| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva’s voice tightened slightly [slightly]" |
| | dialogueSentences | 30 | | tagDensity | 0.467 | | leniency | 0.933 | | rawRatio | 0.071 | | effectiveRatio | 0.067 | |
| 76.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1044 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | |
| | highlights | | 0 | "slightly" | | 1 | "deliberately" | | 2 | "carefully" |
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| 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) | |
| 56.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1044 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "echoing" | | 1 | "unsettled" | | 2 | "scanned" | | 3 | "etched" | | 4 | "standard" | | 5 | "oppressive" | | 6 | "gloom" | | 7 | "glint" | | 8 | "apprehension" |
| |
| 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 | 109 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 109 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 125 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1041 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 799 | | uniqueNames | 12 | | maxNameDensity | 1.13 | | worstName | "Davies" | | maxWindowNameDensity | 3 | | worstWindowName | "Davies" | | discoveredNames | | Harlow | 1 | | Quinn | 8 | | Tube | 2 | | Davies | 9 | | Clean | 1 | | Veil | 2 | | Compass | 1 | | Kowalski | 1 | | Eva | 4 | | Morris | 1 | | Market | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Davies" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Morris" | | 6 | "Market" |
| | places | | | globalScore | 0.937 | | windowScore | 0.667 | |
| 69.35% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 2 | | matches | | 0 | "tasted like damp concrete and something e" | | 1 | "ng, eager, and visibly unsettled by the lo" |
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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 | 1041 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 125 | | matches | | 0 | "understood that something" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 22.15 | | std | 13.1 | | cv | 0.591 | | sampleLengths | | 0 | 67 | | 1 | 38 | | 2 | 28 | | 3 | 28 | | 4 | 49 | | 5 | 1 | | 6 | 17 | | 7 | 10 | | 8 | 13 | | 9 | 48 | | 10 | 14 | | 11 | 12 | | 12 | 23 | | 13 | 26 | | 14 | 23 | | 15 | 22 | | 16 | 25 | | 17 | 10 | | 18 | 6 | | 19 | 24 | | 20 | 31 | | 21 | 11 | | 22 | 25 | | 23 | 26 | | 24 | 2 | | 25 | 16 | | 26 | 25 | | 27 | 10 | | 28 | 21 | | 29 | 19 | | 30 | 19 | | 31 | 24 | | 32 | 25 | | 33 | 44 | | 34 | 29 | | 35 | 4 | | 36 | 6 | | 37 | 27 | | 38 | 19 | | 39 | 17 | | 40 | 23 | | 41 | 41 | | 42 | 35 | | 43 | 4 | | 44 | 23 | | 45 | 10 | | 46 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 109 | | matches | | |
| 46.15% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 130 | | matches | | 0 | "were playing" | | 1 | "was watching" | | 2 | "was reaching" |
| |
| 74.29% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 125 | | ratio | 0.024 | | matches | | 0 | "The air tasted like damp concrete and something else—something acrid, faintly metallic, that clung to the back of Harlow Quinn’s throat." | | 1 | "Middle-aged, dressed in a sharp suit—the kind that cost more than Quinn’s yearly salary." | | 2 | "The face was intricately etched with symbols—protective sigils, she recognized vaguely from a briefing she’d skimmed years ago." |
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| 97.03% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 331 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.04229607250755287 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.021148036253776436 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 125 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 125 | | mean | 8.33 | | std | 5.55 | | cv | 0.666 | | sampleLengths | | 0 | 21 | | 1 | 19 | | 2 | 27 | | 3 | 13 | | 4 | 8 | | 5 | 12 | | 6 | 5 | | 7 | 9 | | 8 | 14 | | 9 | 3 | | 10 | 1 | | 11 | 1 | | 12 | 12 | | 13 | 10 | | 14 | 6 | | 15 | 8 | | 16 | 9 | | 17 | 9 | | 18 | 4 | | 19 | 3 | | 20 | 8 | | 21 | 8 | | 22 | 1 | | 23 | 7 | | 24 | 5 | | 25 | 2 | | 26 | 2 | | 27 | 1 | | 28 | 10 | | 29 | 8 | | 30 | 5 | | 31 | 12 | | 32 | 5 | | 33 | 2 | | 34 | 8 | | 35 | 18 | | 36 | 3 | | 37 | 14 | | 38 | 9 | | 39 | 3 | | 40 | 15 | | 41 | 2 | | 42 | 2 | | 43 | 4 | | 44 | 12 | | 45 | 14 | | 46 | 3 | | 47 | 20 | | 48 | 7 | | 49 | 15 |
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| 63.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.44 | | totalSentences | 125 | | uniqueOpeners | 55 | |
| 37.88% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 88 | | matches | | 0 | "Perhaps her eyes were playing" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 88 | | matches | | 0 | "She pulled her worn leather" | | 1 | "He was young, eager, and" | | 2 | "She scanned the area." | | 3 | "She smelled it again, that" | | 4 | "she asked, her voice low," | | 5 | "She noticed a small, brass" | | 6 | "she asked, carefully picking it" | | 7 | "She pointed to the man’s" | | 8 | "She pocketed the compass" | | 9 | "It felt strangely cold against" | | 10 | "She needed someone who understood" | | 11 | "It took a few rings" | | 12 | "Her voice was instantly alert" | | 13 | "She hung up, watching as" | | 14 | "She returned to the body," | | 15 | "His face was frozen in" | | 16 | "Her gaze drifted toward the" | | 17 | "She knelt down, squinting in" | | 18 | "She carefully plucked it up" | | 19 | "She didn't elaborate." |
| | ratio | 0.273 | |
| 96.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 88 | | matches | | 0 | "The air tasted like damp" | | 1 | "She pulled her worn leather" | | 2 | "The abandoned Tube station was" | | 3 | "Tracks disappeared into inky tunnels" | | 4 | "A tableau of unsettling stillness." | | 5 | "A man lay sprawled near" | | 6 | "Sergeant Davies said, his voice" | | 7 | "He was young, eager, and" | | 8 | "Quinn crouched beside the body," | | 9 | "She scanned the area." | | 10 | "She smelled it again, that" | | 11 | "she asked, her voice low," | | 12 | "Davies replied, adjusting his cap" | | 13 | "She noticed a small, brass" | | 14 | "The face was intricately etched" | | 15 | "A Veil Compass." | | 16 | "she asked, carefully picking it" | | 17 | "Davies said, gesturing towards the" | | 18 | "The compass needle spun erratically," | | 19 | "Quinn murmured, more to herself" |
| | ratio | 0.727 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 2 | | matches | | 0 | "Middle-aged, dressed in a sharp suit—the kind that cost more than Quinn’s yearly salary." | | 1 | "A place that reminded her, with a jolt of icy dread, of the circumstances surrounding her partner, DS Morris’s, death three years ago." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 5 | | matches | | 0 | "Sergeant Davies said, his voice echoing slightly" | | 1 | "she asked, her voice low, deliberately calm" | | 2 | "Quinn murmured, more to herself than to Davies" | | 3 | "Davies said, his voice a touch less enthusiastic" | | 4 | "Quinn said, a ghost of a smile playing on her lips" |
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| 83.33% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn murmured (murmur)" | | 1 | "she stated (state)" |
| | dialogueSentences | 30 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.2 | | effectiveRatio | 0.133 | |