| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 71 | | tagDensity | 0.239 | | leniency | 0.479 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1892 | | totalAiIsmAdverbs | 3 | | 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) | |
| 86.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1892 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "glint" | | 1 | "velvet" | | 2 | "etched" | | 3 | "quivered" | | 4 | "trembled" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "let out a breath" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 158 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 158 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 211 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1891 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 60 | | wordCount | 1371 | | uniqueNames | 8 | | maxNameDensity | 1.68 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Bell" | | discoveredNames | | Bell | 18 | | Eva | 11 | | Kowalski | 1 | | Veil | 1 | | Market | 1 | | Camden | 1 | | Quinn | 23 | | Venn | 4 |
| | persons | | 0 | "Bell" | | 1 | "Eva" | | 2 | "Kowalski" | | 3 | "Market" | | 4 | "Quinn" |
| | places | | | globalScore | 0.661 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 105 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1891 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 211 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 103 | | mean | 18.36 | | std | 16.32 | | cv | 0.889 | | sampleLengths | | 0 | 10 | | 1 | 26 | | 2 | 10 | | 3 | 3 | | 4 | 4 | | 5 | 31 | | 6 | 79 | | 7 | 33 | | 8 | 4 | | 9 | 41 | | 10 | 3 | | 11 | 2 | | 12 | 2 | | 13 | 2 | | 14 | 11 | | 15 | 19 | | 16 | 10 | | 17 | 1 | | 18 | 26 | | 19 | 52 | | 20 | 12 | | 21 | 45 | | 22 | 44 | | 23 | 28 | | 24 | 4 | | 25 | 11 | | 26 | 6 | | 27 | 16 | | 28 | 4 | | 29 | 57 | | 30 | 9 | | 31 | 5 | | 32 | 26 | | 33 | 4 | | 34 | 4 | | 35 | 6 | | 36 | 4 | | 37 | 51 | | 38 | 23 | | 39 | 25 | | 40 | 14 | | 41 | 6 | | 42 | 20 | | 43 | 8 | | 44 | 8 | | 45 | 22 | | 46 | 33 | | 47 | 7 | | 48 | 26 | | 49 | 11 |
| |
| 74.17% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 14 | | totalSentences | 158 | | matches | | 0 | "were cracked" | | 1 | "been told" | | 2 | "been marked" | | 3 | "been etched" | | 4 | "was sealed" | | 5 | "was dusted" | | 6 | "was lined" | | 7 | "was discovered" | | 8 | "been wiped" | | 9 | "was cramped" | | 10 | "was interrupted" | | 11 | "been carried" | | 12 | "been laid" | | 13 | "been made" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 222 | | matches | | 0 | "was already looking" | | 1 | "was already reaching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 211 | | ratio | 0.005 | | matches | | 0 | "That meant the impossible part was a lie—or the truth had been made to look like one." |
| |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1377 | | adjectiveStacks | 2 | | stackExamples | | 0 | "same crescent-shaped chip" | | 1 | "thin red-brown streak" |
| | adverbCount | 31 | | adverbRatio | 0.02251270878721859 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.002904865649963689 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 211 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 211 | | mean | 8.96 | | std | 5.4 | | cv | 0.603 | | sampleLengths | | 0 | 10 | | 1 | 10 | | 2 | 16 | | 3 | 6 | | 4 | 4 | | 5 | 3 | | 6 | 4 | | 7 | 16 | | 8 | 15 | | 9 | 7 | | 10 | 11 | | 11 | 20 | | 12 | 12 | | 13 | 13 | | 14 | 16 | | 15 | 11 | | 16 | 22 | | 17 | 4 | | 18 | 8 | | 19 | 15 | | 20 | 9 | | 21 | 9 | | 22 | 3 | | 23 | 2 | | 24 | 2 | | 25 | 2 | | 26 | 11 | | 27 | 7 | | 28 | 8 | | 29 | 4 | | 30 | 10 | | 31 | 1 | | 32 | 15 | | 33 | 11 | | 34 | 15 | | 35 | 14 | | 36 | 12 | | 37 | 11 | | 38 | 12 | | 39 | 17 | | 40 | 16 | | 41 | 8 | | 42 | 4 | | 43 | 10 | | 44 | 9 | | 45 | 13 | | 46 | 12 | | 47 | 6 | | 48 | 22 | | 49 | 4 |
| |
| 52.29% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.35071090047393366 | | totalSentences | 211 | | uniqueOpeners | 74 | |
| 24.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 137 | | matches | | 0 | "Further off, someone laughed, and" |
| | ratio | 0.007 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 137 | | matches | | 0 | "He was already looking past" | | 1 | "Its enamel signs were cracked," | | 2 | "She noticed them noticing." | | 3 | "She tucked a curl of" | | 4 | "Her satchel bumped against her" | | 5 | "Her freckles stood out in" | | 6 | "They watched Quinn approach but" | | 7 | "He was middle-aged, with a" | | 8 | "His eyes were open." | | 9 | "His right hand curled around" | | 10 | "They crossed the room from" | | 11 | "She tucked her hair behind" | | 12 | "She studied the bolt." | | 13 | "She turned back to the" | | 14 | "Its frame was clean around" | | 15 | "He passed her his torch." | | 16 | "She touched the frame of" | | 17 | "It shifted half an inch." | | 18 | "It smelled of damp stone" | | 19 | "She glanced back at Venn." |
| | ratio | 0.168 | |
| 51.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 112 | | totalSentences | 137 | | matches | | 0 | "The bone token was cold" | | 1 | "Quinn slid it into the" | | 2 | "The mechanism clicked somewhere deep" | | 3 | "DS Bell said behind her" | | 4 | "He was already looking past" | | 5 | "Quinn tucked the token into" | | 6 | "The station had been dead" | | 7 | "Its enamel signs were cracked," | | 8 | "A woman in a veil" | | 9 | "A man with silver rings" | | 10 | "The Veil Market had moved" | | 11 | "Quinn had been told this" | | 12 | "She noticed them noticing." | | 13 | "She tucked a curl of" | | 14 | "Her satchel bumped against her" | | 15 | "Eva glanced at her over" | | 16 | "Her freckles stood out in" | | 17 | "Bell caught up, ducking beneath" | | 18 | "The platform opened before them," | | 19 | "They watched Quinn approach but" |
| | ratio | 0.818 | |
| 72.99% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 137 | | matches | | 0 | "Whoever had forced the door" | | 1 | "Whoever made the print had" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 63 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 71 | | tagDensity | 0.239 | | leniency | 0.479 | | rawRatio | 0 | | effectiveRatio | 0 | |