| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.533 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 81.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1090 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "sharply" | | 1 | "cautiously" | | 2 | "suddenly" | | 3 | "slowly" |
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| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1090 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "charged" | | 1 | "pounding" | | 2 | "fractured" | | 3 | "could feel" | | 4 | "perfect" | | 5 | "navigated" | | 6 | "echoes" | | 7 | "shattered" | | 8 | "beacon" | | 9 | "weight" | | 10 | "echoing" | | 11 | "silence" | | 12 | "echo" | | 13 | "flickered" | | 14 | "glinting" | | 15 | "wavered" | | 16 | "flicker" | | 17 | "gloom" |
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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 | 140 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 140 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 147 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1083 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 56.22% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1013 | | uniqueNames | 11 | | maxNameDensity | 1.88 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 19 | | Paramedic | 1 | | London | 2 | | Herrera | 13 | | Morris | 1 | | Tube | 1 | | Cowardice | 1 | | Victorian | 1 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Cowardice" |
| | places | | | globalScore | 0.562 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | 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 | 1083 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 147 | | matches | (empty) | |
| 91.73% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 26.41 | | std | 12.44 | | cv | 0.471 | | sampleLengths | | 0 | 25 | | 1 | 29 | | 2 | 26 | | 3 | 21 | | 4 | 12 | | 5 | 42 | | 6 | 30 | | 7 | 35 | | 8 | 13 | | 9 | 31 | | 10 | 22 | | 11 | 18 | | 12 | 32 | | 13 | 11 | | 14 | 33 | | 15 | 34 | | 16 | 77 | | 17 | 48 | | 18 | 29 | | 19 | 26 | | 20 | 27 | | 21 | 28 | | 22 | 26 | | 23 | 23 | | 24 | 29 | | 25 | 33 | | 26 | 33 | | 27 | 28 | | 28 | 26 | | 29 | 16 | | 30 | 13 | | 31 | 11 | | 32 | 12 | | 33 | 48 | | 34 | 24 | | 35 | 14 | | 36 | 12 | | 37 | 36 | | 38 | 20 | | 39 | 13 | | 40 | 17 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 140 | | matches | | 0 | "was tangled" | | 1 | "was barred" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 191 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 147 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 508 | | adjectiveStacks | 1 | | stackExamples | | 0 | "Paramedic gone off-grid." |
| | adverbCount | 10 | | adverbRatio | 0.01968503937007874 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.003937007874015748 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 147 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 147 | | mean | 7.37 | | std | 4.69 | | cv | 0.637 | | sampleLengths | | 0 | 14 | | 1 | 11 | | 2 | 7 | | 3 | 3 | | 4 | 12 | | 5 | 7 | | 6 | 7 | | 7 | 7 | | 8 | 12 | | 9 | 5 | | 10 | 5 | | 11 | 1 | | 12 | 10 | | 13 | 8 | | 14 | 4 | | 15 | 23 | | 16 | 19 | | 17 | 9 | | 18 | 4 | | 19 | 12 | | 20 | 5 | | 21 | 21 | | 22 | 5 | | 23 | 9 | | 24 | 5 | | 25 | 1 | | 26 | 3 | | 27 | 4 | | 28 | 6 | | 29 | 5 | | 30 | 4 | | 31 | 16 | | 32 | 3 | | 33 | 10 | | 34 | 9 | | 35 | 12 | | 36 | 6 | | 37 | 6 | | 38 | 10 | | 39 | 16 | | 40 | 7 | | 41 | 1 | | 42 | 3 | | 43 | 1 | | 44 | 3 | | 45 | 1 | | 46 | 13 | | 47 | 5 | | 48 | 4 | | 49 | 6 |
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| 75.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.46938775510204084 | | totalSentences | 147 | | uniqueOpeners | 69 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 120 | | matches | | 0 | "Too many sleepless nights staring" | | 1 | "Maybe the only smart way." | | 2 | "Definitely voices, drawing closer." | | 3 | "At least three people, arguing" |
| | ratio | 0.033 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 120 | | matches | | 0 | "she muttered, recalling his file" | | 1 | "She'd picked a hell of" | | 2 | "Her words dissolved into the" | | 3 | "He wasn't slowing down." | | 4 | "He couldn't hide for long." | | 5 | "She tucked the thought away." | | 6 | "She could feel it." | | 7 | "She navigated by echoes and" | | 8 | "She listened harder." | | 9 | "It hung ajar, lock shattered." | | 10 | "She eased it open." | | 11 | "You're a detective, not a" | | 12 | "It thrummed against her skin," | | 13 | "She looked back the way" | | 14 | "She strained her ears." | | 15 | "She ducked into a branching" | | 16 | "She let them pass, then" | | 17 | "He'd had no time to" | | 18 | "She ignored them, focusing instead" | | 19 | "It led her to a" |
| | ratio | 0.225 | |
| 93.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 88 | | totalSentences | 120 | | matches | | 0 | "Detective Harlow Quinn charged through" | | 1 | "The suspect darted ahead, reflected" | | 2 | "she muttered, recalling his file" | | 3 | "The suspect veered sharply into" | | 4 | "Quinn followed, boots pounding the" | | 5 | "Steam rose from grates, silhouetting" | | 6 | "Quinn hit the fence climbing." | | 7 | "Razor wire at the top." | | 8 | "She'd picked a hell of" | | 9 | "Her words dissolved into the" | | 10 | "He wasn't slowing down." | | 11 | "The razor ripped through the" | | 12 | "The alley opened into a" | | 13 | "He couldn't hide for long." | | 14 | "A case they never discussed." | | 15 | "The supernatural secrets it dredged" | | 16 | "She tucked the thought away." | | 17 | "Herrera was close." | | 18 | "She could feel it." | | 19 | "Quinn stalked forward, avoiding the" |
| | ratio | 0.733 | |
| 83.33% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 120 | | matches | | 0 | "Now leading her on this" | | 1 | "If Herrera answered even one" |
| | ratio | 0.017 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 16.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Quinn pressed (press)" |
| | dialogueSentences | 15 | | tagDensity | 0.467 | | leniency | 0.933 | | rawRatio | 0.286 | | effectiveRatio | 0.267 | |