| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1134 | | 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) | |
| 55.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1134 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "gloom" | | 1 | "wavering" | | 2 | "etched" | | 3 | "warmth" | | 4 | "tracing" | | 5 | "weight" | | 6 | "scanned" | | 7 | "pulse" | | 8 | "furrowed" |
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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 | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 81 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1127 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 62.04% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 739 | | uniqueNames | 3 | | maxNameDensity | 1.76 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | | persons | | | places | (empty) | | globalScore | 0.62 | | windowScore | 0.833 | |
| 59.09% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 2 | | matches | | 0 | "tasted like rust and rot" | | 1 | "as if reaching for something" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.887 | | wordCount | 1127 | | matches | | 0 | "not north but at an odd angle, directly toward the tunnel wall" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 104 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 28.18 | | std | 20.5 | | cv | 0.728 | | sampleLengths | | 0 | 61 | | 1 | 41 | | 2 | 22 | | 3 | 61 | | 4 | 3 | | 5 | 48 | | 6 | 60 | | 7 | 4 | | 8 | 37 | | 9 | 17 | | 10 | 16 | | 11 | 55 | | 12 | 5 | | 13 | 14 | | 14 | 45 | | 15 | 5 | | 16 | 72 | | 17 | 5 | | 18 | 35 | | 19 | 7 | | 20 | 4 | | 21 | 73 | | 22 | 13 | | 23 | 22 | | 24 | 38 | | 25 | 22 | | 26 | 29 | | 27 | 21 | | 28 | 46 | | 29 | 9 | | 30 | 3 | | 31 | 16 | | 32 | 50 | | 33 | 46 | | 34 | 12 | | 35 | 10 | | 36 | 37 | | 37 | 13 | | 38 | 28 | | 39 | 22 |
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| 87.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 81 | | matches | | 0 | "been swept" | | 1 | "was gone" | | 2 | "were stacked" | | 3 | "was brought" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 127 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 1 | | flaggedSentences | 7 | | totalSentences | 104 | | ratio | 0.067 | | matches | | 0 | "Quinn had seen that look before—the need to make the facts fit the first story that came along." | | 1 | "She noted the position of the debris near the feet—a crushed beer can, a scatter of gravel." | | 2 | "Silver cufflinks on his shirt—one with a small, dark stone inset." | | 3 | "A faint reflection caught her eye—a small metallic object half-hidden under a chunk of fallen plaster near the ticket booth." | | 4 | "The overturned crates were stacked haphazardly—three toppled, two upright." | | 5 | "Two distinct sole patterns: one with a pronounced tread, likely a heavy boot; the other smooth-soled, possibly dress shoes." | | 6 | "The token, the compass, the missing watch, the swept floor—none of it added up to a simple robbery." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 748 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, bone-white token," |
| | adverbCount | 16 | | adverbRatio | 0.0213903743315508 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0066844919786096255 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 10.84 | | std | 7.29 | | cv | 0.673 | | sampleLengths | | 0 | 12 | | 1 | 22 | | 2 | 27 | | 3 | 16 | | 4 | 7 | | 5 | 18 | | 6 | 7 | | 7 | 15 | | 8 | 3 | | 9 | 11 | | 10 | 20 | | 11 | 6 | | 12 | 8 | | 13 | 11 | | 14 | 2 | | 15 | 3 | | 16 | 11 | | 17 | 37 | | 18 | 10 | | 19 | 17 | | 20 | 9 | | 21 | 5 | | 22 | 19 | | 23 | 4 | | 24 | 37 | | 25 | 9 | | 26 | 8 | | 27 | 2 | | 28 | 14 | | 29 | 10 | | 30 | 3 | | 31 | 11 | | 32 | 10 | | 33 | 6 | | 34 | 11 | | 35 | 4 | | 36 | 5 | | 37 | 5 | | 38 | 9 | | 39 | 9 | | 40 | 14 | | 41 | 22 | | 42 | 5 | | 43 | 16 | | 44 | 20 | | 45 | 3 | | 46 | 16 | | 47 | 17 | | 48 | 5 | | 49 | 13 |
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| 65.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.41346153846153844 | | totalSentences | 104 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 96.47% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 68 | | matches | | 0 | "He was a good officer," | | 1 | "he said, nodding as she" | | 2 | "She squatted beside the corpse," | | 3 | "His face was slack, eyes" | | 4 | "She pressed her gloved fingers" | | 5 | "She noted the position of" | | 6 | "She spotted a faint line" | | 7 | "They were clean." | | 8 | "She retrieved it." | | 9 | "She turned it over in" | | 10 | "She pocketed it." | | 11 | "She moved to the tick" | | 12 | "She crouched and studied them." | | 13 | "She pointed at the drag" | | 14 | "She stood, brushing dust from" | | 15 | "He pulled out a small," | | 16 | "She'd never seen anything like" | | 17 | "She slipped it into her" | | 18 | "Her voice was flat, final" | | 19 | "She didn't believe in the" |
| | ratio | 0.309 | |
| 11.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 68 | | matches | | 0 | "The air in the abandoned" | | 1 | "Harlow Quinn ducked under the" | | 2 | "The portable floodlights the forensics" | | 3 | "DS Finch stood near the" | | 4 | "He was a good officer," | | 5 | "Quinn had seen that look" | | 6 | "he said, nodding as she" | | 7 | "Quinn didn't answer." | | 8 | "She squatted beside the corpse," | | 9 | "The man lay on his" | | 10 | "His face was slack, eyes" | | 11 | "She pressed her gloved fingers" | | 12 | "Quinn stood and walked a" | | 13 | "She noted the position of" | | 14 | "The concrete had been swept." | | 15 | "She spotted a faint line" | | 16 | "Quinn knelt again, this time" | | 17 | "They were clean." | | 18 | "The man's clothes were good" | | 19 | "A subtle pinstripe in the" |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 1 | | matches | | 0 | "The man lay on his side, one arm bent beneath him, the other stretched out as if reaching for something." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn interrupted (interrupt)" |
| | dialogueSentences | 42 | | tagDensity | 0.238 | | leniency | 0.476 | | rawRatio | 0.1 | | effectiveRatio | 0.048 | |