| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.18 | | leniency | 0.36 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 876 | | 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) | |
| 71.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 876 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "silk" | | 1 | "traced" | | 2 | "weight" |
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| 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 | 70 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 70 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 876 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 90.71% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 16 | | wordCount | 506 | | uniqueNames | 8 | | maxNameDensity | 1.19 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Kentish | 1 | | Town | 1 | | London | 1 | | Savile | 1 | | Row | 1 | | Quinn | 6 | | Davies | 4 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Davies" | | 2 | "Morris" |
| | places | | 0 | "Kentish" | | 1 | "Town" | | 2 | "London" |
| | globalScore | 0.907 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | 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 | 876 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 111 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 59 | | mean | 14.85 | | std | 14.35 | | cv | 0.966 | | sampleLengths | | 0 | 14 | | 1 | 47 | | 2 | 23 | | 3 | 9 | | 4 | 5 | | 5 | 44 | | 6 | 4 | | 7 | 53 | | 8 | 7 | | 9 | 3 | | 10 | 63 | | 11 | 33 | | 12 | 6 | | 13 | 2 | | 14 | 7 | | 15 | 7 | | 16 | 7 | | 17 | 5 | | 18 | 5 | | 19 | 24 | | 20 | 6 | | 21 | 1 | | 22 | 37 | | 23 | 11 | | 24 | 9 | | 25 | 7 | | 26 | 42 | | 27 | 5 | | 28 | 6 | | 29 | 14 | | 30 | 22 | | 31 | 3 | | 32 | 5 | | 33 | 29 | | 34 | 10 | | 35 | 30 | | 36 | 13 | | 37 | 11 | | 38 | 11 | | 39 | 3 | | 40 | 7 | | 41 | 3 | | 42 | 27 | | 43 | 34 | | 44 | 23 | | 45 | 3 | | 46 | 20 | | 47 | 4 | | 48 | 7 | | 49 | 31 |
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| 85.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 70 | | matches | | 0 | "was laced" | | 1 | "been ripped" | | 2 | "was rusted" | | 3 | "was drawn" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 78 | | matches | | 0 | "was wearing" | | 1 | "was humming" | | 2 | "wasn't pointing" | | 3 | "was pointing" | | 4 | "was spinning" | | 5 | "was standing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 111 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 507 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.027613412228796843 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0019723865877712033 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 7.89 | | std | 5.48 | | cv | 0.695 | | sampleLengths | | 0 | 14 | | 1 | 10 | | 2 | 9 | | 3 | 9 | | 4 | 9 | | 5 | 10 | | 6 | 13 | | 7 | 10 | | 8 | 9 | | 9 | 5 | | 10 | 12 | | 11 | 4 | | 12 | 3 | | 13 | 1 | | 14 | 24 | | 15 | 4 | | 16 | 14 | | 17 | 14 | | 18 | 2 | | 19 | 3 | | 20 | 5 | | 21 | 15 | | 22 | 7 | | 23 | 3 | | 24 | 3 | | 25 | 9 | | 26 | 22 | | 27 | 21 | | 28 | 8 | | 29 | 10 | | 30 | 8 | | 31 | 5 | | 32 | 2 | | 33 | 8 | | 34 | 4 | | 35 | 2 | | 36 | 2 | | 37 | 7 | | 38 | 7 | | 39 | 7 | | 40 | 5 | | 41 | 5 | | 42 | 11 | | 43 | 13 | | 44 | 6 | | 45 | 1 | | 46 | 12 | | 47 | 25 | | 48 | 11 | | 49 | 9 |
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| 64.56% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.44144144144144143 | | totalSentences | 111 | | uniqueOpeners | 49 | |
| 55.56% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 60 | | matches | | 0 | "Just gravel and dust and" |
| | ratio | 0.017 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 60 | | matches | | 0 | "They had just been precise" | | 1 | "She tilted the man's foot" | | 2 | "She swept her torch across" | | 3 | "She crouched again, hovering her" | | 4 | "She gestured to the gold" | | 5 | "It wasn't graffiti." | | 6 | "She traced the air an" | | 7 | "She could see her breath." | | 8 | "She shone her torch on" | | 9 | "He shifted his weight from" | | 10 | "he asked, trying to make" | | 11 | "She looked at the spiral" | | 12 | "She pulled her coat tighter" | | 13 | "She felt the weight in" | | 14 | "It had been dead weight" | | 15 | "She pulled it out, shielding" | | 16 | "It was pointing at the" | | 17 | "She shoved it back into" | | 18 | "She pulled it out again." | | 19 | "It spun until it snapped" |
| | ratio | 0.333 | |
| 51.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 49 | | totalSentences | 60 | | matches | | 0 | "The dead man on the" | | 1 | "Quinn crouched beside the body," | | 2 | "The left brogue sat snug" | | 3 | "The right brogue was laced" | | 4 | "They had just been precise" | | 5 | "Davies said, angling his torch" | | 6 | "She tilted the man's foot" | | 7 | "The leather was clean." | | 8 | "She swept her torch across" | | 9 | "The gravel lay thick and" | | 10 | "South Kentish Town." | | 11 | "The lifts had been ripped" | | 12 | "The only way in or" | | 13 | "The tiled walls were stained" | | 14 | "The air tasted of copper" | | 15 | "Quinn stood and walked a" | | 16 | "Charcoal wool suit, Savile Row" | | 17 | "The man's face was peaceful." | | 18 | "She crouched again, hovering her" | | 19 | "A pale strip of skin," |
| | ratio | 0.817 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 60 | | matches | | 0 | "Whoever had dressed him hadn't" | | 1 | "Now it was humming against" |
| | ratio | 0.033 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 19 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 1 | | matches | | 0 | "he asked, trying to make conversation" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 50 | | tagDensity | 0.06 | | leniency | 0.12 | | rawRatio | 0 | | effectiveRatio | 0 | |