| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 81.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1369 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "sharply" | | 1 | "loosely" | | 2 | "carefully" | | 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) | |
| 52.52% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1369 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "stark" | | 1 | "fluttered" | | 2 | "gloom" | | 3 | "scanning" | | 4 | "etched" | | 5 | "intricate" | | 6 | "standard" | | 7 | "pulsed" | | 8 | "could feel" | | 9 | "footsteps" | | 10 | "chill" | | 11 | "weight" |
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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 | 149 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 149 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 149 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1369 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 5 | | matches | | 0 | "It looks like a heart attack, Miller said, though his voice lacked conviction." | | 1 | "I see a pattern, Quinn said, her voice low." | | 2 | "It points toward the nearest supernatural rift, she said." | | 3 | "That is not grout, Quinn said." | | 4 | "Morris knew this, she whispered." |
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| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 1369 | | uniqueNames | 12 | | maxNameDensity | 1.31 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Miller" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 3 | | Quinn | 18 | | Morris | 5 | | Veil | 2 | | Market | 2 | | Miller | 18 | | Detective | 2 | | London | 1 | | Underground | 1 | | You | 3 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Miller" | | 5 | "You" |
| | places | | | globalScore | 0.843 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | glossingSentenceCount | 1 | | matches | | 0 | "darkness that seemed to swallow the light from her flashlight" |
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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 | 1369 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 149 | | matches | | 0 | "is that the" | | 1 | "follow that thing" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 26.84 | | std | 21.56 | | cv | 0.803 | | sampleLengths | | 0 | 110 | | 1 | 43 | | 2 | 39 | | 3 | 62 | | 4 | 18 | | 5 | 11 | | 6 | 52 | | 7 | 29 | | 8 | 54 | | 9 | 6 | | 10 | 8 | | 11 | 40 | | 12 | 14 | | 13 | 64 | | 14 | 8 | | 15 | 32 | | 16 | 42 | | 17 | 17 | | 18 | 28 | | 19 | 9 | | 20 | 32 | | 21 | 52 | | 22 | 7 | | 23 | 10 | | 24 | 4 | | 25 | 25 | | 26 | 18 | | 27 | 24 | | 28 | 40 | | 29 | 39 | | 30 | 6 | | 31 | 10 | | 32 | 16 | | 33 | 16 | | 34 | 47 | | 35 | 31 | | 36 | 9 | | 37 | 17 | | 38 | 5 | | 39 | 1 | | 40 | 18 | | 41 | 28 | | 42 | 36 | | 43 | 18 | | 44 | 24 | | 45 | 14 | | 46 | 8 | | 47 | 19 | | 48 | 22 | | 49 | 3 |
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| 81.71% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 10 | | totalSentences | 149 | | matches | | 0 | "was supposed" | | 1 | "were lined" | | 2 | "was swept" | | 3 | "were arranged" | | 4 | "was built" | | 5 | "was etched" | | 6 | "was opened" | | 7 | "been emptied" | | 8 | "was pulled" | | 9 | "been breached" |
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| 86.52% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 235 | | matches | | 0 | "wasn't pointing" | | 1 | "was spinning" | | 2 | "was vibrating" | | 3 | "was walking" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 149 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1372 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.023323615160349854 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.0058309037900874635 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 149 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 149 | | mean | 9.19 | | std | 5.87 | | cv | 0.639 | | sampleLengths | | 0 | 16 | | 1 | 18 | | 2 | 20 | | 3 | 24 | | 4 | 11 | | 5 | 21 | | 6 | 19 | | 7 | 24 | | 8 | 13 | | 9 | 13 | | 10 | 13 | | 11 | 8 | | 12 | 21 | | 13 | 7 | | 14 | 4 | | 15 | 8 | | 16 | 14 | | 17 | 18 | | 18 | 5 | | 19 | 6 | | 20 | 5 | | 21 | 11 | | 22 | 5 | | 23 | 11 | | 24 | 20 | | 25 | 13 | | 26 | 4 | | 27 | 7 | | 28 | 5 | | 29 | 9 | | 30 | 5 | | 31 | 10 | | 32 | 18 | | 33 | 12 | | 34 | 4 | | 35 | 2 | | 36 | 3 | | 37 | 5 | | 38 | 13 | | 39 | 19 | | 40 | 5 | | 41 | 3 | | 42 | 4 | | 43 | 3 | | 44 | 7 | | 45 | 3 | | 46 | 13 | | 47 | 9 | | 48 | 3 | | 49 | 3 |
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| 37.25% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.2483221476510067 | | totalSentences | 149 | | uniqueOpeners | 37 | |
| 69.93% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 143 | | matches | | 0 | "Just old tiling." | | 1 | "Maybe a souvenir from the" | | 2 | "Then we should call it" |
| | ratio | 0.021 | |
| 54.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 59 | | totalSentences | 143 | | matches | | 0 | "She kept her movements economical," | | 1 | "She glanced at the worn" | | 2 | "She moved toward the center" | | 3 | "He was younger, his shoulders" | | 4 | "He looked up as Quinn" | | 5 | "He lay on his back," | | 6 | "She ignored the damp seeping" | | 7 | "She examined the victim's chest." | | 8 | "It looks like a heart" | | 9 | "We got a call from" | | 10 | "She looked around the room." | | 11 | "It was the same smell" | | 12 | "She noticed the floor tiles" | | 13 | "It wasn't wear and tear." | | 14 | "It was deliberate." | | 15 | "She moved closer to the" | | 16 | "They weren't random." | | 17 | "They were geometric." | | 18 | "She pulled a small magnifying" | | 19 | "You seeing things, Detective?" |
| | ratio | 0.413 | |
| 40.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 120 | | totalSentences | 143 | | matches | | 0 | "The air inside the abandoned" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "She kept her movements economical," | | 3 | "She glanced at the worn" | | 4 | "The hands had stopped moving" | | 5 | "She moved toward the center" | | 6 | "The Veil Market was supposed" | | 7 | "DS Miller stood by the" | | 8 | "He was younger, his shoulders" | | 9 | "He looked up as Quinn" | | 10 | "Quinn stopped five feet from" | | 11 | "The victim was a man" | | 12 | "He lay on his back," | | 13 | "That was the first thing" | | 14 | "A homicide this violent, based" | | 15 | "Miller clicked his flashlight off," | | 16 | "Quinn crouched, her knees popping." | | 17 | "She ignored the damp seeping" | | 18 | "She examined the victim's chest." | | 19 | "The skin was pale, almost" |
| | ratio | 0.839 | |
| 69.93% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 143 | | matches | | 0 | "If this gets out, the" | | 1 | "If we call it in," |
| | ratio | 0.014 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 2 | | matches | | 0 | "She kept her movements economical, her posture rigid with the military precision that had defined her eighteen years of service." | | 1 | "It was a compass, small and heavy, the casing made of brass that had succumbed to a thick patina of verdigris." |
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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 | |