| 66.67% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva followed close [close]" | | 1 | "Eva’s voice hitched slightly [slightly]" |
| | dialogueSentences | 30 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.167 | | effectiveRatio | 0.133 | |
| 85.90% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1418 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "sharply" | | 1 | "slightly" | | 2 | "perfectly" |
| |
| 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) | |
| 33.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1418 | | totalAiIsms | 19 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | word | "voice hitched" | | count | 1 |
| | 13 | |
| | highlights | | 0 | "measured" | | 1 | "loomed" | | 2 | "macabre" | | 3 | "grave" | | 4 | "predator" | | 5 | "calculated" | | 6 | "standard" | | 7 | "weight" | | 8 | "shattered" | | 9 | "glint" | | 10 | "etched" | | 11 | "magnetic" | | 12 | "voice hitched" | | 13 | "traced" |
| |
| 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 | 94 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 112 | | 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 | 1407 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1029 | | uniqueNames | 8 | | maxNameDensity | 2.62 | | worstName | "Harlow" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Morris | 2 | | British | 1 | | Museum | 1 | | Harlow | 27 | | Eva | 14 | | North | 1 |
| | persons | | | places | (empty) | | globalScore | 0.188 | | windowScore | 0.167 | |
| 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 | 1407 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 112 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 31.27 | | std | 20.22 | | cv | 0.647 | | sampleLengths | | 0 | 66 | | 1 | 41 | | 2 | 18 | | 3 | 86 | | 4 | 39 | | 5 | 2 | | 6 | 4 | | 7 | 66 | | 8 | 10 | | 9 | 2 | | 10 | 37 | | 11 | 14 | | 12 | 17 | | 13 | 25 | | 14 | 54 | | 15 | 33 | | 16 | 42 | | 17 | 19 | | 18 | 34 | | 19 | 26 | | 20 | 45 | | 21 | 39 | | 22 | 37 | | 23 | 11 | | 24 | 52 | | 25 | 10 | | 26 | 54 | | 27 | 6 | | 28 | 15 | | 29 | 1 | | 30 | 39 | | 31 | 43 | | 32 | 10 | | 33 | 30 | | 34 | 17 | | 35 | 33 | | 36 | 45 | | 37 | 5 | | 38 | 60 | | 39 | 42 | | 40 | 50 | | 41 | 10 | | 42 | 21 | | 43 | 60 | | 44 | 37 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 94 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 156 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 112 | | ratio | 0.009 | | matches | | 0 | "A thick layer of gray dust coated the concrete, undisturbed for years—except for a single set of frantic, scuffing footprints." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1039 | | adjectiveStacks | 1 | | stackExamples | | 0 | "singular, star-shaped laceration." |
| | adverbCount | 21 | | adverbRatio | 0.02021174205967276 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.009624639076034648 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 112 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 112 | | mean | 12.56 | | std | 7.58 | | cv | 0.603 | | sampleLengths | | 0 | 17 | | 1 | 17 | | 2 | 19 | | 3 | 13 | | 4 | 5 | | 5 | 18 | | 6 | 18 | | 7 | 18 | | 8 | 2 | | 9 | 15 | | 10 | 10 | | 11 | 9 | | 12 | 17 | | 13 | 9 | | 14 | 12 | | 15 | 12 | | 16 | 10 | | 17 | 2 | | 18 | 18 | | 19 | 9 | | 20 | 2 | | 21 | 4 | | 22 | 22 | | 23 | 16 | | 24 | 15 | | 25 | 13 | | 26 | 10 | | 27 | 2 | | 28 | 14 | | 29 | 23 | | 30 | 14 | | 31 | 17 | | 32 | 16 | | 33 | 9 | | 34 | 29 | | 35 | 25 | | 36 | 10 | | 37 | 4 | | 38 | 19 | | 39 | 9 | | 40 | 33 | | 41 | 10 | | 42 | 9 | | 43 | 34 | | 44 | 9 | | 45 | 3 | | 46 | 3 | | 47 | 11 | | 48 | 14 | | 49 | 31 |
| |
| 48.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.33035714285714285 | | totalSentences | 112 | | uniqueOpeners | 37 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 91 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 91 | | matches | | 0 | "She rolled her shoulder, easing" | | 1 | "Her brown eyes tracked the" | | 2 | "She tapped a gloved finger" | | 3 | "They were blackened, cauterized instantly," | | 4 | "Her curly red hair caught" | | 5 | "She approached the scattered evidence" | | 6 | "She paced a slow circle" | | 7 | "Her knuckles whitened on the" | | 8 | "She bypassed Eva, ignoring the" | | 9 | "Her boots crunched over shattered" | | 10 | "She swept her flashlight across" | | 11 | "She withdrew a pair of" | | 12 | "It was a heavy brass" | | 13 | "It did not seek North." | | 14 | "She tucked another curl behind" | | 15 | "They faced the blank wall." | | 16 | "She pressed her thumb into" |
| | ratio | 0.187 | |
| 9.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 82 | | totalSentences | 91 | | matches | | 0 | "The scent of oxidized iron" | | 1 | "Harlow kept her spine rigid," | | 2 | "Harlow unbuttoned her trench coat." | | 3 | "She rolled her shoulder, easing" | | 4 | "The victim lay suspended in" | | 5 | "Her brown eyes tracked the" | | 6 | "Carmine droplets beaded on the" | | 7 | "She tapped a gloved finger" | | 8 | "Blood did not fall upward" | | 9 | "The edges of the torn" | | 10 | "They were blackened, cauterized instantly," | | 11 | "Memories of scorched pavement and" | | 12 | "The phantom smell of ozone" | | 13 | "Harlow shoved the memory into" | | 14 | "Harlow turned her head." | | 15 | "Eva stood just outside the" | | 16 | "The British Museum research assistant" | | 17 | "Her curly red hair caught" | | 18 | "Harlow stood, brushing a flake" | | 19 | "Eva pushed her round glasses" |
| | ratio | 0.901 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 91 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 1 | | matches | | 0 | "She rolled her shoulder, easing a stiff muscle, before halting at the edge of the central floodlight’s halo." |
| |
| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva retreated, her knuckles turning bone-white" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |