| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.464 | | leniency | 0.929 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1371 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1371 | | totalAiIsms | 28 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | |
| | highlights | | 0 | "weight" | | 1 | "shattered" | | 2 | "etched" | | 3 | "shimmered" | | 4 | "rhythmic" | | 5 | "pulse" | | 6 | "echoed" | | 7 | "dancing" | | 8 | "perfect" | | 9 | "mechanical" | | 10 | "silence" | | 11 | "magnetic" | | 12 | "traced" | | 13 | "abyssal" | | 14 | "trembled" | | 15 | "chaotic" | | 16 | "familiar" | | 17 | "echoing" | | 18 | "grave" | | 19 | "silk" | | 20 | "velvet" |
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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 | 89 | | matches | (empty) | |
| 62.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 2 | | narrationSentences | 89 | | 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 | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1369 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 94.95% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1090 | | uniqueNames | 10 | | maxNameDensity | 1.1 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 12 | | Quinn | 1 | | Camden | 1 | | Tube | 2 | | Sergeant | 1 | | Miller | 9 | | Metropolitan | 1 | | Police | 1 | | Veil | 1 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Miller" | | 4 | "Market" |
| | places | (empty) | | globalScore | 0.95 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like a flute fashioned from glass" |
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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 | 1369 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 33.39 | | std | 20.79 | | cv | 0.623 | | sampleLengths | | 0 | 69 | | 1 | 59 | | 2 | 14 | | 3 | 54 | | 4 | 13 | | 5 | 14 | | 6 | 81 | | 7 | 19 | | 8 | 56 | | 9 | 25 | | 10 | 41 | | 11 | 10 | | 12 | 15 | | 13 | 75 | | 14 | 18 | | 15 | 26 | | 16 | 11 | | 17 | 46 | | 18 | 37 | | 19 | 51 | | 20 | 8 | | 21 | 47 | | 22 | 19 | | 23 | 32 | | 24 | 51 | | 25 | 40 | | 26 | 7 | | 27 | 10 | | 28 | 41 | | 29 | 18 | | 30 | 25 | | 31 | 49 | | 32 | 20 | | 33 | 15 | | 34 | 80 | | 35 | 10 | | 36 | 47 | | 37 | 45 | | 38 | 15 | | 39 | 29 | | 40 | 27 |
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| 97.38% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 89 | | matches | | 0 | "was supposed" | | 1 | "was supposed" | | 2 | "was replaced" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 183 | | matches | (empty) | |
| 87.91% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 104 | | ratio | 0.019 | | matches | | 0 | "Beneath her boots, the floor crunched with the debris of decades—shattered glass, pulverized concrete, and forgotten remnants of commuters lost to time." | | 1 | "The tunnel walls began to shimmer, the brickwork appearing to turn translucent, revealing a sprawling, chaotic marketplace of stalls and flickering lanterns buried deep beneath the earth—a place that shouldn't exist." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1098 | | adjectiveStacks | 1 | | stackExamples | | 0 | "uneven, dirt-packed alleyways" |
| | adverbCount | 25 | | adverbRatio | 0.022768670309653915 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00546448087431694 | |
| 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 | 13.16 | | std | 8.27 | | cv | 0.628 | | sampleLengths | | 0 | 11 | | 1 | 23 | | 2 | 13 | | 3 | 22 | | 4 | 10 | | 5 | 12 | | 6 | 31 | | 7 | 3 | | 8 | 3 | | 9 | 14 | | 10 | 6 | | 11 | 22 | | 12 | 6 | | 13 | 20 | | 14 | 13 | | 15 | 14 | | 16 | 5 | | 17 | 12 | | 18 | 8 | | 19 | 17 | | 20 | 11 | | 21 | 28 | | 22 | 10 | | 23 | 9 | | 24 | 6 | | 25 | 9 | | 26 | 41 | | 27 | 15 | | 28 | 10 | | 29 | 11 | | 30 | 3 | | 31 | 17 | | 32 | 10 | | 33 | 8 | | 34 | 2 | | 35 | 15 | | 36 | 14 | | 37 | 18 | | 38 | 4 | | 39 | 4 | | 40 | 6 | | 41 | 29 | | 42 | 18 | | 43 | 3 | | 44 | 15 | | 45 | 8 | | 46 | 11 | | 47 | 8 | | 48 | 11 | | 49 | 14 |
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| 35.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.2692307692307692 | | totalSentences | 104 | | uniqueOpeners | 28 | |
| 77.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 86 | | matches | | 0 | "Too quick to dismiss the" | | 1 | "Then, he turned and vanished" |
| | ratio | 0.023 | |
| 10.70% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 86 | | matches | | 0 | "It defied physics." | | 1 | "It defied logic." | | 2 | "He picked his way through" | | 3 | "He didn’t look at the" | | 4 | "He kept his eyes fixed" | | 5 | "She reached out, her fingers" | | 6 | "She glanced at the worn" | | 7 | "she gestured, keeping her voice" | | 8 | "He leaned against a rusted" | | 9 | "She traced the line of" | | 10 | "They weren't footprints." | | 11 | "They were circular, rhythmic impressions" | | 12 | "She followed them with her" | | 13 | "She stepped closer to Miller," | | 14 | "She watched his expression." | | 15 | "He was too calm." | | 16 | "She reached into her inner" | | 17 | "He pushed off the support" | | 18 | "She looked back at the" | | 19 | "It was just a piece" |
| | ratio | 0.523 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 82 | | totalSentences | 86 | | matches | | 0 | "The damp, subterranean air clung" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "Harlow centered her light on" | | 3 | "A small brass compass sat" | | 4 | "The verdigris patina coating the" | | 5 | "It defied physics." | | 6 | "It defied logic." | | 7 | "a voice echoed from the" | | 8 | "Sergeant Miller stepped into the" | | 9 | "He picked his way through" | | 10 | "He didn’t look at the" | | 11 | "He kept his eyes fixed" | | 12 | "Harlow knelt by the compass." | | 13 | "She reached out, her fingers" | | 14 | "The hair on her arms" | | 15 | "A low-frequency hum emanated from" | | 16 | "She glanced at the worn" | | 17 | "The second hand ticked in" | | 18 | "she gestured, keeping her voice" | | 19 | "Miller sighed, flipping his notepad" |
| | ratio | 0.953 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 86 | | matches | (empty) | | ratio | 0 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "He picked his way through the rubble with a casualness that pricked at Harlow’s nerves, his boots scraping against the uneven stone." | | 1 | "The tunnel walls began to shimmer, the brickwork appearing to turn translucent, revealing a sprawling, chaotic marketplace of stalls and flickering lanterns bur…" | | 2 | "Miller grabbed her arm, his grip bruising, but she tore away, her focus narrowed to the man who was supposed to be dead, the man who was supposed to be resting …" |
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| 9.62% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 3 | | matches | | 0 | "Harlow stood, her sharp jaw grinding" | | 1 | "she continued, her voice lowering into a dangerous, rhythmic cadence" | | 2 | "she breathed, the word a blade in her throat" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 7 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she continued (continue)" | | 2 | "she pressed (press)" | | 3 | "she whispered (whisper)" | | 4 | "Miller warned (warn)" | | 5 | "she demanded (demand)" | | 6 | "she breathed (breathe)" |
| | dialogueSentences | 28 | | tagDensity | 0.357 | | leniency | 0.714 | | rawRatio | 0.7 | | effectiveRatio | 0.5 | |