| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said softly [softly]" |
| | dialogueSentences | 31 | | tagDensity | 0.581 | | leniency | 1 | | rawRatio | 0.056 | | effectiveRatio | 0.056 | |
| 95.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1161 | | totalAiIsmAdverbs | 1 | | 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) | |
| 0.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1161 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | word | "down her spine" | | count | 1 |
| | 18 | |
| | highlights | | 0 | "scanned" | | 1 | "aftermath" | | 2 | "perfect" | | 3 | "echoing" | | 4 | "silence" | | 5 | "glinting" | | 6 | "navigated" | | 7 | "etched" | | 8 | "navigating" | | 9 | "furrowed" | | 10 | "surreal" | | 11 | "apprehension" | | 12 | "pristine" | | 13 | "familiar" | | 14 | "uncanny" | | 15 | "pulsed" | | 16 | "chill" | | 17 | "down her spine" | | 18 | "echo" |
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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 | 2 | | narrationSentences | 83 | | matches | | 0 | "d in confusion" | | 1 | "felt a chill" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 1160 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 844 | | uniqueNames | 17 | | maxNameDensity | 1.54 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 13 | | Tube | 1 | | Northern | 1 | | Line | 1 | | Blitz | 1 | | Davies | 4 | | Italian | 1 | | Miller | 1 | | Veil | 3 | | Compass | 2 | | Met | 1 | | London | 1 | | Eva | 7 | | Kowalski | 1 | | Market | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Line" | | 3 | "Blitz" | | 4 | "Davies" | | 5 | "Miller" | | 6 | "Eva" | | 7 | "Kowalski" | | 8 | "Market" | | 9 | "Morris" |
| | places | | 0 | "Compass" | | 1 | "Met" | | 2 | "London" |
| | globalScore | 0.73 | | windowScore | 0.667 | |
| 68.03% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 2 | | matches | | 0 | "as if reading her thoughts" | | 1 | "as if searching for an invisible clue" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.862 | | wordCount | 1160 | | matches | | 0 | "Not just any abandoned station, but a section of the Northern Line shuttered in the aftermath of" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 35.15 | | std | 19.18 | | cv | 0.546 | | sampleLengths | | 0 | 83 | | 1 | 37 | | 2 | 15 | | 3 | 20 | | 4 | 46 | | 5 | 17 | | 6 | 51 | | 7 | 41 | | 8 | 45 | | 9 | 55 | | 10 | 25 | | 11 | 8 | | 12 | 27 | | 13 | 39 | | 14 | 8 | | 15 | 31 | | 16 | 22 | | 17 | 18 | | 18 | 29 | | 19 | 65 | | 20 | 17 | | 21 | 41 | | 22 | 38 | | 23 | 23 | | 24 | 51 | | 25 | 78 | | 26 | 14 | | 27 | 36 | | 28 | 25 | | 29 | 40 | | 30 | 22 | | 31 | 72 | | 32 | 21 |
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| 92.58% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 83 | | matches | | 0 | "been repurposed" | | 1 | "been shot" | | 2 | "was staged" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 137 | | matches | (empty) | |
| 83.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 96 | | ratio | 0.021 | | matches | | 0 | "The suit was expensive; Italian wool, hand-stitched." | | 1 | "The scent of dust and metal deepened, mingling with a new odor - ozone, like the aftermath of a lightning strike." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 844 | | adjectiveStacks | 1 | | stackExamples | | 0 | "single, perfect white rose" |
| | adverbCount | 30 | | adverbRatio | 0.035545023696682464 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.015402843601895734 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 12.08 | | std | 8.93 | | cv | 0.739 | | sampleLengths | | 0 | 16 | | 1 | 23 | | 2 | 4 | | 3 | 29 | | 4 | 7 | | 5 | 1 | | 6 | 3 | | 7 | 18 | | 8 | 3 | | 9 | 7 | | 10 | 7 | | 11 | 2 | | 12 | 9 | | 13 | 6 | | 14 | 14 | | 15 | 6 | | 16 | 13 | | 17 | 7 | | 18 | 7 | | 19 | 10 | | 20 | 9 | | 21 | 17 | | 22 | 7 | | 23 | 1 | | 24 | 2 | | 25 | 25 | | 26 | 13 | | 27 | 3 | | 28 | 30 | | 29 | 11 | | 30 | 16 | | 31 | 17 | | 32 | 10 | | 33 | 2 | | 34 | 4 | | 35 | 10 | | 36 | 14 | | 37 | 3 | | 38 | 15 | | 39 | 9 | | 40 | 14 | | 41 | 11 | | 42 | 6 | | 43 | 2 | | 44 | 10 | | 45 | 17 | | 46 | 17 | | 47 | 16 | | 48 | 2 | | 49 | 2 |
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| 59.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4166666666666667 | | totalSentences | 96 | | uniqueOpeners | 40 | |
| 92.59% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 72 | | matches | | 0 | "Just a stillness that felt" | | 1 | "Especially not in places like" |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 72 | | matches | | 0 | "He hadn’t been shot, stabbed," | | 1 | "She knelt beside the body," | | 2 | "His eyes were open, staring" | | 3 | "she murmured, her voice echoing" | | 4 | "Her gaze drifted to the" | | 5 | "She moved closer, her worn" | | 6 | "It was a small, brass" | | 7 | "It was intricately crafted, its" | | 8 | "It hummed faintly in her" | | 9 | "She recognized it from whispered" | | 10 | "He was a young sergeant," | | 11 | "She turned her attention to" | | 12 | "She felt that familiar gnawing" | | 13 | "she muttered, the memory of" | | 14 | "He’d always had an uncanny" | | 15 | "She issued the orders, her" | | 16 | "Her eyes scanned the surrounding" | | 17 | "She pointed towards the tunnel" | | 18 | "She felt a chill crawl" |
| | ratio | 0.264 | |
| 29.44% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 72 | | matches | | 0 | "The air tasted of dust" | | 1 | "Detective Harlow Quinn scanned the" | | 2 | "An abandoned Tube station." | | 3 | "This was different." | | 4 | "The victim lay sprawled on" | | 5 | "He hadn’t been shot, stabbed," | | 6 | "Sergeant Davies had called it" | | 7 | "Quinn didn’t believe in tragic" | | 8 | "She knelt beside the body," | | 9 | "The suit was expensive; Italian" | | 10 | "The man’s face was pale," | | 11 | "His eyes were open, staring" | | 12 | "A single, perfect white rose" | | 13 | "she murmured, her voice echoing" | | 14 | "Her gaze drifted to the" | | 15 | "A man tumbling from the" | | 16 | "This…this was staged." | | 17 | "Officer Miller offered, standing a" | | 18 | "Quinn ignored him, her attention" | | 19 | "She moved closer, her worn" |
| | ratio | 0.861 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 2 | | matches | | 0 | "She felt that familiar gnawing sensation, the one that always accompanied a case that pricked at the edges of her understanding." | | 1 | "The case that cost her DS Morris his life three years ago." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 7 | | matches | | 0 | "she murmured, her voice echoing in the silence" | | 1 | "Quinn said, her voice clipped" | | 2 | "Eva continued, her voice low" | | 3 | "Eva said softly, as if reading her thoughts" | | 4 | "She issued, her voice ringing with the authority of years of experience" | | 5 | "Her eyes scanned, as if searching for an invisible clue" | | 6 | "She felt, a premonition of something dark and unsettling" |
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| 20.97% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 4 | | fancyTags | | 0 | "she murmured (murmur)" | | 1 | "Quinn confirmed (confirm)" | | 2 | "Eva continued (continue)" | | 3 | "she muttered (mutter)" |
| | dialogueSentences | 31 | | tagDensity | 0.323 | | leniency | 0.645 | | rawRatio | 0.4 | | effectiveRatio | 0.258 | |