| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 36 | | tagDensity | 0.361 | | leniency | 0.722 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 74.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 990 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "completely" | | 3 | "carefully" |
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| 20.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | 0 | "Chen" | | 1 | "Marcus" | | 2 | "Sarah" | | 3 | "Thompson" |
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| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 54.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 990 | | totalAiIsms | 9 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | word | "carried the weight" | | count | 1 |
| | 8 | |
| | highlights | | 0 | "gloom" | | 1 | "echoed" | | 2 | "blown wide" | | 3 | "etched" | | 4 | "racing" | | 5 | "pristine" | | 6 | "footsteps" | | 7 | "carried the weight" | | 8 | "measured" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 78 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 985 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 700 | | uniqueNames | 12 | | maxNameDensity | 0.86 | | worstName | "Holloway" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Holloway" | | discoveredNames | | Thompson | 5 | | Tube | 1 | | Camden | 2 | | Market | 2 | | Maglite | 1 | | Chen | 3 | | British | 1 | | Museum | 1 | | Dr | 2 | | Holloway | 6 | | Veil | 1 | | Sarah | 3 |
| | persons | | 0 | "Thompson" | | 1 | "Chen" | | 2 | "Dr" | | 3 | "Holloway" | | 4 | "Veil" | | 5 | "Sarah" |
| | places | | 0 | "Camden" | | 1 | "Market" | | 2 | "British" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 53.85% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 2 | | matches | | 0 | "circles that seemed to slow whenever I moved" | | 1 | "something between concern and calculation" |
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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 | 985 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 22.39 | | std | 14.01 | | cv | 0.626 | | sampleLengths | | 0 | 12 | | 1 | 34 | | 2 | 14 | | 3 | 51 | | 4 | 32 | | 5 | 10 | | 6 | 43 | | 7 | 15 | | 8 | 37 | | 9 | 18 | | 10 | 29 | | 11 | 4 | | 12 | 36 | | 13 | 10 | | 14 | 43 | | 15 | 9 | | 16 | 7 | | 17 | 43 | | 18 | 49 | | 19 | 12 | | 20 | 33 | | 21 | 3 | | 22 | 39 | | 23 | 7 | | 24 | 44 | | 25 | 22 | | 26 | 10 | | 27 | 18 | | 28 | 8 | | 29 | 25 | | 30 | 5 | | 31 | 15 | | 32 | 40 | | 33 | 10 | | 34 | 29 | | 35 | 17 | | 36 | 32 | | 37 | 21 | | 38 | 16 | | 39 | 23 | | 40 | 3 | | 41 | 17 | | 42 | 35 | | 43 | 5 |
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| 91.77% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 78 | | matches | | 0 | "were blown" | | 1 | "was handwritten" | | 2 | "been killed" |
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| 27.59% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 116 | | matches | | 0 | "wasn't pointing" | | 1 | "was spinning" | | 2 | "was spinning" |
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| 57.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 101 | | ratio | 0.03 | | matches | | 0 | "The positioning of her limbs—too neat, too deliberate." | | 1 | "Her disappearance had made the papers because of who she worked for—Dr." | | 2 | "The lack of blood spatter, the pristine condition of her clothes, the careful placement of her body—all pointed to a dump site." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 704 | | adjectiveStacks | 1 | | stackExamples | | 0 | "sharp behind wire-rimmed glasses." |
| | adverbCount | 25 | | adverbRatio | 0.03551136363636364 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.011363636363636364 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 9.75 | | std | 6.98 | | cv | 0.716 | | sampleLengths | | 0 | 11 | | 1 | 1 | | 2 | 30 | | 3 | 4 | | 4 | 4 | | 5 | 2 | | 6 | 8 | | 7 | 12 | | 8 | 2 | | 9 | 37 | | 10 | 6 | | 11 | 11 | | 12 | 15 | | 13 | 10 | | 14 | 22 | | 15 | 21 | | 16 | 2 | | 17 | 1 | | 18 | 2 | | 19 | 10 | | 20 | 10 | | 21 | 10 | | 22 | 9 | | 23 | 8 | | 24 | 7 | | 25 | 11 | | 26 | 10 | | 27 | 8 | | 28 | 11 | | 29 | 4 | | 30 | 2 | | 31 | 2 | | 32 | 6 | | 33 | 4 | | 34 | 12 | | 35 | 10 | | 36 | 10 | | 37 | 6 | | 38 | 15 | | 39 | 18 | | 40 | 4 | | 41 | 9 | | 42 | 5 | | 43 | 2 | | 44 | 7 | | 45 | 6 | | 46 | 22 | | 47 | 3 | | 48 | 5 | | 49 | 15 |
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| 71.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.46534653465346537 | | totalSentences | 101 | | uniqueOpeners | 47 | |
| 99.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 67 | | matches | | 0 | "Instead, I studied the compass" | | 1 | "Instead, I climbed the stairs" |
| | ratio | 0.03 | |
| 70.75% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 67 | | matches | | 0 | "I checked my watch." | | 1 | "I punched in 7-4-2-8 and" | | 2 | "My Maglite cut through the" | | 3 | "I approached slowly, noting the" | | 4 | "I crouched beside the body," | | 5 | "Her pupils were blown wide," | | 6 | "Her disappearance had made the" | | 7 | "I asked, nodding toward her" | | 8 | "He handed me another bag" | | 9 | "I stood, my mind racing" | | 10 | "I kept my voice low," | | 11 | "He nodded, already moving toward" | | 12 | "I didn't answer." | | 13 | "It was spinning, lazy circles" | | 14 | "I heard footsteps on the" | | 15 | "His voice carried the weight" | | 16 | "I gestured toward the body." | | 17 | "I handed him the compass" | | 18 | "He took it carefully, turning" | | 19 | "He read it, his lips" |
| | ratio | 0.373 | |
| 42.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 67 | | matches | | 0 | "The red phone rang three" | | 1 | "The voice on the other" | | 2 | "I checked my watch." | | 3 | "The abandoned Tube station beneath" | | 4 | "The entrance lay hidden behind" | | 5 | "I punched in 7-4-2-8 and" | | 6 | "The door groaned open, revealing" | | 7 | "My Maglite cut through the" | | 8 | "Thompson's voice echoed up from" | | 9 | "The platform stretched longer than" | | 10 | "I approached slowly, noting the" | | 11 | "The way her left hand" | | 12 | "The absence of blood despite" | | 13 | "The positioning of her limbs—too" | | 14 | "Thompson hovered nearby, his flashlight" | | 15 | "I crouched beside the body," | | 16 | "The victim's eyes were open," | | 17 | "Her pupils were blown wide," | | 18 | "Her disappearance had made the" | | 19 | "Marcus Holloway, the eccentric curator" |
| | ratio | 0.836 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 7 | | matches | | 0 | "The door groaned open, revealing stairs that descended into absolute blackness." | | 1 | "The platform stretched longer than I remembered, its curved walls lined with faded advertisements for products that hadn't existed in fifty years." | | 2 | "Inside lay a brass compass, its face etched with symbols that made my skin prickle." | | 3 | "The underground black market that moved locations every full moon." | | 4 | "Holloway making his way toward me, his cane tapping against the ancient tiles." | | 5 | "The old man moved with surprising grace for someone who walked with a limp, his eyes sharp behind wire-rimmed glasses." | | 6 | "The needle was spinning again, faster now, as if excited by my proximity to whatever lay ahead." |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 36 | | tagDensity | 0.083 | | leniency | 0.167 | | rawRatio | 0 | | effectiveRatio | 0 | |