| 85.71% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 2 | | adverbTags | | 0 | "a low whisper trembling slightly [slightly]" | | 1 | "Eva spoke quickly [quickly]" |
| | dialogueSentences | 35 | | tagDensity | 0.343 | | leniency | 0.686 | | rawRatio | 0.167 | | effectiveRatio | 0.114 | |
| 80.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1265 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "completely" | | 1 | "slightly" | | 2 | "softly" | | 3 | "lazily" | | 4 | "quickly" |
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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) | |
| 40.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1265 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "weight" | | 1 | "oppressive" | | 2 | "stark" | | 3 | "eyebrow" | | 4 | "measured" | | 5 | "echoing" | | 6 | "silence" | | 7 | "rhythmic" | | 8 | "standard" | | 9 | "flickered" | | 10 | "unreadable" | | 11 | "whisper" | | 12 | "etched" | | 13 | "crystalline" | | 14 | "shimmered" |
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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 | 93 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | 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 | 1261 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 59.09% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 880 | | uniqueNames | 8 | | maxNameDensity | 1.82 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 2 | | Quinn | 16 | | Miller | 7 | | Eva | 6 | | Kowalski | 2 | | North | 1 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 0.591 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.793 | | wordCount | 1261 | | matches | | 0 | "No other footprints but" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 31.53 | | std | 25.22 | | cv | 0.8 | | sampleLengths | | 0 | 77 | | 1 | 19 | | 2 | 9 | | 3 | 64 | | 4 | 66 | | 5 | 60 | | 6 | 2 | | 7 | 45 | | 8 | 8 | | 9 | 30 | | 10 | 9 | | 11 | 16 | | 12 | 4 | | 13 | 41 | | 14 | 42 | | 15 | 14 | | 16 | 14 | | 17 | 98 | | 18 | 10 | | 19 | 9 | | 20 | 46 | | 21 | 17 | | 22 | 91 | | 23 | 21 | | 24 | 17 | | 25 | 1 | | 26 | 14 | | 27 | 12 | | 28 | 26 | | 29 | 44 | | 30 | 26 | | 31 | 37 | | 32 | 22 | | 33 | 65 | | 34 | 33 | | 35 | 59 | | 36 | 2 | | 37 | 40 | | 38 | 1 | | 39 | 50 |
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| 75.08% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 93 | | matches | | 0 | "been ripped" | | 1 | "was crouched" | | 2 | "was pulled" | | 3 | "were curled" | | 4 | "was made" | | 5 | "was etched" | | 6 | "been replaced" | | 7 | "been found" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 139 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 115 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 884 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.02828054298642534 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.011312217194570135 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 10.97 | | std | 8.33 | | cv | 0.76 | | sampleLengths | | 0 | 20 | | 1 | 9 | | 2 | 6 | | 3 | 20 | | 4 | 22 | | 5 | 19 | | 6 | 9 | | 7 | 4 | | 8 | 2 | | 9 | 5 | | 10 | 14 | | 11 | 20 | | 12 | 19 | | 13 | 18 | | 14 | 32 | | 15 | 16 | | 16 | 14 | | 17 | 11 | | 18 | 5 | | 19 | 5 | | 20 | 25 | | 21 | 2 | | 22 | 11 | | 23 | 34 | | 24 | 6 | | 25 | 2 | | 26 | 26 | | 27 | 4 | | 28 | 7 | | 29 | 2 | | 30 | 12 | | 31 | 3 | | 32 | 1 | | 33 | 4 | | 34 | 41 | | 35 | 3 | | 36 | 5 | | 37 | 10 | | 38 | 9 | | 39 | 15 | | 40 | 14 | | 41 | 10 | | 42 | 4 | | 43 | 6 | | 44 | 9 | | 45 | 4 | | 46 | 1 | | 47 | 2 | | 48 | 11 | | 49 | 13 |
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| 51.59% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3565217391304348 | | totalSentences | 115 | | uniqueOpeners | 41 | |
| 39.22% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 85 | | matches | | 0 | "Then she looked at Eva" |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 85 | | matches | | 0 | "It smelled of wet concrete," | | 1 | "She moved past him, her" | | 2 | "She looked completely out of" | | 3 | "He stood up, brushing dust" | | 4 | "Her voice was a low" | | 5 | "Her shoes were sensible flats," | | 6 | "She crouched, the worn leather" | | 7 | "His eyes were wide open," | | 8 | "It was his skin." | | 9 | "It was taut, stretched thin" | | 10 | "He looked like a mummy." | | 11 | "She gestured to the victim’s" | | 12 | "He knelt again, peering closer." | | 13 | "It was made of brass," | | 14 | "She picked it up with" | | 15 | "It spun lazily, then settled," | | 16 | "She watched Eva’s face." | | 17 | "It wasn’t white like a" | | 18 | "It shimmered, almost iridescent in" | | 19 | "She stood, slipping the compass" |
| | ratio | 0.294 | |
| 42.35% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 85 | | matches | | 0 | "The air in the abandoned" | | 1 | "It smelled of wet concrete," | | 2 | "Something faintly acrid, like burnt" | | 3 | "Detective Harlow Quinn’s boots crunched" | | 4 | "The beam of her torch" | | 5 | "A uniformed constable nodded at" | | 6 | "Quinn raised an eyebrow." | | 7 | "She moved past him, her" | | 8 | "The tracks had been ripped" | | 9 | "DS Miller, a young detective" | | 10 | "She looked completely out of" | | 11 | "Quinn stopped a few metres" | | 12 | "The silence was absolute, broken" | | 13 | "He stood up, brushing dust" | | 14 | "Quinn’s eyes flickered to the" | | 15 | "Miller gave her a dismissive" | | 16 | "Quinn approached the woman, her" | | 17 | "The woman flinched, tucking a" | | 18 | "A nervous habit." | | 19 | "Her voice was a low" |
| | ratio | 0.835 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 85 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 1 | | matches | | 0 | "Standing a few feet away, hugging a worn leather satchel to her chest, was a young woman with a riot of curly red hair and round glasses that magnified wide, gr…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 4 | | matches | | 0 | "Quinn said, her tone flat" | | 1 | "Quinn asked, not looking at Miller" | | 2 | "Eva spoke quickly, her words tumbling over each other" | | 3 | "Eva continued, her voice dropping to a conspiratorial hush" |
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| 64.29% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "Quinn pressed (press)" | | 1 | "Eva spoke quickly (speak)" | | 2 | "Eva continued (continue)" |
| | dialogueSentences | 35 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.6 | | effectiveRatio | 0.171 | |