| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said finally [finally]" | | 1 | "Quinn said quietly [quietly]" |
| | dialogueSentences | 17 | | tagDensity | 0.588 | | leniency | 1 | | rawRatio | 0.2 | | effectiveRatio | 0.2 | |
| 94.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 870 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 36.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 870 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "glinting" | | 1 | "silk" | | 2 | "cataloged" | | 3 | "pulse" | | 4 | "quickened" | | 5 | "perfect" | | 6 | "pristine" | | 7 | "footsteps" | | 8 | "echoing" | | 9 | "echo" |
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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 | 1 | | narrationSentences | 52 | | matches | | |
| 5.49% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 52 | | filterMatches | | | hedgeMatches | | 0 | "tried to" | | 1 | "seemed to" | | 2 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 59 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 863 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 1 | | matches | | 0 | "Behind her, Martinez called out something about finding traces of an unknown substance, but Quinn barely heard him." |
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| 59.91% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 666 | | uniqueNames | 12 | | maxNameDensity | 1.8 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Tube | 2 | | London | 1 | | Sergeant | 1 | | Martinez | 6 | | Oxford | 1 | | Morris | 3 | | Kowalski | 3 | | Veil | 2 | | Market | 3 | | Eva | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Martinez" | | 4 | "Morris" | | 5 | "Kowalski" | | 6 | "Market" | | 7 | "Eva" |
| | places | | 0 | "London" | | 1 | "Oxford" | | 2 | "Veil" |
| | globalScore | 0.599 | | windowScore | 0.833 | |
| 28.05% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like he'd decided to take a nap in" | | 1 | "quite place, like ozone and old books" |
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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 | 863 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 80.73% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 41.1 | | std | 17.78 | | cv | 0.433 | | sampleLengths | | 0 | 51 | | 1 | 54 | | 2 | 44 | | 3 | 39 | | 4 | 66 | | 5 | 17 | | 6 | 32 | | 7 | 54 | | 8 | 23 | | 9 | 58 | | 10 | 12 | | 11 | 34 | | 12 | 8 | | 13 | 28 | | 14 | 54 | | 15 | 45 | | 16 | 13 | | 17 | 63 | | 18 | 53 | | 19 | 58 | | 20 | 57 |
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| 91.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 52 | | matches | | 0 | "was drawn" | | 1 | "was disturbed" | | 2 | "been dropped" |
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| 83.04% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 114 | | matches | | 0 | "were using" | | 1 | "was already moving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 59 | | ratio | 0.085 | | matches | | 0 | "And there was something else—a faint smell she couldn't quite place, like ozone and old books." | | 1 | "She glanced at her watch—just past midnight." | | 2 | "Eva Kowalski might be young and nervous—always tucking that red hair behind her left ear when she was anxious—but she knew something about this death." | | 3 | "She flexed her hand, feeling the old burn scar across her palm—another souvenir from the night Morris vanished." | | 4 | "Sometimes solving a mystery wasn't about what you could see—it was about what you couldn't explain away." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 675 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.037037037037037035 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.01925925925925926 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 59 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 59 | | mean | 14.63 | | std | 7.08 | | cv | 0.484 | | sampleLengths | | 0 | 16 | | 1 | 15 | | 2 | 20 | | 3 | 14 | | 4 | 6 | | 5 | 23 | | 6 | 11 | | 7 | 18 | | 8 | 26 | | 9 | 4 | | 10 | 22 | | 11 | 13 | | 12 | 11 | | 13 | 28 | | 14 | 27 | | 15 | 3 | | 16 | 14 | | 17 | 12 | | 18 | 20 | | 19 | 18 | | 20 | 7 | | 21 | 9 | | 22 | 20 | | 23 | 5 | | 24 | 18 | | 25 | 3 | | 26 | 18 | | 27 | 21 | | 28 | 16 | | 29 | 12 | | 30 | 7 | | 31 | 27 | | 32 | 8 | | 33 | 18 | | 34 | 6 | | 35 | 4 | | 36 | 11 | | 37 | 7 | | 38 | 7 | | 39 | 13 | | 40 | 16 | | 41 | 11 | | 42 | 15 | | 43 | 19 | | 44 | 8 | | 45 | 5 | | 46 | 14 | | 47 | 25 | | 48 | 24 | | 49 | 24 |
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| 89.83% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.559322033898305 | | totalSentences | 59 | | uniqueOpeners | 33 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 51 | | matches | | 0 | "Just a middle-aged man in" | | 1 | "Then he'd disappeared during a" | | 2 | "Sometimes solving a mystery wasn't" |
| | ratio | 0.059 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 51 | | matches | | 0 | "She studied the victim's manicured" | | 1 | "Her worn leather watch ticked" | | 2 | "she said finally" | | 3 | "She swept her torch beam" | | 4 | "she paused, frowning" | | 5 | "She kept her face neutral," | | 6 | "She'd seen that particular shade" | | 7 | "She walked a careful circle" | | 8 | "She'd smelled it before, three" | | 9 | "She glanced at her watch—just" | | 10 | "She remembered the last time" | | 11 | "she told Martinez" | | 12 | "She walked purposefully toward the" | | 13 | "She flexed her hand, feeling" | | 14 | "She was already moving toward" |
| | ratio | 0.294 | |
| 97.25% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 51 | | matches | | 0 | "Detective Harlow Quinn crouched beside" | | 1 | "The abandoned Tube platform hadn't" | | 2 | "The victim lay spread-eagled, facing" | | 3 | "Quinn's torch beam caught something" | | 4 | "Detective Sergeant Martinez said from" | | 5 | "Quinn didn't respond immediately." | | 6 | "She studied the victim's manicured" | | 7 | "Her worn leather watch ticked" | | 8 | "she said finally" | | 9 | "She swept her torch beam" | | 10 | "she paused, frowning" | | 11 | "Martinez shifted uncomfortably." | | 12 | "Quinn stood, her movements precise" | | 13 | "A flash of red hair," | | 14 | "She kept her face neutral," | | 15 | "She'd seen that particular shade" | | 16 | "Martinez confirmed, straightening up" | | 17 | "Quinn's jaw tightened." | | 18 | "She walked a careful circle" | | 19 | "The dust was disturbed in" |
| | ratio | 0.725 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 51 | | matches | | 0 | "If her suspicions were correct," | | 1 | "Either at the Veil Market," |
| | ratio | 0.039 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "Blue police lights filtered down through the street grates far above, casting strange shadows that danced across the curved walls." | | 1 | "Just a middle-aged man in an expensive suit who looked like he'd decided to take a nap in one of London's forgotten places." | | 2 | "The brass compass in the evidence bag seemed to gleam of its own accord, its needle spinning wildly, pointing to something that shouldn't exist." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn stood, her movements precise despite hours into their shift" | | 1 | "She walked, her torch beam revealing more oddities" |
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| 32.35% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "Martinez confirmed (confirm)" | | 1 | "she announced (announce)" |
| | dialogueSentences | 17 | | tagDensity | 0.353 | | leniency | 0.706 | | rawRatio | 0.333 | | effectiveRatio | 0.235 | |