| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.37 | | leniency | 0.739 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1550 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 83.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1550 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "scanned" | | 1 | "etched" | | 2 | "perfect" | | 3 | "velvet" | | 4 | "flicked" |
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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 | 167 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 167 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 196 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1550 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1236 | | uniqueNames | 9 | | maxNameDensity | 1.94 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 24 | | Camden | 1 | | Town | 1 | | Tube | 1 | | Veil | 2 | | Market | 4 | | Compass | 1 | | Kowalski | 1 | | Eva | 15 |
| | persons | | 0 | "Quinn" | | 1 | "Market" | | 2 | "Compass" | | 3 | "Kowalski" | | 4 | "Eva" |
| | places | | | globalScore | 0.529 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | 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 | 1550 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 196 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 84 | | mean | 18.45 | | std | 14.81 | | cv | 0.803 | | sampleLengths | | 0 | 65 | | 1 | 14 | | 2 | 65 | | 3 | 24 | | 4 | 3 | | 5 | 8 | | 6 | 56 | | 7 | 41 | | 8 | 16 | | 9 | 8 | | 10 | 4 | | 11 | 3 | | 12 | 50 | | 13 | 7 | | 14 | 6 | | 15 | 25 | | 16 | 31 | | 17 | 3 | | 18 | 15 | | 19 | 28 | | 20 | 4 | | 21 | 4 | | 22 | 8 | | 23 | 4 | | 24 | 7 | | 25 | 27 | | 26 | 5 | | 27 | 7 | | 28 | 49 | | 29 | 8 | | 30 | 7 | | 31 | 43 | | 32 | 22 | | 33 | 20 | | 34 | 6 | | 35 | 4 | | 36 | 26 | | 37 | 29 | | 38 | 3 | | 39 | 19 | | 40 | 21 | | 41 | 37 | | 42 | 27 | | 43 | 7 | | 44 | 6 | | 45 | 8 | | 46 | 34 | | 47 | 11 | | 48 | 5 | | 49 | 5 |
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| 96.86% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 167 | | matches | | 0 | "was buttoned" | | 1 | "been sealed" | | 2 | "were gloved" | | 3 | "was sewn" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 203 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 196 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1237 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.016168148746968473 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0016168148746968471 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 196 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 196 | | mean | 7.91 | | std | 5.04 | | cv | 0.637 | | sampleLengths | | 0 | 9 | | 1 | 27 | | 2 | 17 | | 3 | 12 | | 4 | 14 | | 5 | 13 | | 6 | 11 | | 7 | 13 | | 8 | 11 | | 9 | 7 | | 10 | 6 | | 11 | 4 | | 12 | 16 | | 13 | 8 | | 14 | 3 | | 15 | 8 | | 16 | 14 | | 17 | 26 | | 18 | 5 | | 19 | 5 | | 20 | 6 | | 21 | 8 | | 22 | 14 | | 23 | 5 | | 24 | 6 | | 25 | 8 | | 26 | 12 | | 27 | 1 | | 28 | 3 | | 29 | 8 | | 30 | 4 | | 31 | 2 | | 32 | 1 | | 33 | 15 | | 34 | 12 | | 35 | 12 | | 36 | 11 | | 37 | 7 | | 38 | 6 | | 39 | 7 | | 40 | 18 | | 41 | 4 | | 42 | 1 | | 43 | 2 | | 44 | 19 | | 45 | 3 | | 46 | 2 | | 47 | 3 | | 48 | 5 | | 49 | 10 |
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| 40.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.2755102040816326 | | totalSentences | 196 | | uniqueOpeners | 54 | |
| 49.02% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 136 | | matches | | 0 | "Then back to the pool." | | 1 | "Then to the man’s throat." |
| | ratio | 0.015 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 136 | | matches | | 0 | "She counted twelve steps, then" | | 1 | "She pushed the door with" | | 2 | "It had moved here." | | 3 | "He snapped a salute she" | | 4 | "She did not look up" | | 5 | "She lifted her gloved fingers" | | 6 | "She tucked hair behind her" | | 7 | "She stopped at a stall" | | 8 | "His throat showed a faint" | | 9 | "His coat was buttoned." | | 10 | "She lifted her glasses and" | | 11 | "She stood and paced the" | | 12 | "She leaned in." | | 13 | "She stood abruptly." | | 14 | "She lifted the pen and" | | 15 | "She turned back to the" | | 16 | "She followed the prints to" | | 17 | "She stepped closer." | | 18 | "Its needle pointed up." | | 19 | "She lifted her torch and" |
| | ratio | 0.206 | |
| 11.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 122 | | totalSentences | 136 | | matches | | 0 | "The stairwell stank of wet" | | 1 | "Harlow Quinn kept her hand" | | 2 | "The worn leather watch on" | | 3 | "She counted twelve steps, then" | | 4 | "She pushed the door with" | | 5 | "The Veil Market had taken" | | 6 | "Stalls of hammered tin and" | | 7 | "Venders moved between tables with" | | 8 | "The air thrummed low, a" | | 9 | "A full moon schedule, the" | | 10 | "The Market moved every full" | | 11 | "It had moved here." | | 12 | "A constable in a hi-vis" | | 13 | "He snapped a salute she" | | 14 | "She did not look up" | | 15 | "The body lay on the" | | 16 | "A man in his thirties," | | 17 | "The water around him was" | | 18 | "Quinn crouched, kept her knees" | | 19 | "The brass case of a" |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 136 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 1 | | matches | | 0 | "A man in his thirties, coat too heavy for the damp, face down in a shallow pool of water that had seeped through the cracked tiles." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 17 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.37 | | leniency | 0.739 | | rawRatio | 0 | | effectiveRatio | 0 | |