 14 | 2 | | 15 | 8 | | 16 | 11 | | 17 | 12 | | 18 | 2 | | 19 | 1 | | 20 | 12 | | 21 | 2 | | 22 | 2 | | 23 | 2 | | 24 | 16 | | 25 | 14 | | 26 | 11 | | 27 | 6 | | 28 | 14 | | 29 | 4 | | 30 | 7 | | 31 | 3 | | 32 | 6 | | 33 | 17 | | 34 | 5 | | 35 | 10 | | 36 | 21 | | 37 | 14 | | 38 | 29 | | 39 | 5 | | 40 | 10 | | 41 | 6 | | 42 | 4 | | 43 | 6 | | 44 | 11 | | 45 | 30 | | 46 | 11 | | 47 | 8 | | 48 | 7 | | 49 | 2 |
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| 67.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4418604651162791 | | totalSentences | 129 | | uniqueOpeners | 57 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 80 | | matches | | 0 | "Just a scattering of regulars" | | 1 | "Then she glanced at the" | | 2 | "Then, she was gone, swallowed" | | 3 | "Then, he delivered the news" |
| | ratio | 0.05 | |
| 5.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 80 | | matches | | 0 | "He hadn’t expected much, hadn’t" | | 1 | "He was beginning to think" | | 2 | "He turned, squinting against the" | | 3 | "He knew that silhouette, that" | | 4 | "He said, the name tasting" | | 5 | "She offered a small, uncertain" | | 6 | "He gestured to a stool" | | 7 | "She slid onto the stool," | | 8 | "Her bright blue eyes seemed" | | 9 | "she said, her voice a" | | 10 | "He poured her a gin" | | 11 | "she replied, taking a sip" | | 12 | "She didn’t elaborate." | | 13 | "he stated, more a confirmation" | | 14 | "She looked around the bar," | | 15 | "He grunted, gesturing to his" | | 16 | "She didn’t smile back." | | 17 | "He leaned against the bar," | | 18 | "She hesitated, swirling the ice" | | 19 | "She took another sip of" |
| | ratio | 0.538 | |
| 35.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 80 | | matches | | 0 | "Silas muttered, wiping down the" | | 1 | "The Raven’s Nest was unusually" | | 2 | "He hadn’t expected much, hadn’t" | | 3 | "The ghosts had been particularly" | | 4 | "He was beginning to think" | | 5 | "The voice was a thread," | | 6 | "He turned, squinting against the" | | 7 | "A woman stood framed in" | | 8 | "He knew that silhouette, that" | | 9 | "He said, the name tasting" | | 10 | "She offered a small, uncertain" | | 11 | "He gestured to a stool" | | 12 | "She slid onto the stool," | | 13 | "The girl he remembered had" | | 14 | "A tailored grey coat hung" | | 15 | "Her bright blue eyes seemed" | | 16 | "she said, her voice a" | | 17 | "He poured her a gin" | | 18 | "she replied, taking a sip" | | 19 | "She didn’t elaborate." |
| | ratio | 0.85 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 93.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 2 | | matches | | 0 | "It was the look of someone who'd traded their soul for power." | | 1 | "He took a long swallow of his whiskey, the burning liquid doing little to warm the chill that had settled in his bones." |
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| 29.76% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 4 | | matches | | 0 | "she said, her voice a little too even" | | 1 | "She met, her eyes unreadable" | | 2 | "she replied, her tone devoid of emotion" | | 3 | "he said, his voice low and clipped" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "Silas muttered (mutter)" | | 1 | "he stated (state)" |
| | dialogueSentences | 62 | | tagDensity | 0.129 | | leniency | 0.258 | | rawRatio | 0.25 | | effectiveRatio | 0.065 | |