| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | 0 | "Tomás melted back [back]" |
| | dialogueSentences | 29 | | tagDensity | 0.345 | | leniency | 0.69 | | rawRatio | 0.1 | | effectiveRatio | 0.069 | |
| 95.94% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1232 | | 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) | |
| 63.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1232 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "warmth" | | 1 | "churning" | | 2 | "footsteps" | | 3 | "constructed" | | 4 | "silk" | | 5 | "lurch" | | 6 | "weight" | | 7 | "depths" | | 8 | "familiar" |
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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 | 101 | | matches | (empty) | |
| 86.28% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 101 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | 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 | 3 | | markdownWords | 9 | | totalWords | 1224 | | ratio | 0.007 | | matches | | 0 | "Morris would have cut through the alley." | | 1 | "Nonsense" | | 2 | "Superstition" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 1 | | matches | | 0 | "*Nonsense*, she told herself." |
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| 75.96% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1013 | | uniqueNames | 16 | | maxNameDensity | 1.48 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Jamestown | 1 | | Road | 1 | | Quinn | 15 | | Camden | 3 | | Raven | 2 | | Nest | 2 | | Lock | 1 | | Tube | 2 | | Victorian | 1 | | Morris | 4 | | Saint | 1 | | Christopher | 1 | | Tomás | 4 | | Spanish | 1 | | Market | 1 | | Backup | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Camden" | | 2 | "Raven" | | 3 | "Lock" | | 4 | "Morris" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" | | 8 | "Spanish" | | 9 | "Market" | | 10 | "Backup" |
| | places | | | globalScore | 0.76 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 1 | | matches | | 0 | "quite fit, of knowing something was wrong without being able to name it" |
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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 | 1224 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 120 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 23.54 | | std | 20.13 | | cv | 0.855 | | sampleLengths | | 0 | 40 | | 1 | 3 | | 2 | 6 | | 3 | 75 | | 4 | 23 | | 5 | 7 | | 6 | 25 | | 7 | 46 | | 8 | 5 | | 9 | 3 | | 10 | 37 | | 11 | 50 | | 12 | 10 | | 13 | 9 | | 14 | 53 | | 15 | 56 | | 16 | 4 | | 17 | 58 | | 18 | 5 | | 19 | 27 | | 20 | 8 | | 21 | 72 | | 22 | 41 | | 23 | 11 | | 24 | 4 | | 25 | 59 | | 26 | 9 | | 27 | 25 | | 28 | 4 | | 29 | 32 | | 30 | 6 | | 31 | 20 | | 32 | 2 | | 33 | 24 | | 34 | 7 | | 35 | 46 | | 36 | 5 | | 37 | 44 | | 38 | 19 | | 39 | 2 | | 40 | 6 | | 41 | 2 | | 42 | 36 | | 43 | 38 | | 44 | 3 | | 45 | 21 | | 46 | 47 | | 47 | 30 | | 48 | 8 | | 49 | 23 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 101 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 190 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 120 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1021 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.034280117531831536 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.0068560235063663075 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 10.2 | | std | 7.13 | | cv | 0.699 | | sampleLengths | | 0 | 22 | | 1 | 18 | | 2 | 3 | | 3 | 3 | | 4 | 3 | | 5 | 17 | | 6 | 16 | | 7 | 13 | | 8 | 29 | | 9 | 5 | | 10 | 10 | | 11 | 3 | | 12 | 5 | | 13 | 7 | | 14 | 10 | | 15 | 4 | | 16 | 11 | | 17 | 27 | | 18 | 6 | | 19 | 5 | | 20 | 4 | | 21 | 4 | | 22 | 5 | | 23 | 3 | | 24 | 15 | | 25 | 10 | | 26 | 4 | | 27 | 3 | | 28 | 5 | | 29 | 24 | | 30 | 8 | | 31 | 18 | | 32 | 1 | | 33 | 1 | | 34 | 8 | | 35 | 9 | | 36 | 8 | | 37 | 14 | | 38 | 16 | | 39 | 15 | | 40 | 6 | | 41 | 13 | | 42 | 15 | | 43 | 22 | | 44 | 4 | | 45 | 13 | | 46 | 15 | | 47 | 18 | | 48 | 12 | | 49 | 4 |
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| 61.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.44166666666666665 | | totalSentences | 120 | | uniqueOpeners | 53 | |
| 36.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 91 | | matches | | 0 | "Then he was gone, swallowed" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 91 | | matches | | 0 | "He didn't stop." | | 1 | "They never did." | | 2 | "Her knees complained." | | 3 | "Her partner's ghost complained louder." | | 4 | "She ignored the thought and" | | 5 | "Her hand brushed her radio." | | 6 | "She kept running." | | 7 | "She knew where this led." | | 8 | "Her torch clicked on, the" | | 9 | "She knew it was useless." | | 10 | "He pushed off the pillar," | | 11 | "He laughed, but there was" | | 12 | "His eyes dropped to her" | | 13 | "He held up a hand" | | 14 | "Her radio crackled." |
| | ratio | 0.165 | |
| 64.40% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 91 | | matches | | 0 | "The suspect's trainer splashed through" | | 1 | "Quinn's lungs burned as she" | | 2 | "He didn't stop." | | 3 | "They never did." | | 4 | "The figure cut left down" | | 5 | "The green glow of a" | | 6 | "A pub door swung open" | | 7 | "Her knees complained." | | 8 | "Her partner's ghost complained louder." | | 9 | "*Morris would have cut through" | | 10 | "She ignored the thought and" | | 11 | "Alleys got you killed." | | 12 | "Morris had taught her that," | | 13 | "The suspect, a young man" | | 14 | "Quinn went around, losing precious" | | 15 | "Her hand brushed her radio." | | 16 | "Backup would be smart." | | 17 | "Backup would be protocol." | | 18 | "Backup would let him vanish." | | 19 | "She kept running." |
| | ratio | 0.791 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 91 | | matches | (empty) | | ratio | 0 | |
| 6.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 8 | | matches | | 0 | "The suspect, a young man she'd clocked leaving the Raven's Nest twenty minutes ago, vaulted a rubbish bin with the ease of someone who'd done it before." | | 1 | "The entrance to the abandoned Tube station gaped like a wound in the night, its Victorian brickwork crumbling around a doorway that shouldn't exist." | | 2 | "Three years of watching, of collecting evidence that didn't quite fit, of knowing something was wrong without being able to name it." | | 3 | "Something that made her think of her grandmother's stories about the fae." | | 4 | "Stalls crowded every available space, constructed from materials that shouldn't hold together: bone and silk, tarnished silver and what looked horribly like dri…" | | 5 | "Lanterns hung from chains that disappeared into darkness above, their light too yellow, too hungry." | | 6 | "Something small and quick darted between legs, chittering in a language that hurt to hear." | | 7 | "Young, olive-skinned, with short curly dark brown hair and warm brown eyes that held far too much knowledge." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 81.03% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "He laughed (laugh)" | | 1 | "Tomás pressed (press)" |
| | dialogueSentences | 29 | | tagDensity | 0.172 | | leniency | 0.345 | | rawRatio | 0.4 | | effectiveRatio | 0.138 | |