| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1634 | | totalAiIsmAdverbs | 2 | | 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) | |
| 32.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1634 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "long shadow" | | 1 | "chaotic" | | 2 | "shattered" | | 3 | "scanned" | | 4 | "depths" | | 5 | "weight" | | 6 | "resonated" | | 7 | "porcelain" | | 8 | "flickered" | | 9 | "constructed" | | 10 | "intricate" | | 11 | "pulse" | | 12 | "tracing" | | 13 | "imposing" | | 14 | "vibrated" | | 15 | "echoed" | | 16 | "calculated" | | 17 | "chill" |
| |
| 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 | 124 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 124 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 133 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1616 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1523 | | uniqueNames | 20 | | maxNameDensity | 0.98 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Quinn | 15 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 8 | | Camden | 2 | | Maglite | 1 | | Glock | 2 | | London | 2 | | Station | 1 | | Northbound | 1 | | Victorian | 2 | | Veil | 1 | | Market | 1 | | Morris | 3 | | Regent | 1 | | Raven | 1 | | Nest | 1 | | Two | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Tomás" | | 5 | "Morris" | | 6 | "Raven" | | 7 | "Two" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Market" | | 3 | "Regent" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 107 | | 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 | 1616 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 133 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 32.98 | | std | 26.66 | | cv | 0.809 | | sampleLengths | | 0 | 56 | | 1 | 4 | | 2 | 48 | | 3 | 56 | | 4 | 5 | | 5 | 21 | | 6 | 114 | | 7 | 63 | | 8 | 43 | | 9 | 12 | | 10 | 14 | | 11 | 51 | | 12 | 66 | | 13 | 8 | | 14 | 31 | | 15 | 53 | | 16 | 50 | | 17 | 31 | | 18 | 1 | | 19 | 20 | | 20 | 20 | | 21 | 51 | | 22 | 37 | | 23 | 23 | | 24 | 19 | | 25 | 118 | | 26 | 3 | | 27 | 95 | | 28 | 49 | | 29 | 29 | | 30 | 14 | | 31 | 15 | | 32 | 50 | | 33 | 8 | | 34 | 9 | | 35 | 32 | | 36 | 9 | | 37 | 3 | | 38 | 17 | | 39 | 34 | | 40 | 48 | | 41 | 10 | | 42 | 11 | | 43 | 15 | | 44 | 35 | | 45 | 51 | | 46 | 14 | | 47 | 16 | | 48 | 34 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 124 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 243 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 133 | | ratio | 0.008 | | matches | | 0 | "A giant peeling sign clung to the filthy wall above a row of crumbling wooden benches: Camden Station - Northbound." |
| |
| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1541 | | adjectiveStacks | 3 | | stackExamples | | 0 | "claustrophobic, garbage-strewn gap" | | 1 | "Acrid, eye-watering smoke" | | 2 | "small, jagged white object" |
| | adverbCount | 19 | | adverbRatio | 0.012329656067488644 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.004542504866969501 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 133 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 133 | | mean | 12.15 | | std | 6.44 | | cv | 0.53 | | sampleLengths | | 0 | 15 | | 1 | 17 | | 2 | 14 | | 3 | 10 | | 4 | 4 | | 5 | 5 | | 6 | 11 | | 7 | 17 | | 8 | 15 | | 9 | 10 | | 10 | 20 | | 11 | 21 | | 12 | 5 | | 13 | 5 | | 14 | 5 | | 15 | 16 | | 16 | 13 | | 17 | 5 | | 18 | 8 | | 19 | 24 | | 20 | 16 | | 21 | 7 | | 22 | 14 | | 23 | 27 | | 24 | 6 | | 25 | 11 | | 26 | 9 | | 27 | 5 | | 28 | 4 | | 29 | 28 | | 30 | 8 | | 31 | 19 | | 32 | 16 | | 33 | 12 | | 34 | 3 | | 35 | 11 | | 36 | 4 | | 37 | 20 | | 38 | 10 | | 39 | 11 | | 40 | 6 | | 41 | 5 | | 42 | 14 | | 43 | 18 | | 44 | 8 | | 45 | 21 | | 46 | 4 | | 47 | 4 | | 48 | 13 | | 49 | 8 |
| |
| 66.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.41353383458646614 | | totalSentences | 133 | | uniqueOpeners | 55 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 123 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 123 | | matches | | 0 | "Her trench coat hung heavy" | | 1 | "He hit a stack of" | | 2 | "She adjusted her stride, altering" | | 3 | "He maintained a twenty-yard lead." | | 4 | "She drew her ASP baton." | | 5 | "His left foot kicked out." | | 6 | "He collided hard with a" | | 7 | "He grabbed the fence mesh," | | 8 | "He shoved off the fence" | | 9 | "She tracked his chaotic path" | | 10 | "She lowered her center of" | | 11 | "Her target had vanished from" | | 12 | "She swept the area piecemeal." | | 13 | "Her eyes scanned the trash" | | 14 | "She collapsed her baton with" | | 15 | "She slung a leg over" | | 16 | "Her deep voice bounced off" | | 17 | "She ignored the rough, flaking" | | 18 | "Her boots hit a solid" | | 19 | "She dropped into a crouch," |
| | ratio | 0.26 | |
| 73.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 123 | | matches | | 0 | "Harlow Quinn pounded through the" | | 1 | "Her trench coat hung heavy" | | 2 | "Tomás Herrera ignored the command." | | 3 | "He hit a stack of" | | 4 | "The explosive movement sent his" | | 5 | "The harsh amber glare of" | | 6 | "Quinn set her sharp jaw" | | 7 | "She adjusted her stride, altering" | | 8 | "The worn leather watch on" | | 9 | "He maintained a twenty-yard lead." | | 10 | "She drew her ASP baton." | | 11 | "Tomás slipped on a sodden" | | 12 | "His left foot kicked out." | | 13 | "He collided hard with a" | | 14 | "The violent impact tore his" | | 15 | "He grabbed the fence mesh," | | 16 | "He shoved off the fence" | | 17 | "Quinn maintained her precise, metered" | | 18 | "Adrenaline commanded Tomás, forcing him" | | 19 | "She tracked his chaotic path" |
| | ratio | 0.772 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 123 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 80 | | technicalSentenceCount | 1 | | matches | | 0 | "He shoved off the fence and sprinted across a flooded intersection, dodging a blaring yellow cab that threw a massive wave of gray water over his legs." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |