| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.455 | | leniency | 0.909 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 87.42% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 795 | | 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) | |
| 43.40% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 795 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "jaw clenched" | | 1 | "flicker" | | 2 | "clandestine" | | 3 | "echoed" | | 4 | "fractured" | | 5 | "velvet" | | 6 | "pulsed" | | 7 | "silk" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 63 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 63 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 69 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 792 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 73.72% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 721 | | uniqueNames | 14 | | maxNameDensity | 1.53 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Quinn | 11 | | Morris | 2 | | London | 1 | | Raven | 1 | | Nest | 1 | | Camden | 2 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 5 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Raven" | | 3 | "Camden" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" |
| | places | | | globalScore | 0.737 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 792 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 69 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 33 | | std | 30.08 | | cv | 0.911 | | sampleLengths | | 0 | 112 | | 1 | 16 | | 2 | 10 | | 3 | 89 | | 4 | 74 | | 5 | 69 | | 6 | 58 | | 7 | 18 | | 8 | 51 | | 9 | 4 | | 10 | 73 | | 11 | 9 | | 12 | 20 | | 13 | 16 | | 14 | 22 | | 15 | 4 | | 16 | 29 | | 17 | 39 | | 18 | 6 | | 19 | 30 | | 20 | 20 | | 21 | 4 | | 22 | 13 | | 23 | 6 |
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| 99.69% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 63 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 128 | | matches | (empty) | |
| 18.63% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 69 | | ratio | 0.043 | | matches | | 0 | "Harlow Quinn kept her shoulders low, her boots slapping wet pavement in a rhythm that matched her heartbeat—steady, hard, unyielding." | | 1 | "The runner reached into his coat, produced something small and yellowed—bone." | | 2 | "Her partner's absence hummed in her skull—DS Morris had walked into darkness without understanding it, and the darkness had kept him." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 728 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.020604395604395604 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006868131868131868 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 69 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 69 | | mean | 11.48 | | std | 7.54 | | cv | 0.657 | | sampleLengths | | 0 | 23 | | 1 | 20 | | 2 | 15 | | 3 | 21 | | 4 | 33 | | 5 | 12 | | 6 | 4 | | 7 | 10 | | 8 | 5 | | 9 | 23 | | 10 | 4 | | 11 | 34 | | 12 | 4 | | 13 | 19 | | 14 | 14 | | 15 | 5 | | 16 | 4 | | 17 | 3 | | 18 | 6 | | 19 | 13 | | 20 | 2 | | 21 | 27 | | 22 | 14 | | 23 | 12 | | 24 | 11 | | 25 | 15 | | 26 | 17 | | 27 | 5 | | 28 | 18 | | 29 | 3 | | 30 | 28 | | 31 | 4 | | 32 | 5 | | 33 | 13 | | 34 | 6 | | 35 | 12 | | 36 | 18 | | 37 | 8 | | 38 | 7 | | 39 | 4 | | 40 | 14 | | 41 | 9 | | 42 | 11 | | 43 | 13 | | 44 | 26 | | 45 | 3 | | 46 | 6 | | 47 | 9 | | 48 | 11 | | 49 | 8 |
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| 53.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.34782608695652173 | | totalSentences | 69 | | uniqueOpeners | 24 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 61 | | matches | | 0 | "Then she saw him." | | 1 | "Then she walked into the" |
| | ratio | 0.033 | |
| 75.74% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 61 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "He didn't look back." | | 2 | "He cut left, past the" | | 3 | "She knew the clique gathered" | | 4 | "He bolted north, toward Camden," | | 5 | "She didn't call out again." | | 6 | "She saved her breath." | | 7 | "Her lungs burned clean, her" | | 8 | "He pressed it against the" | | 9 | "She had heard whispers in" | | 10 | "She stood at the threshold." | | 11 | "His olive skin looked pale" | | 12 | "His short curly dark brown" | | 13 | "His left forearm bore the" | | 14 | "He looked up." | | 15 | "He wiped his hands on" | | 16 | "He glanced toward the deeper" | | 17 | "Her partner's absence hummed in" | | 18 | "She wouldn't repeat that ignorance" | | 19 | "Her sharp jaw hardened." |
| | ratio | 0.361 | |
| 9.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 55 | | totalSentences | 61 | | matches | | 0 | "Rain sheared through Soho like" | | 1 | "Harlow Quinn kept her shoulders" | | 2 | "Her closely cropped salt-and-pepper hair" | | 3 | "The sharp line of her" | | 4 | "The runner wore a heavy" | | 5 | "He didn't look back." | | 6 | "The word tore from Quinn's" | | 7 | "The figure didn't break stride." | | 8 | "He cut left, past the" | | 9 | "Quinn knew the bar." | | 10 | "She knew the clique gathered" | | 11 | "The runner didn't enter." | | 12 | "He bolted north, toward Camden," | | 13 | "Quinn's worn leather watch dug" | | 14 | "She didn't call out again." | | 15 | "She saved her breath." | | 16 | "The street narrowed." | | 17 | "Trash cans clattered in an" | | 18 | "The runner vaulted a low" | | 19 | "Her lungs burned clean, her" |
| | ratio | 0.902 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 5 | | matches | | 0 | "Harlow Quinn kept her shoulders low, her boots slapping wet pavement in a rhythm that matched her heartbeat—steady, hard, unyielding." | | 1 | "She knew the clique gathered behind those walls covered with old maps and black-and-white photographs, behind the bookshelf that masked a hidden back room used …" | | 2 | "He bolted north, toward Camden, rain plastering his hood to his skull, his boots skidding on the slick cobblestones." | | 3 | "Her lungs burned clean, her legs driving with the same discipline that had earned her the badge she now carried in her mind more than her pocket." | | 4 | "She had heard whispers in interrogation rooms, fragments from suspects who spoke of a hidden supernatural black market that sold enchanted goods and moved locat…" |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "Tomás moved, his bearing calm, his scarred arm tense" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 11 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0 | | effectiveRatio | 0 | |