| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn said simply [simply]" |
| | dialogueSentences | 25 | | tagDensity | 0.44 | | leniency | 0.88 | | rawRatio | 0.091 | | effectiveRatio | 0.08 | |
| 83.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2097 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "perfectly" | | 1 | "carefully" | | 2 | "slightly" | | 3 | "very" | | 4 | "slowly" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 52.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2097 | | totalAiIsms | 20 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | word | "down her spine" | | count | 1 |
| | 14 | | | 15 | |
| | highlights | | 0 | "chill" | | 1 | "standard" | | 2 | "familiar" | | 3 | "weight" | | 4 | "etched" | | 5 | "scanned" | | 6 | "blown wide" | | 7 | "whisper" | | 8 | "scanning" | | 9 | "charged" | | 10 | "raced" | | 11 | "magnetic" | | 12 | "trembled" | | 13 | "down her spine" | | 14 | "crystallized" | | 15 | "jaw clenched" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 180 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 180 | | filterMatches | | | hedgeMatches | | 0 | "happened to" | | 1 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 193 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 55 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2097 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 76 | | wordCount | 1636 | | uniqueNames | 17 | | maxNameDensity | 1.28 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 21 | | Tube | 1 | | Camden | 2 | | Patel | 15 | | Morris | 5 | | Market | 4 | | Thames | 1 | | Kowalski | 1 | | British | 2 | | Museum | 2 | | London | 2 | | Aurora | 3 | | Veil | 3 | | Compass | 1 | | Throat | 1 | | Eva | 11 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Patel" | | 4 | "Morris" | | 5 | "Kowalski" | | 6 | "Museum" | | 7 | "Aurora" | | 8 | "Throat" | | 9 | "Eva" |
| | places | | 0 | "Thames" | | 1 | "British" | | 2 | "London" | | 3 | "Veil" |
| | globalScore | 0.858 | | windowScore | 0.833 | |
| 88.02% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 121 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like human skin" | | 1 | "seemed smaller now, the shadows deeper" | | 2 | "appeared ordinary again, but she knew better now" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.954 | | wordCount | 2097 | | matches | | 0 | "Not words exactly, but the impression of them" | | 1 | "Not someone she'd arrested, but someone she'd seen in surveillance photos" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 193 | | matches | | 0 | "learned that lesson" | | 1 | "mention that the" |
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| 71.93% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 47.66 | | std | 19.15 | | cv | 0.402 | | sampleLengths | | 0 | 98 | | 1 | 91 | | 2 | 34 | | 3 | 38 | | 4 | 61 | | 5 | 76 | | 6 | 51 | | 7 | 47 | | 8 | 61 | | 9 | 58 | | 10 | 47 | | 11 | 67 | | 12 | 64 | | 13 | 49 | | 14 | 58 | | 15 | 27 | | 16 | 53 | | 17 | 32 | | 18 | 15 | | 19 | 47 | | 20 | 35 | | 21 | 59 | | 22 | 65 | | 23 | 29 | | 24 | 18 | | 25 | 48 | | 26 | 43 | | 27 | 56 | | 28 | 3 | | 29 | 77 | | 30 | 55 | | 31 | 58 | | 32 | 40 | | 33 | 60 | | 34 | 19 | | 35 | 50 | | 36 | 51 | | 37 | 42 | | 38 | 35 | | 39 | 41 | | 40 | 49 | | 41 | 27 | | 42 | 47 | | 43 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 180 | | matches | | 0 | "been torn" | | 1 | "been found" |
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| 79.23% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 276 | | matches | | 0 | "was staring" | | 1 | "were connecting" | | 2 | "was seeing" | | 3 | "was using" | | 4 | "was only beginning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 193 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1640 | | adjectiveStacks | 2 | | stackExamples | | 0 | "underground supernatural black market" | | 1 | "same distinctive green thread." |
| | adverbCount | 53 | | adverbRatio | 0.03231707317073171 | | lyAdverbCount | 26 | | lyAdverbRatio | 0.015853658536585366 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 193 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 193 | | mean | 10.87 | | std | 8.49 | | cv | 0.782 | | sampleLengths | | 0 | 17 | | 1 | 19 | | 2 | 19 | | 3 | 16 | | 4 | 6 | | 5 | 21 | | 6 | 15 | | 7 | 23 | | 8 | 8 | | 9 | 2 | | 10 | 6 | | 11 | 15 | | 12 | 22 | | 13 | 9 | | 14 | 17 | | 15 | 8 | | 16 | 12 | | 17 | 26 | | 18 | 7 | | 19 | 29 | | 20 | 25 | | 21 | 7 | | 22 | 18 | | 23 | 10 | | 24 | 23 | | 25 | 6 | | 26 | 5 | | 27 | 7 | | 28 | 9 | | 29 | 17 | | 30 | 4 | | 31 | 9 | | 32 | 12 | | 33 | 9 | | 34 | 38 | | 35 | 4 | | 36 | 5 | | 37 | 3 | | 38 | 5 | | 39 | 15 | | 40 | 6 | | 41 | 7 | | 42 | 16 | | 43 | 15 | | 44 | 5 | | 45 | 2 | | 46 | 2 | | 47 | 18 | | 48 | 16 | | 49 | 10 |
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| 45.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.2849740932642487 | | totalSentences | 193 | | uniqueOpeners | 55 | |
| 19.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 169 | | matches | | 0 | "Then it stopped, pointing directly" |
| | ratio | 0.006 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 169 | | matches | | 0 | "She adjusted the worn leather" | | 1 | "His face wore the exhausted" | | 2 | "She scanned the platform, noting" | | 3 | "He gestured toward the body" | | 4 | "His eyes stared at the" | | 5 | "It was the satchel beside" | | 6 | "She crouched beside the body," | | 7 | "She'd learned that lesson three" | | 8 | "She studied the victim's hands." | | 9 | "It matched the etchings on" | | 10 | "She pulled a small flashlight" | | 11 | "They led from the body" | | 12 | "she said, rising" | | 13 | "Her salt-and-pepper hair caught the" | | 14 | "She'd heard the rumors for" | | 15 | "She turned away from Patel," | | 16 | "She stepped closer, ignoring Patel's" | | 17 | "Her skin prickled." | | 18 | "Her breath caught." | | 19 | "She recognized the handwriting on" |
| | ratio | 0.237 | |
| 33.96% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 144 | | totalSentences | 169 | | matches | | 0 | "Detective Harlow Quinn stepped from" | | 1 | "Camden's forgotten underbelly stretched out" | | 2 | "The air carried the metallic" | | 3 | "This wasn't a standard murder" | | 4 | "She adjusted the worn leather" | | 5 | "The call had come in" | | 6 | "Body in the old Camden" | | 7 | "Quinn had spotted it immediately," | | 8 | "Sergeant Patel approached, his boots" | | 9 | "His face wore the exhausted" | | 10 | "Quinn's voice cut through the" | | 11 | "She scanned the platform, noting" | | 12 | "Patel rubbed the back of" | | 13 | "He gestured toward the body" | | 14 | "Quinn moved forward, her sharp" | | 15 | "The victim was a man" | | 16 | "His eyes stared at the" | | 17 | "It was the satchel beside" | | 18 | "The kind of satchel she'd" | | 19 | "She crouched beside the body," |
| | ratio | 0.852 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 169 | | matches | (empty) | | ratio | 0 | |
| 68.23% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 67 | | technicalSentenceCount | 7 | | matches | | 0 | "Camden's forgotten underbelly stretched out before her, its tiled walls slick with condensation that gleamed under the portable floodlights." | | 1 | "The victim was a man in his thirties, well-dressed in a tailored coat that had seen better days." | | 2 | "An underground supernatural black market that shifted locations with every full moon." | | 3 | "Her best friend's childhood companion, the occult researcher who worked at the British Museum's restricted archives." | | 4 | "The same Aurora who had been Quinn's confidential informant on several cases involving the city's hidden underbelly." | | 5 | "Something that had left Eva's hair on a dead man's wrist and her books beside his body." | | 6 | "The mystery was only beginning to unfold, its tendrils reaching deeper into the hidden world beneath London than even she had suspected." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 3 | | matches | | 0 | "Quinn moved along, her fingers hovering just above the mortar without touching" | | 1 | "Quinn said, her voice low" | | 2 | "she told, her tone brooking no argument" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 25 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.2 | | effectiveRatio | 0.08 | |