| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 69 | | tagDensity | 0.159 | | leniency | 0.319 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1496 | | 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) | |
| 63.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1496 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "velvet" | | 1 | "silk" | | 2 | "measured" | | 3 | "fluttered" | | 4 | "trembled" | | 5 | "weight" | | 6 | "pulse" | | 7 | "raced" | | 8 | "shattered" | | 9 | "vibrated" | | 10 | "glint" |
| |
| 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 | 167 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 167 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 223 | | 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 | 1496 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 14.75% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 69 | | wordCount | 1220 | | uniqueNames | 12 | | maxNameDensity | 2.7 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 3 | | Harlow | 1 | | Quinn | 33 | | Tube | 1 | | Camden | 1 | | Underground | 1 | | Veil | 3 | | Market | 2 | | Sergeant | 1 | | Vale | 17 | | Compass | 2 | | Morris | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Underground" | | 4 | "Market" | | 5 | "Sergeant" | | 6 | "Vale" | | 7 | "Compass" | | 8 | "Morris" |
| | places | | | globalScore | 0.148 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | 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 | 1496 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 223 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 148 | | mean | 10.11 | | std | 10.87 | | cv | 1.076 | | sampleLengths | | 0 | 18 | | 1 | 6 | | 2 | 32 | | 3 | 8 | | 4 | 5 | | 5 | 49 | | 6 | 60 | | 7 | 5 | | 8 | 37 | | 9 | 7 | | 10 | 5 | | 11 | 6 | | 12 | 3 | | 13 | 38 | | 14 | 3 | | 15 | 5 | | 16 | 7 | | 17 | 5 | | 18 | 33 | | 19 | 3 | | 20 | 12 | | 21 | 1 | | 22 | 3 | | 23 | 7 | | 24 | 8 | | 25 | 36 | | 26 | 6 | | 27 | 6 | | 28 | 4 | | 29 | 1 | | 30 | 5 | | 31 | 1 | | 32 | 5 | | 33 | 10 | | 34 | 11 | | 35 | 5 | | 36 | 15 | | 37 | 8 | | 38 | 9 | | 39 | 5 | | 40 | 4 | | 41 | 6 | | 42 | 31 | | 43 | 5 | | 44 | 4 | | 45 | 12 | | 46 | 3 | | 47 | 5 | | 48 | 39 | | 49 | 4 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 167 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 221 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 223 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1220 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.018852459016393444 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.000819672131147541 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 223 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 223 | | mean | 6.71 | | std | 4.39 | | cv | 0.654 | | sampleLengths | | 0 | 18 | | 1 | 6 | | 2 | 10 | | 3 | 7 | | 4 | 1 | | 5 | 1 | | 6 | 13 | | 7 | 8 | | 8 | 5 | | 9 | 10 | | 10 | 20 | | 11 | 7 | | 12 | 12 | | 13 | 12 | | 14 | 18 | | 15 | 8 | | 16 | 10 | | 17 | 12 | | 18 | 5 | | 19 | 11 | | 20 | 17 | | 21 | 5 | | 22 | 2 | | 23 | 2 | | 24 | 7 | | 25 | 5 | | 26 | 6 | | 27 | 3 | | 28 | 13 | | 29 | 25 | | 30 | 3 | | 31 | 5 | | 32 | 7 | | 33 | 5 | | 34 | 8 | | 35 | 16 | | 36 | 9 | | 37 | 3 | | 38 | 12 | | 39 | 1 | | 40 | 3 | | 41 | 7 | | 42 | 5 | | 43 | 3 | | 44 | 6 | | 45 | 10 | | 46 | 12 | | 47 | 8 | | 48 | 6 | | 49 | 6 |
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| 52.77% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3542600896860987 | | totalSentences | 223 | | uniqueOpeners | 79 | |
| 70.42% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 142 | | matches | | 0 | "Somewhere deeper in the market," | | 1 | "Then something behind him placed" | | 2 | "Only the compass remained." |
| | ratio | 0.021 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 142 | | matches | | 0 | "Her leather watch ticked against" | | 1 | "His feet rested on the" | | 2 | "His throat showed no bruising." | | 3 | "His eyes stared at the" | | 4 | "He had worked with her" | | 5 | "It measured no wider than" | | 6 | "She held her hand near" | | 7 | "Their flames bent towards the" | | 8 | "She examined the token without" | | 9 | "She pointed to it." | | 10 | "It trembled, each grain shifting" | | 11 | "She looked at the man’s" | | 12 | "She knelt and slid a" | | 13 | "She pulled back." | | 14 | "It smelled of wet stone" | | 15 | "His voice came from beneath" | | 16 | "Its handle had no hand" | | 17 | "She watched the corpse." | | 18 | "It formed a human outline," | | 19 | "She reached down and prised" |
| | ratio | 0.204 | |
| 12.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 127 | | totalSentences | 142 | | matches | | 0 | "The dead man’s hand clenched" | | 1 | "Quinn stopped with one polished" | | 2 | "Her leather watch ticked against" | | 3 | "A small sound, neat and" | | 4 | "The constable swallowed and retreated." | | 5 | "The abandoned Tube platform beneath" | | 6 | "Hundreds of lamps hung from" | | 7 | "Stalls crowded the old ticket" | | 8 | "A woman with silver teeth" | | 9 | "A bent man displayed knives" | | 10 | "Quinn crouched beside the body." | | 11 | "The victim wore a charcoal" | | 12 | "His feet rested on the" | | 13 | "His throat showed no bruising." | | 14 | "His eyes stared at the" | | 15 | "Quinn looked up." | | 16 | "Vale stood beside a broken" | | 17 | "He had worked with her" | | 18 | "Quinn ignored him and examined" | | 19 | "The man’s fingers locked around" |
| | ratio | 0.894 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 142 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 1 | | matches | | 0 | "Old tiles curved into a tunnel that ended in brick, though the Underground maps aboveground showed no station beneath it." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 2 | | fancyTags | | 0 | "he whispered (whisper)" | | 1 | "he breathed (breathe)" |
| | dialogueSentences | 69 | | tagDensity | 0.159 | | leniency | 0.319 | | rawRatio | 0.182 | | effectiveRatio | 0.058 | |