| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 82.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1118 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "sharply" | | 2 | "very" |
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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) | |
| 15.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1118 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "flickered" | | 1 | "warmth" | | 2 | "stark" | | 3 | "scanned" | | 4 | "flicked" | | 5 | "dancing" | | 6 | "footsteps" | | 7 | "echoed" | | 8 | "glint" | | 9 | "otherworldly" | | 10 | "pulse" | | 11 | "quickened" | | 12 | "stomach" | | 13 | "weight" | | 14 | "shimmered" | | 15 | "reminder" |
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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 | 0 | | narrationSentences | 87 | | matches | (empty) | |
| 77.18% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1108 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 960 | | uniqueNames | 13 | | maxNameDensity | 1.04 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tomás" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 10 | | Raven | 2 | | Nest | 3 | | London | 1 | | Veil | 2 | | Market | 2 | | Tube | 1 | | Tomás | 6 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | | globalScore | 0.979 | | windowScore | 0.833 | |
| 70.63% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like human skin" | | 1 | "as if sensing her hesitation" |
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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 | 1108 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 98 | | matches | (empty) | |
| 98.24% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 36.93 | | std | 18.24 | | cv | 0.494 | | sampleLengths | | 0 | 70 | | 1 | 62 | | 2 | 47 | | 3 | 31 | | 4 | 22 | | 5 | 36 | | 6 | 48 | | 7 | 45 | | 8 | 64 | | 9 | 48 | | 10 | 52 | | 11 | 48 | | 12 | 63 | | 13 | 66 | | 14 | 10 | | 15 | 16 | | 16 | 26 | | 17 | 51 | | 18 | 17 | | 19 | 25 | | 20 | 32 | | 21 | 42 | | 22 | 7 | | 23 | 25 | | 24 | 46 | | 25 | 21 | | 26 | 21 | | 27 | 11 | | 28 | 43 | | 29 | 13 |
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| 97.20% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 87 | | matches | | 0 | "was obscured" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 157 | | matches | | 0 | "was warning" | | 1 | "was already melting" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 98 | | ratio | 0.071 | | matches | | 0 | "The suspect—a lanky man in a dark hoodie—moved with the desperation of someone who knew the stakes." | | 1 | "The hum of voices, the clink of coins, the scent of spices and something metallic—blood?—filled the air." | | 2 | "A black market for the supernatural, moving with the full moon, accessible only to those who knew the way—and had the right token." | | 3 | "The crowd was a sea of faces—some human, some not." | | 4 | "A man with olive skin and short, curly dark brown hair—Tomás Herrera—locked eyes with her." | | 5 | "Then he stepped through—and was gone." | | 6 | "Her watch ticked against her wrist, a steady reminder of the world she knew—the world of facts, of evidence, of justice." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 974 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.03182751540041068 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.011293634496919919 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 11.31 | | std | 6.98 | | cv | 0.617 | | sampleLengths | | 0 | 14 | | 1 | 18 | | 2 | 21 | | 3 | 17 | | 4 | 8 | | 5 | 18 | | 6 | 17 | | 7 | 3 | | 8 | 16 | | 9 | 23 | | 10 | 18 | | 11 | 6 | | 12 | 31 | | 13 | 15 | | 14 | 7 | | 15 | 4 | | 16 | 15 | | 17 | 3 | | 18 | 14 | | 19 | 15 | | 20 | 12 | | 21 | 21 | | 22 | 15 | | 23 | 8 | | 24 | 10 | | 25 | 12 | | 26 | 10 | | 27 | 19 | | 28 | 17 | | 29 | 18 | | 30 | 3 | | 31 | 8 | | 32 | 23 | | 33 | 14 | | 34 | 6 | | 35 | 10 | | 36 | 18 | | 37 | 12 | | 38 | 6 | | 39 | 12 | | 40 | 28 | | 41 | 8 | | 42 | 4 | | 43 | 30 | | 44 | 3 | | 45 | 15 | | 46 | 11 | | 47 | 11 | | 48 | 3 | | 49 | 15 |
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| 44.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.32653061224489793 | | totalSentences | 98 | | uniqueOpeners | 32 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 83 | | matches | | 0 | "Then, abruptly, the passage opened" | | 1 | "Then she saw him." | | 2 | "Then he stepped through—and was" |
| | ratio | 0.036 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 83 | | matches | | 0 | "She kept her eyes locked" | | 1 | "She rounded a corner, her" | | 2 | "She pushed through, the warmth" | | 3 | "She didn’t buy it." | | 4 | "Her gaze flicked to the" | | 5 | "She’d heard of the Veil" | | 6 | "She didn’t have a bone" | | 7 | "She moved deeper into the" | | 8 | "Her stomach twisted, but she" | | 9 | "She could close the distance," | | 10 | "She took a step forward," | | 11 | "His Saint Christopher medallion glinted" | | 12 | "It was warning." | | 13 | "he said, his voice low" | | 14 | "She glanced back toward the" | | 15 | "Her jaw tightened." | | 16 | "She studied the arch again." | | 17 | "Her fingers curled into fists." | | 18 | "His eyes were steady, unblinking" | | 19 | "It was real." |
| | ratio | 0.265 | |
| 26.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 83 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn’s boots splashed" | | 2 | "She kept her eyes locked" | | 3 | "The suspect—a lanky man in" | | 4 | "She rounded a corner, her" | | 5 | "The green neon of The" | | 6 | "The suspect had vanished, but" | | 7 | "Quinn didn’t hesitate." | | 8 | "She pushed through, the warmth" | | 9 | "The Nest was nearly empty" | | 10 | "The walls, lined with old" | | 11 | "Quinn’s voice was sharp, her" | | 12 | "The bartender, a broad-shouldered man" | | 13 | "She didn’t buy it." | | 14 | "Her gaze flicked to the" | | 15 | "A hidden door." | | 16 | "The Raven’s Nest had secrets," | | 17 | "The bookshelf creaked as she" | | 18 | "The air beyond was damp," | | 19 | "The passage sloped downward, the" |
| | ratio | 0.867 | |
| 60.24% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 83 | | matches | | | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 2 | | matches | | 0 | "The air beyond was damp, thick with the scent of earth and something older, something that didn’t belong in London’s underbelly." | | 1 | "The stalls were a menagerie of the bizarre: jars of preserved things that shouldn’t exist, weapons that hummed with energy, books bound in what looked like huma…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, his voice low" | | 1 | "Tomás stepped, his voice dropping" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.067 | | leniency | 0.133 | | rawRatio | 0 | | effectiveRatio | 0 | |