| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 61 | | tagDensity | 0.279 | | leniency | 0.557 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1143 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 38.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1143 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "apprehension" | | 1 | "chaotic" | | 2 | "weight" | | 3 | "amidst" | | 4 | "velvet" | | 5 | "standard" | | 6 | "tension" | | 7 | "etched" | | 8 | "whisper" | | 9 | "depths" | | 10 | "echoed" | | 11 | "perfect" | | 12 | "shattered" | | 13 | "silence" |
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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 | 1 | | narrationSentences | 90 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | 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 | 3 | | markdownWords | 14 | | totalWords | 1137 | | ratio | 0.012 | | matches | | 0 | "squelch" | | 1 | "Silas’s Odds & Ends" | | 2 | "“Harlow Quinn. I heard you were looking for me.”" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 49.75% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 798 | | uniqueNames | 6 | | maxNameDensity | 2.01 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Maglite | 1 | | Harlow | 1 | | Quinn | 16 | | Met | 1 | | Vance | 14 | | Odds | 1 |
| | persons | | 0 | "Maglite" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Met" | | 4 | "Vance" |
| | places | (empty) | | globalScore | 0.497 | | windowScore | 0.667 | |
| 8.49% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like a graveyard for a million los" | | 1 | "eyes that seemed to follow you even when the light wasn't moving" | | 2 | "looked like a man-shaped pile of sludge," |
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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 | 1137 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 67 | | mean | 16.97 | | std | 16.33 | | cv | 0.962 | | sampleLengths | | 0 | 79 | | 1 | 1 | | 2 | 7 | | 3 | 14 | | 4 | 42 | | 5 | 29 | | 6 | 15 | | 7 | 52 | | 8 | 32 | | 9 | 21 | | 10 | 3 | | 11 | 14 | | 12 | 5 | | 13 | 49 | | 14 | 25 | | 15 | 6 | | 16 | 45 | | 17 | 10 | | 18 | 20 | | 19 | 14 | | 20 | 4 | | 21 | 3 | | 22 | 1 | | 23 | 18 | | 24 | 40 | | 25 | 6 | | 26 | 1 | | 27 | 7 | | 28 | 7 | | 29 | 19 | | 30 | 4 | | 31 | 10 | | 32 | 5 | | 33 | 36 | | 34 | 8 | | 35 | 3 | | 36 | 3 | | 37 | 24 | | 38 | 35 | | 39 | 15 | | 40 | 4 | | 41 | 13 | | 42 | 43 | | 43 | 5 | | 44 | 3 | | 45 | 6 | | 46 | 4 | | 47 | 4 | | 48 | 30 | | 49 | 5 |
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| 81.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 90 | | matches | | 0 | "was dissolved" | | 1 | "been inflated" | | 2 | "was blackened" | | 3 | "was painted" | | 4 | "been drilled" | | 5 | "was shattered" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 134 | | matches | | 0 | "wasn't sitting" | | 1 | "was pointing" | | 2 | "was shaking" | | 3 | "wasn't pointing" | | 4 | "was vibrating was emitting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 134 | | ratio | 0.015 | | matches | | 0 | "The smell hit Harlow Quinn first—rotten egg sulphur mixed with the metallic tang of old blood and ozone." | | 1 | "The interior of the stall was chaotic—shelves groaning under the weight of oddities." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 802 | | adjectiveStacks | 1 | | stackExamples | | 0 | "jagged, hand-painted script." |
| | adverbCount | 29 | | adverbRatio | 0.03615960099750624 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.008728179551122194 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 8.49 | | std | 5.93 | | cv | 0.699 | | sampleLengths | | 0 | 22 | | 1 | 18 | | 2 | 19 | | 3 | 6 | | 4 | 14 | | 5 | 1 | | 6 | 7 | | 7 | 14 | | 8 | 13 | | 9 | 13 | | 10 | 4 | | 11 | 12 | | 12 | 14 | | 13 | 15 | | 14 | 10 | | 15 | 5 | | 16 | 16 | | 17 | 12 | | 18 | 12 | | 19 | 12 | | 20 | 19 | | 21 | 13 | | 22 | 12 | | 23 | 9 | | 24 | 3 | | 25 | 10 | | 26 | 4 | | 27 | 5 | | 28 | 13 | | 29 | 23 | | 30 | 13 | | 31 | 1 | | 32 | 8 | | 33 | 16 | | 34 | 3 | | 35 | 3 | | 36 | 14 | | 37 | 8 | | 38 | 1 | | 39 | 22 | | 40 | 8 | | 41 | 2 | | 42 | 10 | | 43 | 5 | | 44 | 5 | | 45 | 11 | | 46 | 3 | | 47 | 4 | | 48 | 3 | | 49 | 1 |
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| 61.69% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.43283582089552236 | | totalSentences | 134 | | uniqueOpeners | 58 | |
| 80.32% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 83 | | matches | | 0 | "Instead, she pointed the beam" | | 1 | "Then, a voice." |
| | ratio | 0.024 | |
| 89.88% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 83 | | matches | | 0 | "She adjusted the grip on" | | 1 | "She wasn't supposed to be" | | 2 | "He stepped from the shadows," | | 3 | "He held a notepad." | | 4 | "She stopped in front of" | | 5 | "He was dissolved." | | 6 | "It looked like a man-shaped" | | 7 | "She needed to be close." | | 8 | "She reached out, her fingers" | | 9 | "He stepped back and yanked" | | 10 | "She didn't touch the handle." | | 11 | "It looked normal." | | 12 | "He pulled a tension tool" | | 13 | "It was beautiful." | | 14 | "She reached out and picked" | | 15 | "It was heavier than it" | | 16 | "It didn't spin lazily." | | 17 | "It jittered violently, trembling like" | | 18 | "It was pointing straight down." | | 19 | "He finally fumbled it free" |
| | ratio | 0.325 | |
| 32.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 83 | | matches | | 0 | "The beam of a Maglite" | | 1 | "The smell hit Harlow Quinn" | | 2 | "She adjusted the grip on" | | 3 | "She wasn't supposed to be" | | 4 | "The Met’s bylaws died somewhere" | | 5 | "The voice was young, tight" | | 6 | "a young detective named Vance" | | 7 | "He stepped from the shadows," | | 8 | "He held a notepad." | | 9 | "Quinn kept her eyes on" | | 10 | "Vance said, his voice rising" | | 11 | "Quinn took a step forward," | | 12 | "The abandoned platform looked like" | | 13 | "Graffiti covered the walls in" | | 14 | "She stopped in front of" | | 15 | "The sign above it read" | | 16 | "Vance stepped closer, his hand" | | 17 | "Vance shook his head, staring" | | 18 | "Quinn shone her light inside." | | 19 | "The interior of the stall" |
| | ratio | 0.855 | |
| 60.24% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 83 | | matches | | | ratio | 0.012 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 2 | | matches | | 0 | "The beam of a Maglite cut the stagnant air, illuminating tendrils of dust that hung suspended in the darkness like marionette strings." | | 1 | "Shrapnel sharp as needles was already puncturing her skin, drawing blood." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 6 | | matches | | 0 | "Vance said, his voice rising" | | 1 | "Quinn took, her boots making a wet *squelch* on the damp concrete" | | 2 | "Vance stepped, his hand drifting near his own weapon" | | 3 | "Vance whispered, his voice trembling" | | 4 | "Quinn said, her voice dropping to a whisper" | | 5 | "Quinn said, her eyes narrowing" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "Vance whispered (whisper)" | | 1 | "Vance screamed (scream)" |
| | dialogueSentences | 61 | | tagDensity | 0.131 | | leniency | 0.262 | | rawRatio | 0.25 | | effectiveRatio | 0.066 | |