| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.78% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1186 | | 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) | |
| 74.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1186 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "etched" | | 1 | "flicker" | | 2 | "standard" | | 3 | "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 | 36 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 36 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 45 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 105 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1186 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 794 | | uniqueNames | 14 | | maxNameDensity | 1.13 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Veil | 2 | | Market | 2 | | Met | 1 | | Harlow | 9 | | Eva | 6 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Oxford | 1 | | Ancient | 1 | | History | 1 | | Voss | 3 | | Morris | 3 |
| | persons | | 0 | "Market" | | 1 | "Met" | | 2 | "Harlow" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Museum" | | 6 | "Voss" | | 7 | "Morris" |
| | places | | | globalScore | 0.933 | | windowScore | 0.833 | |
| 74.24% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 33 | | glossingSentenceCount | 1 | | matches | | 0 | "seemed disjointed the lack of stolen goods, the intact entry lock, the broken compass, slots into place" |
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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 | 1186 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 45 | | matches | (empty) | |
| 47.62% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 8 | | mean | 148.25 | | std | 46.95 | | cv | 0.317 | | sampleLengths | | 0 | 114 | | 1 | 175 | | 2 | 118 | | 3 | 104 | | 4 | 233 | | 5 | 154 | | 6 | 197 | | 7 | 91 |
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| 56.53% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 36 | | matches | | 0 | "is snapped" | | 1 | "was supposed" | | 2 | "was supposed" | | 3 | "was found" | | 4 | "get unfettered" | | 5 | "was supposed" | | 6 | "been unaccounted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 124 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 756 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.022486772486772486 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.011904761904761904 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 45 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 45 | | mean | 26.36 | | std | 21.61 | | cv | 0.82 | | sampleLengths | | 0 | 25 | | 1 | 25 | | 2 | 14 | | 3 | 29 | | 4 | 21 | | 5 | 16 | | 6 | 31 | | 7 | 9 | | 8 | 42 | | 9 | 4 | | 10 | 26 | | 11 | 1 | | 12 | 18 | | 13 | 28 | | 14 | 15 | | 15 | 103 | | 16 | 17 | | 17 | 34 | | 18 | 22 | | 19 | 31 | | 20 | 4 | | 21 | 2 | | 22 | 15 | | 23 | 3 | | 24 | 46 | | 25 | 35 | | 26 | 21 | | 27 | 36 | | 28 | 71 | | 29 | 13 | | 30 | 15 | | 31 | 11 | | 32 | 10 | | 33 | 105 | | 34 | 23 | | 35 | 34 | | 36 | 37 | | 37 | 22 | | 38 | 4 | | 39 | 49 | | 40 | 28 | | 41 | 25 | | 42 | 27 | | 43 | 26 | | 44 | 13 |
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| 73.48% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.45454545454545453 | | totalSentences | 44 | | uniqueOpeners | 20 | |
| 95.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 35 | | matches | | 0 | "Instead, only one body lay" |
| | ratio | 0.029 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 9 | | totalSentences | 35 | | matches | | 0 | "It clatters into the leg" | | 1 | "She waves him off, bends" | | 2 | "She knew exactly where they" | | 3 | "She had mapped every abandoned" | | 4 | "She checks the cracked leather" | | 5 | "She recognizes Eva Kowalski’s file," | | 6 | "She tucks a strand of" | | 7 | "She reaches for her radio" | | 8 | "They cut out, plunging the" |
| | ratio | 0.257 | |
| 2.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 32 | | totalSentences | 35 | | matches | | 0 | "Harlow’s boot connects with a" | | 1 | "It clatters into the leg" | | 2 | "She waves him off, bends" | | 3 | "Verdigris crumbles off the brass" | | 4 | "The needle is snapped clean" | | 5 | "This is the Veil Market," | | 6 | "The Met received an anonymous" | | 7 | "She knew exactly where they" | | 8 | "She had mapped every abandoned" | | 9 | "Tonight was full moon." | | 10 | "She checks the cracked leather" | | 11 | "The market was supposed to" | | 12 | "The lead scenes of crimes" | | 13 | "Harlow tucks the broken compass" | | 14 | "She recognizes Eva Kowalski’s file," | | 15 | "Eva approaches slowly, her round" | | 16 | "She tucks a strand of" | | 17 | "Harlow nods to the body," | | 18 | "Eva tucks another strand of" | | 19 | "A thick hardcover peeks out" |
| | ratio | 0.914 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 35 | | matches | (empty) | | ratio | 0 | |
| 27.65% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 5 | | matches | | 0 | "The needle is snapped clean from its pivot, the metal bent as if someone crushed it between their thumb and forefinger." | | 1 | "Instead, only one body lay on the platform, surrounded by half packed crates and a handful of confused uniformed officers who thought they were processing a gan…" | | 2 | "Harlow’s mind races, connects the missing tokens, the broken compass, the warding spike that killed her partner, the same weapon that killed Voss." | | 3 | "All the evidence that had seemed disjointed, the lack of stolen goods, the intact entry lock, the broken compass, slots into place." | | 4 | "A targeted hit by someone who knew the market’s schedule, who had his own bone token to get past the unbreachable lock, who had access to police dispatch logs t…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |