| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 77.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1353 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slowly" | | 1 | "really" | | 2 | "quickly" | | 3 | "slightly" | | 4 | "carefully" | | 5 | "softly" |
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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) | |
| 63.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1353 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "chill" | | 1 | "sense of" | | 2 | "scanned" | | 3 | "etched" | | 4 | "magnetic" | | 5 | "dancing" | | 6 | "perfect" | | 7 | "implication" | | 8 | "etching" | | 9 | "standard" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 172 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 172 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 172 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1353 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 8 | | matches | | 0 | "Sometimes, Eva insisted, her voice gaining a fraction of strength." | | 1 | "Evidence, Eva said quickly." | | 2 | "It points to rifts, Eva whispered." | | 3 | "If there was a door, Quinn said, there would be a draft." | | 4 | "No, Eva said." | | 5 | "Entry requirement, Quinn said, her voice low." | | 6 | "He tried to leave, Eva said softly." | | 7 | "But you need it to find the others, Eva said, stepping forward." |
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| 24.35% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 68 | | wordCount | 1353 | | uniqueNames | 13 | | maxNameDensity | 2.51 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Detective | 2 | | Harlow | 1 | | Quinn | 34 | | London | 1 | | Kowalski | 2 | | British | 1 | | Museum | 1 | | Eva | 18 | | Morris | 2 | | Veil | 1 | | Market | 1 | | You | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Morris" | | 5 | "Market" | | 6 | "You" |
| | places | | | globalScore | 0.244 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 94 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like he was sleeping, save for the" |
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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 | 1353 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 172 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 28.79 | | std | 20.72 | | cv | 0.72 | | sampleLengths | | 0 | 79 | | 1 | 89 | | 2 | 20 | | 3 | 6 | | 4 | 75 | | 5 | 24 | | 6 | 44 | | 7 | 18 | | 8 | 5 | | 9 | 33 | | 10 | 61 | | 11 | 15 | | 12 | 17 | | 13 | 65 | | 14 | 14 | | 15 | 13 | | 16 | 36 | | 17 | 33 | | 18 | 7 | | 19 | 46 | | 20 | 5 | | 21 | 20 | | 22 | 41 | | 23 | 33 | | 24 | 6 | | 25 | 6 | | 26 | 40 | | 27 | 32 | | 28 | 24 | | 29 | 21 | | 30 | 45 | | 31 | 1 | | 32 | 24 | | 33 | 10 | | 34 | 19 | | 35 | 11 | | 36 | 47 | | 37 | 48 | | 38 | 22 | | 39 | 5 | | 40 | 29 | | 41 | 37 | | 42 | 27 | | 43 | 14 | | 44 | 44 | | 45 | 37 | | 46 | 5 |
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| 95.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 172 | | matches | | 0 | "was etched" | | 1 | "was swept" | | 2 | "were curled" | | 3 | "been forced" | | 4 | "was displaced" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 8 | | totalVerbs | 243 | | matches | | 0 | "were currently packing" | | 1 | "was sleeping" | | 2 | "was frizzing" | | 3 | "wasn't searching" | | 4 | "was hunting" | | 5 | "was vibrating" | | 6 | "was watching" | | 7 | "was rewriting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 172 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1356 | | adjectiveStacks | 1 | | stackExamples | | 0 | "thick, undisturbed gray fuzz" |
| | adverbCount | 37 | | adverbRatio | 0.02728613569321534 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.012536873156342183 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 172 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 172 | | mean | 7.87 | | std | 5.94 | | cv | 0.755 | | sampleLengths | | 0 | 30 | | 1 | 22 | | 2 | 1 | | 3 | 26 | | 4 | 11 | | 5 | 22 | | 6 | 10 | | 7 | 17 | | 8 | 4 | | 9 | 2 | | 10 | 4 | | 11 | 19 | | 12 | 10 | | 13 | 4 | | 14 | 6 | | 15 | 6 | | 16 | 9 | | 17 | 3 | | 18 | 16 | | 19 | 20 | | 20 | 27 | | 21 | 7 | | 22 | 11 | | 23 | 6 | | 24 | 19 | | 25 | 25 | | 26 | 14 | | 27 | 2 | | 28 | 2 | | 29 | 4 | | 30 | 1 | | 31 | 11 | | 32 | 4 | | 33 | 4 | | 34 | 2 | | 35 | 3 | | 36 | 3 | | 37 | 2 | | 38 | 4 | | 39 | 5 | | 40 | 10 | | 41 | 8 | | 42 | 3 | | 43 | 9 | | 44 | 11 | | 45 | 12 | | 46 | 3 | | 47 | 9 | | 48 | 2 | | 49 | 2 |
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| 40.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.3058823529411765 | | totalSentences | 170 | | uniqueOpeners | 52 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 143 | | matches | | 0 | "Sometimes, Eva insisted, her voice" | | 1 | "Sometimes the door opens where" | | 2 | "Just grout and grime." | | 3 | "Directly behind the victim's heels," | | 4 | "Just a gap in the" |
| | ratio | 0.035 | |
| 85.73% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 48 | | totalSentences | 143 | | matches | | 0 | "She checked her watch, the" | | 1 | "She crouched, the knees of" | | 2 | "He looked like he was" | | 3 | "They were a placeholder for" | | 4 | "You're looking at it wrong," | | 5 | "She turned her head slowly," | | 6 | "I'm looking at a dead" | | 7 | "It's not a closed station," | | 8 | "It's not electrical." | | 9 | "It's residual energy." | | 10 | "it's a bleed point." | | 11 | "It suggested neglect." | | 12 | "It suggested a place where" | | 13 | "She unbuckled her satchel and" | | 14 | "It was a compass, but" | | 15 | "It spun, lazy at first," | | 16 | "It wasn't searching for magnetic" | | 17 | "It was hunting." | | 18 | "She watched the dust motes" | | 19 | "She watched Eva's hands, trembling" |
| | ratio | 0.336 | |
| 68.39% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 112 | | totalSentences | 143 | | matches | | 0 | "The damp cold of the" | | 1 | "She checked her watch, the" | | 2 | "Quinn holstered her light and" | | 3 | "She crouched, the knees of" | | 4 | "The body was a male," | | 5 | "He looked like he was" | | 6 | "Cause of death remains unknown," | | 7 | "Quinn hated those words." | | 8 | "They were a placeholder for" | | 9 | "You're looking at it wrong," | | 10 | "The voice came from behind" | | 11 | "Quinn didn't startle." | | 12 | "She turned her head slowly," | | 13 | "Eva Kowalski stood near the" | | 14 | "The young woman's curly red" | | 15 | "Quinn stood, brushing dust from" | | 16 | "I'm looking at a dead" | | 17 | "That's the only way that" | | 18 | "Eva tucked a stray curl" | | 19 | "The British Museum researcher wasn't" |
| | ratio | 0.783 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 143 | | matches | | 0 | "If there was a door," | | 1 | "If you're still inside when" | | 2 | "If the market moved, there" | | 3 | "If there are others, Quinn" |
| | ratio | 0.028 | |
| 92.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 4 | | matches | | 0 | "The damp cold of the abandoned Tube station seeped through the sole of Detective Harlow Quinn's boots, a persistent chill that had nothing to do with the London…" | | 1 | "At five-nine, she towered over the scene techs who were currently packing up their kits with uneasy glances toward the tunnel shadows." | | 2 | "The British Museum researcher wasn't police, but she had clearance Quinn didn't fully understand, granted by superiors who spoke in hushed tones about restricte…" | | 3 | "You said this market moves, Quinn said, recalling the intel she'd scraped together from informants who smelled of sulfur and fear." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |