| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.488 | | leniency | 0.976 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1165 | | 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) | |
| 65.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1165 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "chill" | | 1 | "familiar" | | 2 | "etched" | | 3 | "trembled" | | 4 | "pulse" | | 5 | "warmth" | | 6 | "flickered" | | 7 | "mechanical" |
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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 | 85 | | matches | (empty) | |
| 92.44% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1164 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 49.75% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 798 | | uniqueNames | 13 | | maxNameDensity | 2.01 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Quinn | 16 | | Tube | 2 | | Camden | 1 | | Eva | 14 | | Kowalsci | 1 | | Morris | 3 | | Veil | 1 | | Compass | 2 | | Aurora | 1 | | Oxford | 1 | | Quin | 5 | | Simple | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Eva" | | 2 | "Kowalsci" | | 3 | "Morris" | | 4 | "Aurora" | | 5 | "Quin" |
| | places | | 0 | "London" | | 1 | "Compass" | | 2 | "Oxford" |
| | globalScore | 0.497 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.859 | | wordCount | 1164 | | matches | | 0 | "not pointing it at the man, but over the track itself" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 30.63 | | std | 21.57 | | cv | 0.704 | | sampleLengths | | 0 | 78 | | 1 | 65 | | 2 | 27 | | 3 | 70 | | 4 | 35 | | 5 | 7 | | 6 | 57 | | 7 | 85 | | 8 | 35 | | 9 | 41 | | 10 | 13 | | 11 | 8 | | 12 | 59 | | 13 | 9 | | 14 | 24 | | 15 | 40 | | 16 | 48 | | 17 | 15 | | 18 | 28 | | 19 | 33 | | 20 | 56 | | 21 | 17 | | 22 | 31 | | 23 | 29 | | 24 | 23 | | 25 | 36 | | 26 | 5 | | 27 | 19 | | 28 | 18 | | 29 | 9 | | 30 | 21 | | 31 | 6 | | 32 | 15 | | 33 | 8 | | 34 | 35 | | 35 | 50 | | 36 | 4 | | 37 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 145 | | matches | (empty) | |
| 88.95% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 106 | | ratio | 0.019 | | matches | | 0 | "The rain in London does not wash the city clean; it soaks it into the bone." | | 1 | "Morris had died in a loop like that; a fall that wasn't physics." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 800 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.01875 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 10.98 | | std | 6.64 | | cv | 0.605 | | sampleLengths | | 0 | 16 | | 1 | 31 | | 2 | 20 | | 3 | 2 | | 4 | 4 | | 5 | 5 | | 6 | 20 | | 7 | 28 | | 8 | 17 | | 9 | 17 | | 10 | 10 | | 11 | 16 | | 12 | 5 | | 13 | 13 | | 14 | 13 | | 15 | 18 | | 16 | 5 | | 17 | 15 | | 18 | 20 | | 19 | 2 | | 20 | 3 | | 21 | 2 | | 22 | 12 | | 23 | 21 | | 24 | 24 | | 25 | 8 | | 26 | 17 | | 27 | 14 | | 28 | 11 | | 29 | 11 | | 30 | 9 | | 31 | 15 | | 32 | 9 | | 33 | 6 | | 34 | 20 | | 35 | 15 | | 36 | 19 | | 37 | 5 | | 38 | 2 | | 39 | 5 | | 40 | 4 | | 41 | 4 | | 42 | 8 | | 43 | 9 | | 44 | 10 | | 45 | 30 | | 46 | 10 | | 47 | 4 | | 48 | 5 | | 49 | 16 |
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| 41.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3113207547169811 | | totalSentences | 106 | | uniqueOpeners | 33 | |
| 47.62% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 70 | | matches | | | ratio | 0.014 | |
| 54.29% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 70 | | matches | | 0 | "Her worn leather watch on" | | 1 | "She turned to find Eva" | | 2 | "She didn't repeat the clichés." | | 3 | "She moved to the body," | | 4 | "Her knuckles creaked." | | 5 | "She pushed her glasses up" | | 6 | "She had buried DS Morris" | | 7 | "She stood, her salt-and-pepper hair" | | 8 | "She reached into her coat" | | 9 | "She held it out, not" | | 10 | "It didn't point at the" | | 11 | "She was Aurora's childhood friend," | | 12 | "She didn't believe in magic." | | 13 | "She moved with precision, never" | | 14 | "She reached out with a" | | 15 | "She felt the smooth metal" | | 16 | "She reached down and picked" | | 17 | "It was dry to the" | | 18 | "Her voice was flat, the" | | 19 | "She looked at the past" |
| | ratio | 0.414 | |
| 2.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 70 | | matches | | 0 | "The rain in London does" | | 1 | "Harlow Quinn stepped off the" | | 2 | "Her worn leather watch on" | | 3 | "The clock was irrelevant." | | 4 | "The tape, however, was not." | | 5 | "She turned to find Eva" | | 6 | "Eva pulled her red curls" | | 7 | "The green in her eyes" | | 8 | "Eva said, her voice thin" | | 9 | "She didn't repeat the clichés." | | 10 | "Morris had died in a" | | 11 | "Quinn’s jawline set, the muscle" | | 12 | "She moved to the body," | | 13 | "Eva leaned over him, the" | | 14 | "Her knuckles creaked." | | 15 | "Eva murmured, tapping a gloved" | | 16 | "She pushed her glasses up" | | 17 | "Quinn felt the cold settle" | | 18 | "She had buried DS Morris" | | 19 | "She stood, her salt-and-pepper hair" |
| | ratio | 0.914 | |
| 71.43% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 70 | | matches | | 0 | "If something took the flesh" |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 1 | | matches | | 0 | "Harlow Quinn stepped off the wet platform of the abandoned Tube beneath Camden and immediately shook the water from her sloped shoulders, keeping her military b…" |
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| 50.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 3 | | matches | | 0 | "Eva said, her voice thin over the hum of the ventilation shaft" | | 1 | "Eve said, the shape of the word stuck in her throat" | | 2 | "Eva said, her voice gaining confidence" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 2 | | fancyTags | | 0 | "Eva murmured (murmur)" | | 1 | "Eva whispered (whisper)" |
| | dialogueSentences | 41 | | tagDensity | 0.39 | | leniency | 0.78 | | rawRatio | 0.125 | | effectiveRatio | 0.098 | |