| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.53% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1119 | | 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) | |
| 37.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1119 | | totalAiIsms | 14 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "down her spine" | | count | 1 |
| | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | |
| | highlights | | 0 | "chill" | | 1 | "gloom" | | 2 | "throb" | | 3 | "down her spine" | | 4 | "scanned" | | 5 | "resolved" | | 6 | "fractured" | | 7 | "scanning" | | 8 | "velvet" | | 9 | "quickened" | | 10 | "otherworldly" | | 11 | "flicker" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "air was thick with" | | count | 1 |
| | 1 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "The air was thick with" | | 1 | "A flicker of recognition" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 110 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 110 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 6 | | totalWords | 1119 | | ratio | 0.005 | | matches | | 0 | "run," | | 1 | "be" | | 2 | "exist" | | 3 | "felt" | | 4 | "whatever" | | 5 | "things" |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 1071 | | uniqueNames | 10 | | maxNameDensity | 1.12 | | worstName | "Herrera" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Herrera" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 8 | | London | 2 | | Herrera | 12 | | Veil | 1 | | Market | 2 | | Friday | 1 | | Camden | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Market" | | 4 | "Morris" |
| | places | | | globalScore | 0.94 | | windowScore | 0.5 | |
| 20.13% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 4 | | matches | | 0 | "looked like a vast, cavernous space" | | 1 | "started with a seemingly straightforward rob" | | 2 | "looked like they’d stepped out of a night" | | 3 | "looked like dried herbs" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.894 | | wordCount | 1119 | | matches | | 0 | "not a perpetrator, but a conduit to something foul" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 38.59 | | std | 20.48 | | cv | 0.531 | | sampleLengths | | 0 | 79 | | 1 | 58 | | 2 | 78 | | 3 | 57 | | 4 | 42 | | 5 | 58 | | 6 | 76 | | 7 | 45 | | 8 | 51 | | 9 | 19 | | 10 | 37 | | 11 | 58 | | 12 | 54 | | 13 | 19 | | 14 | 53 | | 15 | 38 | | 16 | 33 | | 17 | 37 | | 18 | 41 | | 19 | 15 | | 20 | 11 | | 21 | 22 | | 22 | 3 | | 23 | 27 | | 24 | 29 | | 25 | 21 | | 26 | 16 | | 27 | 29 | | 28 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 110 | | matches | | |
| 54.28% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 183 | | matches | | 0 | "was going" | | 1 | "was operating" | | 2 | "was pulling" | | 3 | "was handing" |
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| 68.32% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 115 | | ratio | 0.026 | | matches | | 0 | "The Market moved with the full moon, using abandoned spaces, lost tunnels; tonight it was beneath Camden." | | 1 | "But the thought of Herrera disappearing down there, potentially connecting with whoever – or *whatever* – was pulling the strings, was enough to override her caution." | | 2 | "People – and *things* – milled about, their faces obscured by hoods or masks." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1071 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint, sickly-sweet odor" |
| | adverbCount | 36 | | adverbRatio | 0.03361344537815126 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.015873015873015872 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 9.73 | | std | 6.23 | | cv | 0.64 | | sampleLengths | | 0 | 20 | | 1 | 6 | | 2 | 13 | | 3 | 13 | | 4 | 2 | | 5 | 25 | | 6 | 17 | | 7 | 16 | | 8 | 3 | | 9 | 9 | | 10 | 9 | | 11 | 4 | | 12 | 14 | | 13 | 11 | | 14 | 5 | | 15 | 12 | | 16 | 3 | | 17 | 28 | | 18 | 5 | | 19 | 9 | | 20 | 10 | | 21 | 4 | | 22 | 14 | | 23 | 20 | | 24 | 3 | | 25 | 3 | | 26 | 15 | | 27 | 9 | | 28 | 4 | | 29 | 6 | | 30 | 2 | | 31 | 12 | | 32 | 1 | | 33 | 3 | | 34 | 17 | | 35 | 10 | | 36 | 1 | | 37 | 14 | | 38 | 18 | | 39 | 31 | | 40 | 4 | | 41 | 4 | | 42 | 4 | | 43 | 15 | | 44 | 7 | | 45 | 1 | | 46 | 9 | | 47 | 10 | | 48 | 6 | | 49 | 3 |
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| 51.75% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.39473684210526316 | | totalSentences | 114 | | uniqueOpeners | 45 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 100 | | matches | | 0 | "Officially, it was closed." | | 1 | "Just that she had a" | | 2 | "Then she saw him." | | 3 | "Then, the man’s eyes swept" |
| | ratio | 0.04 | |
| 80.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 100 | | matches | | 0 | "She tasted grit and exhaust" | | 1 | "Her leather jacket, usually enough" | | 2 | "She’d been tailing him for" | | 3 | "She pushed off from the" | | 4 | "He was fast, too fast" | | 5 | "She hadn't expected him to" | | 6 | "It confirmed her suspicions." | | 7 | "He pulled a small, bone" | | 8 | "He disappeared into the darkness." | | 9 | "She stopped at the edge" | | 10 | "It wasn’t a sewer." | | 11 | "It was a staircase, spiraling" | | 12 | "She'd heard whispers." | | 13 | "She’d dismissed them as barroom" | | 14 | "She checked her watch, the" | | 15 | "She’d *felt* something that night," | | 16 | "She glanced back at the" | | 17 | "She hadn’t told them where" | | 18 | "Her career, for a start." | | 19 | "She was operating outside of" |
| | ratio | 0.35 | |
| 35.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 100 | | matches | | 0 | "Rain lashed against Detective Harlow" | | 1 | "She tasted grit and exhaust" | | 2 | "Her leather jacket, usually enough" | | 3 | "She’d been tailing him for" | | 4 | "She pushed off from the" | | 5 | "Herrera moved with a deceptive" | | 6 | "Quinn wasn't fooled." | | 7 | "He was fast, too fast" | | 8 | "She hadn't expected him to" | | 9 | "It confirmed her suspicions." | | 10 | "The archway opened onto a" | | 11 | "The air grew thick, smelling" | | 12 | "something metallic and faintly sweet." | | 13 | "The alley ended abruptly at" | | 14 | "Herrera didn’t hesitate." | | 15 | "He pulled a small, bone" | | 16 | "He disappeared into the darkness." | | 17 | "Quinn slowed, her breath misting" | | 18 | "She stopped at the edge" | | 19 | "It wasn’t a sewer." |
| | ratio | 0.85 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 100 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 2 | | matches | | 0 | "A case that had started with a seemingly straightforward robbery gone wrong had devolved into a tangle of strange symbols, archaic rituals, and a primal fear sh…" | | 1 | "Figures with shimmering scales, others with unnaturally pale skin, creatures that looked like they’d stepped out of a nightmare." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 2 | | matches | | 0 | "Herrera said, his voice low and tense" | | 1 | "he said, his voice a silken threat" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 8 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |