| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 29 | | tagDensity | 0.517 | | leniency | 1 | | rawRatio | 0.067 | | effectiveRatio | 0.067 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1525 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 73.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1525 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "shattered" | | 1 | "methodical" | | 2 | "familiar" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "tracing" | | 6 | "echoes" |
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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 | 115 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 115 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 128 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1524 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1086 | | uniqueNames | 7 | | maxNameDensity | 1.29 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Patel" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 14 | | Detective | 1 | | Constable | 1 | | Patel | 14 | | Morris | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Constable" | | 3 | "Patel" | | 4 | "Morris" |
| | places | (empty) | | globalScore | 0.855 | | windowScore | 0.833 | |
| 50.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 3 | | matches | | 0 | "as if reaching for the platform’s edge, the other pinned beneath his ribs" | | 1 | "looked like a human molar" | | 2 | "looked like a language, but not one she’d" |
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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 | 1524 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 128 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 41.19 | | std | 25.72 | | cv | 0.624 | | sampleLengths | | 0 | 58 | | 1 | 62 | | 2 | 64 | | 3 | 64 | | 4 | 42 | | 5 | 8 | | 6 | 7 | | 7 | 82 | | 8 | 37 | | 9 | 80 | | 10 | 51 | | 11 | 6 | | 12 | 29 | | 13 | 87 | | 14 | 4 | | 15 | 19 | | 16 | 62 | | 17 | 46 | | 18 | 36 | | 19 | 70 | | 20 | 7 | | 21 | 75 | | 22 | 28 | | 23 | 19 | | 24 | 14 | | 25 | 61 | | 26 | 4 | | 27 | 72 | | 28 | 19 | | 29 | 70 | | 30 | 32 | | 31 | 70 | | 32 | 9 | | 33 | 51 | | 34 | 28 | | 35 | 28 | | 36 | 23 |
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| 93.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 115 | | matches | | 0 | "was sprawled" | | 1 | "was made" | | 2 | "was swept" | | 3 | "been hauled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 166 | | matches | | 0 | "was pointing" | | 1 | "was beginning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 128 | | ratio | 0.008 | | matches | | 0 | "And the door in the wall, the chalk circle, the bone token—they were fragments of something larger, something that ran beneath the city like a vein of black ice." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1093 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, bone-white token." |
| | adverbCount | 27 | | adverbRatio | 0.024702653247941447 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0036596523330283625 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 128 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 128 | | mean | 11.91 | | std | 10.07 | | cv | 0.845 | | sampleLengths | | 0 | 29 | | 1 | 29 | | 2 | 7 | | 3 | 19 | | 4 | 20 | | 5 | 16 | | 6 | 4 | | 7 | 5 | | 8 | 16 | | 9 | 23 | | 10 | 10 | | 11 | 3 | | 12 | 3 | | 13 | 11 | | 14 | 22 | | 15 | 31 | | 16 | 3 | | 17 | 18 | | 18 | 14 | | 19 | 7 | | 20 | 8 | | 21 | 6 | | 22 | 1 | | 23 | 19 | | 24 | 55 | | 25 | 8 | | 26 | 4 | | 27 | 4 | | 28 | 29 | | 29 | 11 | | 30 | 5 | | 31 | 8 | | 32 | 5 | | 33 | 5 | | 34 | 8 | | 35 | 23 | | 36 | 6 | | 37 | 9 | | 38 | 3 | | 39 | 16 | | 40 | 20 | | 41 | 12 | | 42 | 4 | | 43 | 2 | | 44 | 9 | | 45 | 10 | | 46 | 10 | | 47 | 5 | | 48 | 20 | | 49 | 29 |
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| 39.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.265625 | | totalSentences | 128 | | uniqueOpeners | 34 | |
| 66.01% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 101 | | matches | | 0 | "Then he said," | | 1 | "Then she looked at Patel." |
| | ratio | 0.02 | |
| 81.39% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 101 | | matches | | 0 | "She ducked under the police" | | 1 | "She didn’t know the victim." | | 2 | "He was sprawled on his" | | 3 | "His eyes were open, glassy," | | 4 | "She walked a slow, deliberate" | | 5 | "She stopped at the victim’s" | | 6 | "She pointed at the victim’s" | | 7 | "She looked at Patel, her" | | 8 | "He cleared his throat." | | 9 | "She studied the victim’s suit." | | 10 | "She looked at the floor" | | 11 | "She looked closer." | | 12 | "He picked up the compass," | | 13 | "She pulled out a small" | | 14 | "It was made of dark," | | 15 | "She walked toward it, her" | | 16 | "She ran her gloved fingers" | | 17 | "She tried the handle." | | 18 | "It turned, clicked, and the" | | 19 | "They looked like a language," |
| | ratio | 0.347 | |
| 73.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 101 | | matches | | 0 | "The air in the abandoned" | | 1 | "She ducked under the police" | | 2 | "The platform was a cathedral" | | 3 | "The overhead lights, jury-rigged to" | | 4 | "Quinn tightened her jaw." | | 5 | "She didn’t know the victim." | | 6 | "A man, mid-thirties, dressed in" | | 7 | "He was sprawled on his" | | 8 | "His eyes were open, glassy," | | 9 | "Detective Constable Patel, a younger" | | 10 | "Quinn didn’t answer." | | 11 | "She walked a slow, deliberate" | | 12 | "The worn leather of her" | | 13 | "She stopped at the victim’s" | | 14 | "Patel looked up from his" | | 15 | "She pointed at the victim’s" | | 16 | "She looked at Patel, her" | | 17 | "Patel’s pen stopped moving." | | 18 | "He cleared his throat." | | 19 | "Quinn crouched, her knees popping" |
| | ratio | 0.772 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 101 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 7 | | matches | | 0 | "The air in the abandoned Tube station tasted of rust and old copper, a metallic tang that settled on the back of Harlow Quinn’s tongue like a bad penny." | | 1 | "The overhead lights, jury-rigged to a portable generator, cast long, skeletal shadows that swayed with the hum of the machine." | | 2 | "He was sprawled on his back, one arm flung out as if reaching for the platform’s edge, the other pinned beneath his ribs." | | 3 | "A thin layer of grime and powdered concrete covered the platform, the kind of filth that accumulated in a place no one cleaned." | | 4 | "The light carved through the dark, illuminating a stack of old wooden crates, a collapsed sign advertising a brand of cigarettes that had been dead before Quinn…" | | 5 | "It was made of dark, age-stained wood, with a handle that looked like a human molar." | | 6 | "And the door in the wall, the chalk circle, the bone token—they were fragments of something larger, something that ran beneath the city like a vein of black ice…" |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 3 | | matches | | 0 | "She looked, her brown eyes flat" | | 1 | "Quinn crouched, her knees popping" | | 2 | "She walked, her footsteps echoing in the hollow chamber" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | 0 | "Patel whispered (whisper)" |
| | dialogueSentences | 29 | | tagDensity | 0.207 | | leniency | 0.414 | | rawRatio | 0.167 | | effectiveRatio | 0.069 | |