| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 77.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 883 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "suddenly" | | 1 | "quickly" | | 2 | "slowly" |
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| 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) | |
| 20.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 883 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "chill" | | 1 | "pounding" | | 2 | "raced" | | 3 | "gloom" | | 4 | "footsteps" | | 5 | "weight" | | 6 | "familiar" | | 7 | "comforting" | | 8 | "scanned" | | 9 | "could feel" | | 10 | "racing" |
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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 | 0 | | narrationSentences | 72 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 72 | | filterMatches | (empty) | | hedgeMatches | | 0 | "seemed to" | | 1 | "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 | 73 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 887 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 74.24% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 858 | | uniqueNames | 9 | | maxNameDensity | 1.52 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Harlow | 1 | | Quinn | 13 | | Raven | 1 | | Nest | 1 | | Morris | 3 | | Veil | 1 | | Market | 1 | | People | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "People" |
| | places | | 0 | "London" | | 1 | "Raven" | | 2 | "Market" |
| | globalScore | 0.742 | | windowScore | 0.833 | |
| 36.36% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 3 | | matches | | 0 | "felt like hours, ever since she'd spott" | | 1 | "looked like human skin, crystals that hum" | | 2 | "looked like a severed hand" |
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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 | 887 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 73 | | matches | | |
| 45.58% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 38.57 | | std | 11.94 | | cv | 0.31 | | sampleLengths | | 0 | 49 | | 1 | 19 | | 2 | 51 | | 3 | 57 | | 4 | 44 | | 5 | 62 | | 6 | 24 | | 7 | 43 | | 8 | 38 | | 9 | 58 | | 10 | 54 | | 11 | 34 | | 12 | 34 | | 13 | 32 | | 14 | 31 | | 15 | 36 | | 16 | 26 | | 17 | 20 | | 18 | 33 | | 19 | 35 | | 20 | 42 | | 21 | 39 | | 22 | 26 |
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| 95.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 72 | | matches | | 0 | "was gone" | | 1 | "been played" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 150 | | matches | | |
| 25.44% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 73 | | ratio | 0.041 | | matches | | 0 | "In the distance, she could hear the faint sounds of activity - voices, footsteps, the clink of glass on glass." | | 1 | "As she moved deeper into the station, the air grew thick with strange scents - incense, herbs, something metallic that made her nose wrinkle." | | 2 | "Quinn's eyes widened as she took in the scene - jars of glowing liquid, books bound in what looked like human skin, crystals that hummed with an eerie energy." |
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| 89.67% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 125 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.048 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.024 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 73 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 73 | | mean | 12.15 | | std | 5.26 | | cv | 0.433 | | sampleLengths | | 0 | 15 | | 1 | 20 | | 2 | 14 | | 3 | 19 | | 4 | 13 | | 5 | 10 | | 6 | 28 | | 7 | 10 | | 8 | 17 | | 9 | 19 | | 10 | 11 | | 11 | 12 | | 12 | 10 | | 13 | 22 | | 14 | 11 | | 15 | 8 | | 16 | 11 | | 17 | 20 | | 18 | 12 | | 19 | 15 | | 20 | 9 | | 21 | 10 | | 22 | 13 | | 23 | 20 | | 24 | 24 | | 25 | 14 | | 26 | 18 | | 27 | 11 | | 28 | 29 | | 29 | 8 | | 30 | 15 | | 31 | 17 | | 32 | 14 | | 33 | 1 | | 34 | 14 | | 35 | 11 | | 36 | 8 | | 37 | 14 | | 38 | 11 | | 39 | 9 | | 40 | 9 | | 41 | 10 | | 42 | 13 | | 43 | 5 | | 44 | 13 | | 45 | 13 | | 46 | 6 | | 47 | 17 | | 48 | 13 | | 49 | 13 |
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| 57.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3835616438356164 | | totalSentences | 73 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 70 | | matches | | 0 | "Her breath came in ragged" | | 1 | "she shouted, her voice barely" | | 2 | "She'd been chasing this suspect" | | 3 | "She'd been investigating the clique" | | 4 | "She knew about the Veil" | | 5 | "It was exactly the kind" | | 6 | "She thought of Morris, of" | | 7 | "She drew her weapon, the" | | 8 | "She scanned the crowd, searching" | | 9 | "she said, her voice cutting" | | 10 | "She reached the stall, but" | | 11 | "She couldn't afford to lose" | | 12 | "He was breathing heavily, his" | | 13 | "It wasn't her suspect at" | | 14 | "It was a mirror, reflecting" | | 15 | "They'd led her here, into" | | 16 | "She'd been so focused on" | | 17 | "She struggled, kicking and thrashing," | | 18 | "She'd been played, led like" | | 19 | "She still had her badge," |
| | ratio | 0.3 | |
| 52.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 70 | | matches | | 0 | "The rain hammered down on" | | 1 | "Detective Harlow Quinn's boots splashed" | | 2 | "Her breath came in ragged" | | 3 | "she shouted, her voice barely" | | 4 | "The figure ahead darted left," | | 5 | "Quinn adjusted her trajectory, her" | | 6 | "She'd been chasing this suspect" | | 7 | "She'd been investigating the clique" | | 8 | "This chase might be her" | | 9 | "The suspect suddenly veered right," | | 10 | "Quinn followed, her hand instinctively" | | 11 | "The steps led to a" | | 12 | "Quinn paused at the top" | | 13 | "She knew about the Veil" | | 14 | "Every cop in London did," | | 15 | "A hidden supernatural black market" | | 16 | "It was exactly the kind" | | 17 | "She thought of Morris, of" | | 18 | "Quinn took a deep breath" | | 19 | "The stairs opened into a" |
| | ratio | 0.814 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 2 | | matches | | 0 | "A hidden supernatural black market that moved locations every full moon, selling everything from enchanted trinkets to banned alchemical substances." | | 1 | "He was breathing heavily, his shoulders rising and falling with each labored breath." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 2 | | matches | | 0 | "she said, her voice cutting through the market's ambient noise" | | 1 | "Quinn said, her voice steady despite her racing heart" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 5 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0.333 | | effectiveRatio | 0.333 | |