| 75.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 2 | | adverbTags | | 0 | "She gestured vaguely [vaguely]" | | 1 | "Eva’s breath came fast [fast]" |
| | dialogueSentences | 32 | | tagDensity | 0.281 | | leniency | 0.563 | | rawRatio | 0.222 | | effectiveRatio | 0.125 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 968 | | 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) | |
| 27.69% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 968 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "flicker" | | 1 | "silence" | | 2 | "flicked" | | 3 | "echoed" | | 4 | "whisper" | | 5 | "flickered" | | 6 | "trembled" | | 7 | "tracing" | | 8 | "pulse" | | 9 | "grave" | | 10 | "pounding" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 77 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 77 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 23 | | markdownWords | 24 | | totalWords | 963 | | ratio | 0.025 | | matches | | 0 | "attuned" | | 1 | "waiting" | | 2 | "trap" | | 3 | "Us" | | 4 | "cutting out" | | 5 | "angry" | | 6 | "Look." | | 7 | "moved" | | 8 | "changing" | | 9 | "paint" | | 10 | "breathing" | | 11 | "move" | | 12 | "shifted" | | 13 | "Evie." | | 14 | "shifted" | | 15 | "moved" | | 16 | "lift" | | 17 | "bone" | | 18 | "tilted" | | 19 | "peeled" | | 20 | "screamed" | | 21 | "opening" | | 22 | "finished" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 36.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 750 | | uniqueNames | 7 | | maxNameDensity | 2.27 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Eva" | | discoveredNames | | Quinn | 17 | | Tube | 1 | | Kowalski | 1 | | Eva | 16 | | Veil | 1 | | Market | 1 | | Camden | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Kowalski" | | 2 | "Eva" | | 3 | "Market" |
| | places | (empty) | | globalScore | 0.367 | | windowScore | 0.5 | |
| 50.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 2 | | matches | | 0 | "smelled like copper and old blood" | | 1 | "looked like old ledgers—some torn, some s" |
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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 | 963 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 98 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 24.08 | | std | 13.59 | | cv | 0.565 | | sampleLengths | | 0 | 1 | | 1 | 49 | | 2 | 21 | | 3 | 26 | | 4 | 47 | | 5 | 32 | | 6 | 49 | | 7 | 21 | | 8 | 21 | | 9 | 30 | | 10 | 25 | | 11 | 35 | | 12 | 31 | | 13 | 4 | | 14 | 38 | | 15 | 9 | | 16 | 16 | | 17 | 34 | | 18 | 37 | | 19 | 36 | | 20 | 13 | | 21 | 18 | | 22 | 38 | | 23 | 4 | | 24 | 42 | | 25 | 27 | | 26 | 40 | | 27 | 3 | | 28 | 36 | | 29 | 4 | | 30 | 22 | | 31 | 14 | | 32 | 15 | | 33 | 11 | | 34 | 40 | | 35 | 9 | | 36 | 23 | | 37 | 14 | | 38 | 17 | | 39 | 11 |
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| 91.59% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 77 | | matches | | 0 | "were smeared" | | 1 | "been overlaid" | | 2 | "was swallowed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 117 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 98 | | ratio | 0.061 | | matches | | 0 | "The sigils on its face were smeared with something darker than rust—something that smelled like copper and old blood." | | 1 | "The detective’s peripheral vision caught the shift—a man stepping into the dim light of the abandoned Tube station, his satchel slung over one shoulder." | | 2 | "Quinn’s gaze flicked to the coat, then to the scattered pages of what looked like old ledgers—some torn, some still bound, all covered in the same backward script." | | 3 | "Not a flicker—*cutting out*." | | 4 | "And if it was shifting here—" | | 5 | "Quinn’s boot found purchase on something solid—*bone*." |
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| 88.49% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 756 | | adjectiveStacks | 1 | | stackExamples | | 0 | "unseen pressed against it" |
| | adverbCount | 35 | | adverbRatio | 0.046296296296296294 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007936507936507936 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 9.82 | | std | 6.61 | | cv | 0.674 | | sampleLengths | | 0 | 19 | | 1 | 11 | | 2 | 19 | | 3 | 21 | | 4 | 4 | | 5 | 22 | | 6 | 4 | | 7 | 24 | | 8 | 13 | | 9 | 6 | | 10 | 6 | | 11 | 26 | | 12 | 13 | | 13 | 26 | | 14 | 10 | | 15 | 11 | | 16 | 5 | | 17 | 5 | | 18 | 2 | | 19 | 5 | | 20 | 12 | | 21 | 2 | | 22 | 20 | | 23 | 10 | | 24 | 11 | | 25 | 10 | | 26 | 4 | | 27 | 28 | | 28 | 7 | | 29 | 5 | | 30 | 11 | | 31 | 15 | | 32 | 4 | | 33 | 4 | | 34 | 2 | | 35 | 18 | | 36 | 14 | | 37 | 6 | | 38 | 3 | | 39 | 10 | | 40 | 3 | | 41 | 3 | | 42 | 10 | | 43 | 20 | | 44 | 4 | | 45 | 7 | | 46 | 18 | | 47 | 10 | | 48 | 2 | | 49 | 6 |
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| 41.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.2755102040816326 | | totalSentences | 98 | | uniqueOpeners | 27 | |
| 91.32% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 73 | | matches | | 0 | "Then the lights flickered." | | 1 | "Then the ground *moved*." |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 73 | | matches | | 0 | "She gestured vaguely at the" | | 1 | "She sounded *angry*." | | 2 | "She dropped to her knees," | | 3 | "Her voice cracked" | | 4 | "She’d seen this before." | | 5 | "Her voice cut through the" | | 6 | "She didn’t have to." | | 7 | "She snatched it up, her" | | 8 | "She bolted for the nearest" | | 9 | "They didn’t look back." | | 10 | "She was already pulling out" | | 11 | "She already knew who’d be" |
| | ratio | 0.164 | |
| 28.49% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 73 | | matches | | 0 | "The brass compass lay half-buried" | | 1 | "Detective Quinn’s boot pressed down" | | 2 | "The sigils on its face" | | 3 | "The voice came from the" | | 4 | "Quinn didn’t look up." | | 5 | "A flicker of movement." | | 6 | "The detective’s peripheral vision caught" | | 7 | "Eva Kowalski adjusted her glasses," | | 8 | "Quinn’s fingers twitched toward her" | | 9 | "Eva exhaled through her nose," | | 10 | "She gestured vaguely at the" | | 11 | "Quinn crouched lower, brushing dirt" | | 12 | "The needle had stopped moving." | | 13 | "The air between them thickened." | | 14 | "Eva’s fingers curled around the" | | 15 | "Quinn’s voice dropped, rough as" | | 16 | "The words hung between them," | | 17 | "Eva’s breath hitched, just once," | | 18 | "Quinn’s gaze flicked to the" | | 19 | "A sharp intake of breath." |
| | ratio | 0.863 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 73 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 3 | | matches | | 0 | "She dropped to her knees, her fingers tracing the fresh graffiti now pulsing with a sickly green light." | | 1 | "The air reeked of ozone and something older, something that made her teeth ache." | | 2 | "She was already pulling out her phone, her fingers flying over the screen." |
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| 13.89% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn’s hand shot out, fingers closing around Eva’s wrist" | | 1 | "Eva’s breath came fast, her fingers still pressed against the wall" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 32 | | tagDensity | 0.031 | | leniency | 0.063 | | rawRatio | 1 | | effectiveRatio | 0.063 | |