| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.60% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1137 | | 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) | |
| 69.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1137 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "comfortable" | | 1 | "flicker" | | 2 | "shimmered" | | 3 | "weight" | | 4 | "familiar" | | 5 | "standard" | | 6 | "echoed" |
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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 | 102 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 102 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 103 | | 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 | 1135 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1114 | | uniqueNames | 19 | | maxNameDensity | 0.99 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 3 | | Camden | 1 | | High | 1 | | Street | 1 | | Harlow | 2 | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Morris | 4 | | Tube | 1 | | Underground | 1 | | Tomás | 7 | | Metropolitan | 1 | | Police | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Morris" | | 6 | "Tube" | | 7 | "Tomás" | | 8 | "Police" | | 9 | "Market" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "High" | | 3 | "Street" | | 4 | "Raven" |
| | globalScore | 1 | | windowScore | 1 | |
| 82.43% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like it had been cauterized by som" | | 1 | "smelled like the thing that had taken her" |
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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 | 1135 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 103 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 40.54 | | std | 22.16 | | cv | 0.547 | | sampleLengths | | 0 | 54 | | 1 | 13 | | 2 | 85 | | 3 | 54 | | 4 | 70 | | 5 | 48 | | 6 | 33 | | 7 | 4 | | 8 | 72 | | 9 | 22 | | 10 | 44 | | 11 | 32 | | 12 | 82 | | 13 | 10 | | 14 | 58 | | 15 | 45 | | 16 | 13 | | 17 | 36 | | 18 | 18 | | 19 | 55 | | 20 | 12 | | 21 | 66 | | 22 | 47 | | 23 | 16 | | 24 | 26 | | 25 | 35 | | 26 | 49 | | 27 | 36 |
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| 84.62% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 102 | | matches | | 0 | "being followed" | | 1 | "was faded" | | 2 | "was cropped" | | 3 | "being returned" | | 4 | "been cauterized" | | 5 | "was focused" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 185 | | matches | | 0 | "was gliding" | | 1 | "wasn't running" |
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| 31.90% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 3 | | flaggedSentences | 4 | | totalSentences | 103 | | ratio | 0.039 | | matches | | 0 | "The rain did not fall in London; it attacked." | | 1 | "He wasn't running; he was gliding, moving with the quiet grace of someone comfortable with shadows." | | 2 | "It wasn't magic in the comic book sense; it was alchemical." | | 3 | "The sensory overload of the street—the sirens, the cars, the rain—fell away, leaving her with the only thing that mattered: the open mouth of the dark." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1121 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.016057091882247992 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003568242640499554 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 103 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 103 | | mean | 11.02 | | std | 7.25 | | cv | 0.658 | | sampleLengths | | 0 | 9 | | 1 | 28 | | 2 | 17 | | 3 | 13 | | 4 | 8 | | 5 | 13 | | 6 | 2 | | 7 | 2 | | 8 | 6 | | 9 | 18 | | 10 | 27 | | 11 | 6 | | 12 | 3 | | 13 | 20 | | 14 | 4 | | 15 | 4 | | 16 | 6 | | 17 | 20 | | 18 | 16 | | 19 | 16 | | 20 | 7 | | 21 | 25 | | 22 | 6 | | 23 | 20 | | 24 | 17 | | 25 | 2 | | 26 | 3 | | 27 | 6 | | 28 | 14 | | 29 | 19 | | 30 | 4 | | 31 | 10 | | 32 | 8 | | 33 | 1 | | 34 | 6 | | 35 | 2 | | 36 | 20 | | 37 | 3 | | 38 | 22 | | 39 | 3 | | 40 | 4 | | 41 | 7 | | 42 | 8 | | 43 | 12 | | 44 | 11 | | 45 | 13 | | 46 | 8 | | 47 | 6 | | 48 | 13 | | 49 | 13 |
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| 34.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.2524271844660194 | | totalSentences | 103 | | uniqueOpeners | 26 | |
| 34.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 96 | | matches | | 0 | "Just one step to confirm" |
| | ratio | 0.01 | |
| 45.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 96 | | matches | | 0 | "It hammered down on the" | | 1 | "She checked her watch, the" | | 2 | "She had been certain he" | | 3 | "He was infrastructure." | | 4 | "She didn't run yet." | | 5 | "It burned oxygen and made" | | 6 | "She moved with military precision," | | 7 | "He wasn't running; he was" | | 8 | "She caught a glimpse of" | | 9 | "He knew he was being" | | 10 | "He didn't stop until he" | | 11 | "He didn't turn." | | 12 | "He reached into his inner" | | 13 | "It glinted in the flicker" | | 14 | "He pressed it against a" | | 15 | "She looked at the wall," | | 16 | "It wasn't magic in the" | | 17 | "It was the kind of" | | 18 | "She could hear the sound" | | 19 | "She left the jurisdiction." |
| | ratio | 0.438 | |
| 48.54% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 96 | | matches | | 0 | "The rain did not fall" | | 1 | "It hammered down on the" | | 2 | "She checked her watch, the" | | 3 | "She had been certain he" | | 4 | "The olive-skinned man with the" | | 5 | "The one the clique fed" | | 6 | "Quinn’s jaw tightened, the muscles" | | 7 | "This man wasn't just a" | | 8 | "He was infrastructure." | | 9 | "Quinn stepped off the curb," | | 10 | "She didn't run yet." | | 11 | "Running was for amateurs." | | 12 | "It burned oxygen and made" | | 13 | "She moved with military precision," | | 14 | "Tomás had turned a corner" | | 15 | "He wasn't running; he was" | | 16 | "Quinn followed, her breathing shallow" | | 17 | "She caught a glimpse of" | | 18 | "He knew he was being" | | 19 | "He didn't stop until he" |
| | ratio | 0.823 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 96 | | matches | | 0 | "Now the rumor was a" | | 1 | "If she stepped into that" | | 2 | "If she stayed, she waited" | | 3 | "If she followed, she would" | | 4 | "If she turned back, she" |
| | ratio | 0.052 | |
| 26.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 8 | | matches | | 0 | "Quinn’s jaw tightened, the muscles in her face set as though she were chisel-cut for this exact moment." | | 1 | "Three years since Morris had died in a warehouse that looked too much like a slaughterhouse, and three years since she realized the paperwork was a lie." | | 2 | "Rain dripped from her hood, masking her hair, which was cropped short and salt-and-pepper salt against the black night." | | 3 | "It was the kind of work that had cost him his NHS license." | | 4 | "The Metropolitan Police wanted her to follow procedure, to call for a raid team, to wait for a warrant signed by a judge who didn't understand the supernatural." | | 5 | "She thought of the wound on her partner’s chest that looked like it had been cauterized by something unnatural." | | 6 | "The air from the tunnel smelled of ozone and old earth, a scent that shouldn't exist this deep underground without an industrial leak." | | 7 | "It was a choice that would change her file, change her reputation, change her life." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice flat" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 2 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |