| 91.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 2 | | adverbTags | | 0 | "He glanced around [around]" | | 1 | "Herrera said quietly [quietly]" |
| | dialogueSentences | 37 | | tagDensity | 0.351 | | leniency | 0.703 | | rawRatio | 0.154 | | effectiveRatio | 0.108 | |
| 90.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1000 | | totalAiIsmAdverbs | 2 | | 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) | |
| 80.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1000 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "flickered" | | 2 | "unreadable" | | 3 | "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 | 68 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 68 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | 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 | 998 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 99.20% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 20 | | wordCount | 689 | | uniqueNames | 10 | | maxNameDensity | 1.02 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 7 | | Shoreditch | 1 | | Camden | 1 | | London | 1 | | Morris | 3 | | Veil | 1 | | Market | 1 | | Spanish | 1 | | Herrera | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Morris" | | 4 | "Market" | | 5 | "Herrera" |
| | places | | | globalScore | 0.992 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 998 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 92 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 23.21 | | std | 16.09 | | cv | 0.693 | | sampleLengths | | 0 | 44 | | 1 | 26 | | 2 | 61 | | 3 | 30 | | 4 | 14 | | 5 | 60 | | 6 | 6 | | 7 | 5 | | 8 | 3 | | 9 | 44 | | 10 | 10 | | 11 | 26 | | 12 | 36 | | 13 | 17 | | 14 | 15 | | 15 | 25 | | 16 | 22 | | 17 | 15 | | 18 | 16 | | 19 | 15 | | 20 | 33 | | 21 | 20 | | 22 | 1 | | 23 | 1 | | 24 | 45 | | 25 | 11 | | 26 | 41 | | 27 | 43 | | 28 | 43 | | 29 | 28 | | 30 | 13 | | 31 | 6 | | 32 | 44 | | 33 | 5 | | 34 | 18 | | 35 | 28 | | 36 | 51 | | 37 | 14 | | 38 | 8 | | 39 | 19 | | 40 | 18 | | 41 | 3 | | 42 | 15 |
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| 94.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 68 | | matches | | 0 | "was gone" | | 1 | "being forced" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 119 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 92 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 691 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.020260492040520984 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.008683068017366137 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 10.85 | | std | 6.13 | | cv | 0.565 | | sampleLengths | | 0 | 20 | | 1 | 24 | | 2 | 12 | | 3 | 14 | | 4 | 8 | | 5 | 15 | | 6 | 13 | | 7 | 14 | | 8 | 11 | | 9 | 6 | | 10 | 6 | | 11 | 18 | | 12 | 14 | | 13 | 12 | | 14 | 29 | | 15 | 19 | | 16 | 3 | | 17 | 3 | | 18 | 5 | | 19 | 3 | | 20 | 7 | | 21 | 14 | | 22 | 15 | | 23 | 8 | | 24 | 7 | | 25 | 3 | | 26 | 18 | | 27 | 8 | | 28 | 17 | | 29 | 19 | | 30 | 11 | | 31 | 6 | | 32 | 15 | | 33 | 10 | | 34 | 15 | | 35 | 17 | | 36 | 5 | | 37 | 15 | | 38 | 16 | | 39 | 8 | | 40 | 7 | | 41 | 7 | | 42 | 26 | | 43 | 7 | | 44 | 13 | | 45 | 1 | | 46 | 1 | | 47 | 16 | | 48 | 6 | | 49 | 7 |
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| 59.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.40217391304347827 | | totalSentences | 92 | | uniqueOpeners | 37 | |
| 49.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 67 | | matches | | | ratio | 0.015 | |
| 88.66% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 67 | | matches | | 0 | "She'd been following the whispers" | | 1 | "Her partner, DS Morris, had" | | 2 | "She knew what they were" | | 3 | "She pushed harder, her lungs" | | 4 | "She'd found it." | | 5 | "She nodded at the medallion" | | 6 | "He tilted his head, studying" | | 7 | "She showed her badge, though" | | 8 | "He glanced around, lowering his" | | 9 | "She met his gaze" | | 10 | "He studied her for a" | | 11 | "He pulled a small object" | | 12 | "She caught it, felt its" | | 13 | "She turned the token over" | | 14 | "He stepped closer, his voice" | | 15 | "She'd spent three years circling" | | 16 | "She thought of Morris." | | 17 | "He shook his head" | | 18 | "She looked at the dark" | | 19 | "She thought about calling for" |
| | ratio | 0.328 | |
| 27.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 67 | | matches | | 0 | "The suspect vaulted the market" | | 1 | "Detective Harlow Quinn cursed under" | | 2 | "She'd been following the whispers" | | 3 | "A clique operating in London's" | | 4 | "Her partner, DS Morris, had" | | 5 | "The official report called it" | | 6 | "She knew what they were" | | 7 | "The suspect disappeared around a" | | 8 | "She pushed harder, her lungs" | | 9 | "The corridor opened into a" | | 10 | "The Veil Market sprawled before" | | 11 | "Stalls carved from shadow and" | | 12 | "Creatures she couldn't name browsed" | | 13 | "She'd found it." | | 14 | "Quinn spun, hand moving to" | | 15 | "The man standing behind her" | | 16 | "Dark curls, olive skin, a" | | 17 | "Something glinted at his throat," | | 18 | "She nodded at the medallion" | | 19 | "He tilted his head, studying" |
| | ratio | 0.866 | |
| 74.63% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 67 | | matches | | 0 | "Now she was deep beneath" |
| | ratio | 0.015 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 6 | | matches | | 0 | "Detective Harlow Quinn cursed under her breath, her boots splashing through puddles that reflected the sickly green neon bleeding down from the street above." | | 1 | "Now she was deep beneath Camden, chasing a ghost through corridors that shouldn't exist." | | 2 | "A clique operating in London's supernatural underworld, trading in things that had no business existing." | | 3 | "Stalls carved from shadow and stone peddled vials of luminescent liquid, sheathed blades that hummed with barely contained power, books with covers made from so…" | | 4 | "Creatures she couldn't name browsed alongside humans, their conversations a discordant mix of languages that made her head ache." | | 5 | "Something that made the hair rise on the back of her neck." |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | 0 | "He stepped, his voice dropping" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.054 | | leniency | 0.108 | | rawRatio | 0 | | effectiveRatio | 0 | |