| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.36 | | leniency | 0.72 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 84.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1262 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "very" | | 1 | "softly" | | 2 | "sweetly" |
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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) | |
| 88.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1262 | | totalAiIsms | 3 | | found | | 0 | | | 1 | | | 2 | | word | "the last thing" | | count | 1 |
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| | highlights | | 0 | "stark" | | 1 | "silence" | | 2 | "the last thing" |
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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) | |
| 75.63% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 85 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 11 | | totalWords | 1262 | | ratio | 0.009 | | matches | | 0 | "Three years." | | 1 | "misadventure" | | 2 | "unresolved." | | 3 | "Morris would have gone in already." | | 4 | "can" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 1 | | matches | | 0 | "Then, softly, it laughed, a dry sound like paper tearing." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1094 | | uniqueNames | 19 | | maxNameDensity | 0.64 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Camden | 2 | | Herrera | 2 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Tube | 2 | | Town | 1 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Tomás | 6 | | Saint | 1 | | Christopher | 1 | | Victorian | 1 | | Morris | 3 | | Three | 2 | | Deptford | 1 | | Simple | 1 | | Quinn | 7 |
| | persons | | 0 | "Herrera" | | 1 | "Tomás" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Morris" | | 5 | "Quinn" |
| | places | | 0 | "Camden" | | 1 | "Raven" | | 2 | "Soho" | | 3 | "Tube" | | 4 | "Town" | | 5 | "Chalk" | | 6 | "Farm" | | 7 | "Road" | | 8 | "Victorian" | | 9 | "Three" | | 10 | "Deptford" |
| | globalScore | 1 | | windowScore | 1 | |
| 70.63% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like the same kind of door" | | 1 | "something like coins, and under it a low, sw" |
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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 | 1262 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 99 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 30.78 | | std | 26.39 | | cv | 0.857 | | sampleLengths | | 0 | 43 | | 1 | 60 | | 2 | 93 | | 3 | 3 | | 4 | 3 | | 5 | 82 | | 6 | 64 | | 7 | 13 | | 8 | 65 | | 9 | 58 | | 10 | 12 | | 11 | 61 | | 12 | 95 | | 13 | 12 | | 14 | 9 | | 15 | 29 | | 16 | 3 | | 17 | 11 | | 18 | 40 | | 19 | 43 | | 20 | 5 | | 21 | 24 | | 22 | 4 | | 23 | 5 | | 24 | 60 | | 25 | 44 | | 26 | 23 | | 27 | 9 | | 28 | 11 | | 29 | 41 | | 30 | 9 | | 31 | 1 | | 32 | 23 | | 33 | 23 | | 34 | 36 | | 35 | 2 | | 36 | 20 | | 37 | 14 | | 38 | 63 | | 39 | 7 | | 40 | 39 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 85 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 170 | | matches | | 0 | "was thinking" | | 1 | "was calling" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 99 | | ratio | 0 | | matches | (empty) | |
| 99.87% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1096 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.040145985401459854 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.0072992700729927005 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 12.75 | | std | 9.35 | | cv | 0.734 | | sampleLengths | | 0 | 19 | | 1 | 24 | | 2 | 11 | | 3 | 20 | | 4 | 8 | | 5 | 21 | | 6 | 29 | | 7 | 27 | | 8 | 4 | | 9 | 5 | | 10 | 28 | | 11 | 3 | | 12 | 3 | | 13 | 11 | | 14 | 3 | | 15 | 24 | | 16 | 2 | | 17 | 24 | | 18 | 11 | | 19 | 7 | | 20 | 6 | | 21 | 6 | | 22 | 41 | | 23 | 11 | | 24 | 7 | | 25 | 6 | | 26 | 16 | | 27 | 22 | | 28 | 12 | | 29 | 15 | | 30 | 12 | | 31 | 19 | | 32 | 10 | | 33 | 17 | | 34 | 3 | | 35 | 9 | | 36 | 25 | | 37 | 10 | | 38 | 26 | | 39 | 43 | | 40 | 17 | | 41 | 35 | | 42 | 12 | | 43 | 8 | | 44 | 1 | | 45 | 14 | | 46 | 15 | | 47 | 3 | | 48 | 11 | | 49 | 5 |
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| 70.71% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.45454545454545453 | | totalSentences | 99 | | uniqueOpeners | 45 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 77 | | matches | | 0 | "Twice he glanced back, and" | | 1 | "Then he'd bolted." | | 2 | "Somewhere behind her, a bus" | | 3 | "Then she slid it away" | | 4 | "Then, softly, it laughed, a" | | 5 | "Somewhere below, a voice was" | | 6 | "Then she squared her shoulders," |
| | ratio | 0.091 | |
| 95.32% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 77 | | matches | | 0 | "He took the wet corners" | | 1 | "His dark curls were plastered" | | 2 | "She had followed him from" | | 3 | "She had been good." | | 4 | "He didn't slow." | | 5 | "He cut left past a" | | 6 | "Her boots slapped through standing" | | 7 | "He reached the grille and" | | 8 | "It was small, white, and" | | 9 | "Her hand had already found" | | 10 | "Her other hand, she realized," | | 11 | "She kept her voice level" | | 12 | "She'd told no one where" | | 13 | "She could hear it, faintly," | | 14 | "She thought about the rules." | | 15 | "She had built her whole" | | 16 | "She thought about Tomás Herrera's" | | 17 | "He'd always gone in first." | | 18 | "She unbuttoned her coat, pulled" | | 19 | "She kept her eyes on" |
| | ratio | 0.312 | |
| 83.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 58 | | totalSentences | 77 | | matches | | 0 | "Rain came down over Camden" | | 1 | "Quinn ran through it with" | | 2 | "Tomás Herrera ran well for" | | 3 | "He took the wet corners" | | 4 | "His dark curls were plastered" | | 5 | "She had followed him from" | | 6 | "She had been good." | | 7 | "The shout tore out of" | | 8 | "He didn't slow." | | 9 | "He cut left past a" | | 10 | "Her boots slapped through standing" | | 11 | "The sound already felt very" | | 12 | "The alley bent, then bent" | | 13 | "Brick walls sweated on either" | | 14 | "A rusted fire escape clanged" | | 15 | "The Saint Christopher medallion swung" | | 16 | "Patron saint of travelers, she" | | 17 | "The alley spat them out" | | 18 | "Tomás went straight across it," | | 19 | "Quinn knew every Tube station" |
| | ratio | 0.753 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 2 | | matches | | 0 | "Quinn ran through it with her coat flapping open and her breath sawing in her chest, eyes locked on the figure twenty yards ahead." | | 1 | "But she'd seen things at the edge of that case, half-glimpsed and easy to deny, and this was the first door she had found in three years that felt like the same…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "the figure agreed (agree)" |
| | dialogueSentences | 25 | | tagDensity | 0.16 | | leniency | 0.32 | | rawRatio | 0.25 | | effectiveRatio | 0.08 | |