| 57.14% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 1 | | adverbTags | | 0 | "the gatekeeper's voice scraped like [like]" |
| | dialogueSentences | 14 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 1 | | effectiveRatio | 0.143 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 958 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 37.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 958 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "weight" | | 1 | "shattered" | | 2 | "flicked" | | 3 | "trembled" | | 4 | "firmly" | | 5 | "echoed" | | 6 | "gloom" | | 7 | "intricate" | | 8 | "mechanical" | | 9 | "velvet" | | 10 | "flickered" |
| |
| 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 | 71 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 71 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 84 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 957 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 842 | | uniqueNames | 15 | | maxNameDensity | 1.54 | | worstName | "Tomás" | | maxWindowNameDensity | 3 | | worstWindowName | "Tomás" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Harlow | 1 | | Quinn | 9 | | Tomás | 13 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Glock | 3 | | London | 1 | | Tube | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Saint" | | 2 | "Christopher" | | 3 | "Harlow" | | 4 | "Quinn" | | 5 | "Tomás" | | 6 | "Glock" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Tottenham" | | 4 | "Court" | | 5 | "Road" | | 6 | "London" |
| | globalScore | 0.728 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | 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 | 957 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 23.93 | | std | 17.29 | | cv | 0.723 | | sampleLengths | | 0 | 16 | | 1 | 49 | | 2 | 63 | | 3 | 3 | | 4 | 12 | | 5 | 9 | | 6 | 32 | | 7 | 50 | | 8 | 46 | | 9 | 61 | | 10 | 21 | | 11 | 5 | | 12 | 12 | | 13 | 9 | | 14 | 18 | | 15 | 6 | | 16 | 4 | | 17 | 12 | | 18 | 11 | | 19 | 37 | | 20 | 29 | | 21 | 46 | | 22 | 13 | | 23 | 16 | | 24 | 7 | | 25 | 13 | | 26 | 12 | | 27 | 4 | | 28 | 10 | | 29 | 15 | | 30 | 48 | | 31 | 54 | | 32 | 14 | | 33 | 34 | | 34 | 38 | | 35 | 21 | | 36 | 24 | | 37 | 13 | | 38 | 45 | | 39 | 25 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 142 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 84 | | ratio | 0.012 | | matches | | 0 | "Ahead lay the impossible—the same impossible shadow that had swallowed her partner three years ago and left behind a classified, redacted case file." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 855 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.00935672514619883 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0023391812865497076 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 84 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 84 | | mean | 11.39 | | std | 5.12 | | cv | 0.449 | | sampleLengths | | 0 | 16 | | 1 | 12 | | 2 | 10 | | 3 | 13 | | 4 | 14 | | 5 | 16 | | 6 | 11 | | 7 | 6 | | 8 | 19 | | 9 | 11 | | 10 | 3 | | 11 | 12 | | 12 | 9 | | 13 | 5 | | 14 | 11 | | 15 | 16 | | 16 | 12 | | 17 | 14 | | 18 | 7 | | 19 | 12 | | 20 | 5 | | 21 | 9 | | 22 | 20 | | 23 | 9 | | 24 | 8 | | 25 | 7 | | 26 | 13 | | 27 | 25 | | 28 | 16 | | 29 | 5 | | 30 | 16 | | 31 | 5 | | 32 | 5 | | 33 | 7 | | 34 | 9 | | 35 | 11 | | 36 | 7 | | 37 | 6 | | 38 | 4 | | 39 | 12 | | 40 | 11 | | 41 | 9 | | 42 | 7 | | 43 | 17 | | 44 | 4 | | 45 | 7 | | 46 | 13 | | 47 | 9 | | 48 | 14 | | 49 | 3 |
| |
| 65.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.40476190476190477 | | totalSentences | 84 | | uniqueOpeners | 34 | |
| 46.95% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 71 | | matches | | 0 | "Instead of turning to face" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 71 | | matches | | 0 | "He gripped a heavy canvas" | | 1 | "She did not yell generic" | | 2 | "She kept her chin down," | | 3 | "Her sharp jaw tightened as" | | 4 | "Her voice cut through the" | | 5 | "He veered left into a" | | 6 | "Her left hand brushed the" | | 7 | "She was on her own." | | 8 | "He slipped through the gap" | | 9 | "She unholstered her Glock 17," | | 10 | "She flicked the beam down." | | 11 | "His voice bounced off the" | | 12 | "Her words struck the hollow" | | 13 | "She burst into an abandoned" | | 14 | "His chest heaved." | | 15 | "He held a flat sliver" | | 16 | "He pressed the bone token" | | 17 | "She smelled copper and crushed" | | 18 | "She lowered the gun to" |
| | ratio | 0.268 | |
| 9.30% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 71 | | matches | | 0 | "Tomás Herrera sprinted through the" | | 1 | "He gripped a heavy canvas" | | 2 | "Every impact of his boots" | | 3 | "The silver Saint Christopher medallion" | | 4 | "Detective Harlow Quinn matched his" | | 5 | "Water dripped from her cropped" | | 6 | "She did not yell generic" | | 7 | "She kept her chin down," | | 8 | "Her sharp jaw tightened as" | | 9 | "Her voice cut through the" | | 10 | "Tomás glanced over his shoulder." | | 11 | "Water slicked his olive forehead," | | 12 | "He veered left into a" | | 13 | "Quinn adjusted her angle, cutting" | | 14 | "Her left hand brushed the" | | 15 | "Reinforcements sat stalled three miles" | | 16 | "She was on her own." | | 17 | "Tomás hit the brick wall" | | 18 | "The wood splintered with a" | | 19 | "He slipped through the gap" |
| | ratio | 0.901 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 1 | | matches | | 0 | "Ahead lay the impossible—the same impossible shadow that had swallowed her partner three years ago and left behind a classified, redacted case file." |
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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 | |