| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1421 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 50.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1421 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "electric" | | 1 | "warmth" | | 2 | "silence" | | 3 | "tracing" | | 4 | "unspoken" | | 5 | "weight" | | 6 | "familiar" |
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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 | 117 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 117 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1421 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1258 | | uniqueNames | 19 | | maxNameDensity | 0.79 | | worstName | "Silas" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Welsh | 1 | | English | 1 | | Carter | 1 | | Yu-Fei | 1 | | Golden | 1 | | Empress | 1 | | Hendrick | 1 | | Cardiff | 2 | | Silas | 10 | | Spymaster | 3 | | Evan | 1 | | Pre-Law | 1 | | University | 1 | | Rory | 4 | | Prague | 1 | | Aurora | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Yu-Fei" | | 4 | "Hendrick" | | 5 | "Silas" | | 6 | "Evan" | | 7 | "Rory" | | 8 | "Aurora" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "Prague" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 87.50% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like they’d walked out of the war" | | 1 | "tasted like ash" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1421 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 36.44 | | std | 25.9 | | cv | 0.711 | | sampleLengths | | 0 | 94 | | 1 | 72 | | 2 | 84 | | 3 | 33 | | 4 | 71 | | 5 | 30 | | 6 | 13 | | 7 | 18 | | 8 | 63 | | 9 | 31 | | 10 | 75 | | 11 | 11 | | 12 | 96 | | 13 | 25 | | 14 | 15 | | 15 | 3 | | 16 | 28 | | 17 | 49 | | 18 | 18 | | 19 | 31 | | 20 | 12 | | 21 | 53 | | 22 | 16 | | 23 | 7 | | 24 | 17 | | 25 | 46 | | 26 | 3 | | 27 | 35 | | 28 | 18 | | 29 | 13 | | 30 | 40 | | 31 | 40 | | 32 | 22 | | 33 | 13 | | 34 | 10 | | 35 | 28 | | 36 | 53 | | 37 | 73 | | 38 | 62 |
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| 93.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 117 | | matches | | 0 | "been untroubled" | | 1 | "was gone" | | 2 | "were made" | | 3 | "been lost" |
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| 56.32% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 232 | | matches | | 0 | "was letting" | | 1 | "was trying" | | 2 | "was trying" | | 3 | "was carrying" | | 4 | "was waiting" |
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| 97.86% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 127 | | ratio | 0.016 | | matches | | 0 | "It wasn’t just a year of missing work; it was a year of silence." | | 1 | "The years hadn’t passed them by; they had walked right over them." |
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| 81.53% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 180 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.06111111111111111 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.011111111111111112 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 11.19 | | std | 7.31 | | cv | 0.653 | | sampleLengths | | 0 | 24 | | 1 | 22 | | 2 | 27 | | 3 | 21 | | 4 | 6 | | 5 | 32 | | 6 | 4 | | 7 | 20 | | 8 | 10 | | 9 | 21 | | 10 | 2 | | 11 | 20 | | 12 | 41 | | 13 | 1 | | 14 | 6 | | 15 | 14 | | 16 | 12 | | 17 | 11 | | 18 | 23 | | 19 | 10 | | 20 | 11 | | 21 | 16 | | 22 | 24 | | 23 | 6 | | 24 | 7 | | 25 | 6 | | 26 | 5 | | 27 | 8 | | 28 | 5 | | 29 | 5 | | 30 | 8 | | 31 | 7 | | 32 | 23 | | 33 | 5 | | 34 | 15 | | 35 | 12 | | 36 | 14 | | 37 | 5 | | 38 | 6 | | 39 | 14 | | 40 | 21 | | 41 | 8 | | 42 | 14 | | 43 | 12 | | 44 | 11 | | 45 | 8 | | 46 | 24 | | 47 | 21 | | 48 | 3 | | 49 | 21 |
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| 32.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.2755905511811024 | | totalSentences | 127 | | uniqueOpeners | 35 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 100 | | matches | | 0 | "Just the delivery girl for" | | 1 | "Then he set the bottle" | | 2 | "Instead, he had become a" |
| | ratio | 0.03 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 56 | | totalSentences | 100 | | matches | | 0 | "She kept her head down," | | 1 | "She hadn’t meant to come" | | 2 | "She lived above, in the" | | 3 | "She needed a distraction." | | 4 | "She needed to see the" | | 5 | "She needed to be a" | | 6 | "She moved to the end" | | 7 | "His hazel eyes caught her" | | 8 | "He stopped, the bottle of" | | 9 | "He wore his silver signet" | | 10 | "He looked at her for" | | 11 | "His voice was gravel and" | | 12 | "It was a coat she’d" | | 13 | "It felt different now, heavier." | | 14 | "Her voice sounded flat to" | | 15 | "She wasn’t sure why she" | | 16 | "She hadn’t told Silas about" | | 17 | "They were landlord and tenant." | | 18 | "He wiped his hands on" | | 19 | "It wasn’t just a year" |
| | ratio | 0.56 | |
| 25.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 100 | | matches | | 0 | "The green neon of the" | | 1 | "Aurora pushed open the door," | | 2 | "The warmth of the bar" | | 3 | "She kept her head down," | | 4 | "She hadn’t meant to come" | | 5 | "She lived above, in the" | | 6 | "She needed a distraction." | | 7 | "She needed to see the" | | 8 | "She needed to be a" | | 9 | "The bar was dim, lit" | | 10 | "She moved to the end" | | 11 | "That’s what her friends called" | | 12 | "Here, with no friends to" | | 13 | "A shadow detached itself from" | | 14 | "Silas moved with a slight" | | 15 | "His hazel eyes caught her" | | 16 | "He stopped, the bottle of" | | 17 | "He wore his silver signet" | | 18 | "He looked at her for" | | 19 | "His voice was gravel and" |
| | ratio | 0.87 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 100 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 6 | | matches | | 0 | "Black-and-white photographs of men who looked like they’d walked out of the war and never quite learned to speak English." | | 1 | "Just the delivery girl for Yu-Fei’s Golden Empress who kept to herself." | | 2 | "He looked at her for a long moment, the quiet authority of a man who had seen too much sitting heavy in his posture." | | 3 | "He saw the brightness in her blue eyes that had once been untroubled, the intelligence that had driven her through Pre-Law at Cardiff University." | | 4 | "He saw the tightness in her shoulders, the way she held her left wrist as if it were a broken hinge." | | 5 | "But for ten minutes, they were just two old friends who had seen too much, sitting in a bar that remembered everything." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 3 | | matches | | 0 | "she said, though the excuse tasted like ash" | | 1 | "He leaned, the wood creaking under his weight" | | 2 | "she said, the steel returning to her voice, a sharp, defensive edge" |
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| 83.33% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 2 | | fancyTags | | 0 | "she admitted (admit)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 27 | | tagDensity | 0.556 | | leniency | 1 | | rawRatio | 0.133 | | effectiveRatio | 0.133 | |