| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.345 | | leniency | 0.69 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 894 | | 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) | |
| 83.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 894 | | totalAiIsms | 3 | | found | | | highlights | | |
| 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 | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 82 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 894 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 641 | | uniqueNames | 13 | | maxNameDensity | 1.25 | | worstName | "Doyle" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Marlon | 1 | | Doyle | 8 | | Owen | 2 | | Pike | 6 | | Tube | 1 | | Veil | 2 | | Market | 2 | | Shade | 1 | | Camden | 1 | | Kowalski | 2 | | Eva | 8 | | Wapping | 1 | | Morris | 1 |
| | persons | | 0 | "Marlon" | | 1 | "Doyle" | | 2 | "Owen" | | 3 | "Pike" | | 4 | "Market" | | 5 | "Kowalski" | | 6 | "Eva" | | 7 | "Morris" |
| | places | | | globalScore | 0.876 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | 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 | 894 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 21.29 | | std | 19.2 | | cv | 0.902 | | sampleLengths | | 0 | 23 | | 1 | 31 | | 2 | 60 | | 3 | 57 | | 4 | 4 | | 5 | 16 | | 6 | 35 | | 7 | 37 | | 8 | 6 | | 9 | 7 | | 10 | 4 | | 11 | 6 | | 12 | 1 | | 13 | 57 | | 14 | 18 | | 15 | 4 | | 16 | 30 | | 17 | 4 | | 18 | 16 | | 19 | 25 | | 20 | 3 | | 21 | 15 | | 22 | 18 | | 23 | 7 | | 24 | 12 | | 25 | 60 | | 26 | 6 | | 27 | 28 | | 28 | 6 | | 29 | 73 | | 30 | 8 | | 31 | 23 | | 32 | 3 | | 33 | 19 | | 34 | 11 | | 35 | 30 | | 36 | 10 | | 37 | 6 | | 38 | 12 | | 39 | 44 | | 40 | 55 | | 41 | 4 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 115 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 101 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 647 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.02936630602782071 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0030911901081916537 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 8.85 | | std | 6.21 | | cv | 0.702 | | sampleLengths | | 0 | 10 | | 1 | 13 | | 2 | 20 | | 3 | 9 | | 4 | 2 | | 5 | 10 | | 6 | 5 | | 7 | 23 | | 8 | 5 | | 9 | 17 | | 10 | 7 | | 11 | 19 | | 12 | 20 | | 13 | 6 | | 14 | 5 | | 15 | 4 | | 16 | 13 | | 17 | 3 | | 18 | 35 | | 19 | 5 | | 20 | 14 | | 21 | 4 | | 22 | 1 | | 23 | 1 | | 24 | 9 | | 25 | 3 | | 26 | 6 | | 27 | 7 | | 28 | 4 | | 29 | 6 | | 30 | 1 | | 31 | 11 | | 32 | 15 | | 33 | 6 | | 34 | 19 | | 35 | 6 | | 36 | 2 | | 37 | 3 | | 38 | 2 | | 39 | 11 | | 40 | 4 | | 41 | 17 | | 42 | 13 | | 43 | 2 | | 44 | 2 | | 45 | 3 | | 46 | 13 | | 47 | 25 | | 48 | 3 | | 49 | 3 |
| |
| 73.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.46534653465346537 | | totalSentences | 101 | | uniqueOpeners | 47 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 94.63% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 67 | | matches | | 0 | "I knelt on the gritty" | | 1 | "He had already decided how" | | 2 | "It tick-tocked left, then locked" | | 3 | "I stayed crouched." | | 4 | "I leaned over Owen Pike." | | 5 | "I rose and turned to" | | 6 | "She tucked a curl of" | | 7 | "Her freckled face had gone" | | 8 | "I had read her file" | | 9 | "She pointed a slender finger" | | 10 | "I crouched again." | | 11 | "I unfolded the stiff fingers." | | 12 | "You didn't get past the" | | 13 | "I turned Pike's right hand" | | 14 | "My gut did a flip-flop." | | 15 | "I had seen that identical" | | 16 | "I stood, the compass tight" | | 17 | "I held the compass out" | | 18 | "It pointed at the dead" | | 19 | "I met his eyes" |
| | ratio | 0.313 | |
| 34.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 67 | | matches | | 0 | "The dead man's fingers had" | | 1 | "I knelt on the gritty" | | 2 | "He had already decided how" | | 3 | "The small brass compass came" | | 4 | "The casing carried a patina" | | 5 | "The needle didn't settle north." | | 6 | "It tick-tocked left, then locked" | | 7 | "Stalls draped in midnight-blue silk" | | 8 | "The market moved every full" | | 9 | "Tonight it had chosen Camden." | | 10 | "Doyle's boots crunched on the" | | 11 | "I stayed crouched." | | 12 | "I leaned over Owen Pike." | | 13 | "The back of his skull" | | 14 | "Copper stung my nostrils." | | 15 | "I rose and turned to" | | 16 | "A scuff of shoe on" | | 17 | "Eva Kowalski stood a few" | | 18 | "She tucked a curl of" | | 19 | "Her freckled face had gone" |
| | ratio | 0.851 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 2 | | matches | | 0 | "Behind me, DS Marlon Doyle muttered into his radio, telling the SOCOs to stop arsing about and get down here." | | 1 | "The casing carried a patina of verdigris, the face etched with protective sigils that crawled across the metal under the flickering emergency lights." |
| |
| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva's voice shook, but her words stayed precise" |
| |
| 81.03% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "I agreed (agree)" | | 1 | "Eva whispered (whisper)" |
| | dialogueSentences | 29 | | tagDensity | 0.276 | | leniency | 0.552 | | rawRatio | 0.25 | | effectiveRatio | 0.138 | |