| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "Harlow said quietly [quietly]" |
| | dialogueSentences | 25 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.2 | | effectiveRatio | 0.08 | |
| 91.36% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 579 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 82.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 579 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 57 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 57 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 74 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 7 | | totalWords | 567 | | ratio | 0.012 | | matches | | 0 | "Veil Market territory." | | 1 | "“You shouldn’t have come.”" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 18.71% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 457 | | uniqueNames | 7 | | maxNameDensity | 2.63 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 12 | | Quinn | 1 | | Market | 2 | | Morris | 1 | | Cole | 11 | | Tube | 1 | | Veil | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Cole" |
| | places | | | globalScore | 0.187 | | windowScore | 0.333 | |
| 66.67% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 30 | | glossingSentenceCount | 1 | | matches | | 0 | "as if reaching for something just out of sight" |
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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 | 567 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 17.18 | | std | 15.83 | | cv | 0.921 | | sampleLengths | | 0 | 1 | | 1 | 55 | | 2 | 41 | | 3 | 11 | | 4 | 47 | | 5 | 29 | | 6 | 12 | | 7 | 3 | | 8 | 56 | | 9 | 6 | | 10 | 3 | | 11 | 9 | | 12 | 11 | | 13 | 11 | | 14 | 33 | | 15 | 14 | | 16 | 12 | | 17 | 46 | | 18 | 5 | | 19 | 7 | | 20 | 25 | | 21 | 11 | | 22 | 27 | | 23 | 5 | | 24 | 21 | | 25 | 4 | | 26 | 23 | | 27 | 14 | | 28 | 10 | | 29 | 2 | | 30 | 7 | | 31 | 2 | | 32 | 4 |
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| 80.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 57 | | matches | | 0 | "been scrubbed" | | 1 | "been posed" | | 2 | "were twisted" | | 3 | "being pulled" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 84 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 74 | | ratio | 0.095 | | matches | | 0 | "The air smelled of wet earth and something older—something that didn’t belong in the modern world." | | 1 | "The usual graffiti had been scrubbed away, replaced by something worse—symbols carved into the plaster, their edges still wet." | | 2 | "The victim—a woman, mid-thirties, dressed in a tailored blazer—had been posed like a broken marionette." | | 3 | "His fingers hovered over the woman’s wrist, where a faint, glowing sigil pulsed beneath the skin—visible only under the right light." | | 4 | "The rails were slick with something dark and viscous, not blood—something thicker, shimmering faintly in the dim light." | | 5 | "A sound echoed through the station—a wet, dragging noise, like something being pulled across the ground." | | 6 | "The thing smiled—a lipless stretch of darkness." |
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| 97.77% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 470 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.0425531914893617 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.010638297872340425 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 74 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 74 | | mean | 7.65 | | std | 5.26 | | cv | 0.688 | | sampleLengths | | 0 | 13 | | 1 | 9 | | 2 | 16 | | 3 | 14 | | 4 | 3 | | 5 | 16 | | 6 | 19 | | 7 | 6 | | 8 | 4 | | 9 | 7 | | 10 | 19 | | 11 | 19 | | 12 | 9 | | 13 | 9 | | 14 | 11 | | 15 | 9 | | 16 | 6 | | 17 | 6 | | 18 | 2 | | 19 | 1 | | 20 | 3 | | 21 | 19 | | 22 | 15 | | 23 | 19 | | 24 | 6 | | 25 | 2 | | 26 | 1 | | 27 | 6 | | 28 | 3 | | 29 | 3 | | 30 | 8 | | 31 | 5 | | 32 | 6 | | 33 | 7 | | 34 | 21 | | 35 | 5 | | 36 | 6 | | 37 | 8 | | 38 | 3 | | 39 | 9 | | 40 | 3 | | 41 | 12 | | 42 | 18 | | 43 | 8 | | 44 | 5 | | 45 | 5 | | 46 | 5 | | 47 | 2 | | 48 | 16 | | 49 | 6 |
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| 53.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.33783783783783783 | | totalSentences | 74 | | uniqueOpeners | 25 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 51 | | matches | | 0 | "Instead, she turned toward the" | | 1 | "Then it spoke, in a" | | 2 | "Then it lunged." |
| | ratio | 0.059 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 51 | | matches | | 0 | "Her leather-gloved hand closed around" | | 1 | "His boot scuffed against a" | | 2 | "She didn’t answer." | | 3 | "Her limbs were twisted at" | | 4 | "His fingers hovered over the" | | 5 | "She was already moving, her" | | 6 | "She knelt, brushing her fingers" | | 7 | "It resisted, clinging like tar." | | 8 | "She didn’t need to." | | 9 | "She already knew." | | 10 | "It wasn’t human." | | 11 | "She stood slowly, her gaze" |
| | ratio | 0.235 | |
| 9.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 51 | | matches | | 0 | "The bone token clattered against" | | 1 | "Detective Harlow Quinn crouched, fingers" | | 2 | "The air smelled of wet" | | 3 | "Her leather-gloved hand closed around" | | 4 | "*Veil Market territory.*" | | 5 | "The voice came from behind" | | 6 | "DS Morris’s replacement, a fresh-faced" | | 7 | "Harlow didn’t look up." | | 8 | "The abandoned Tube station yawned" | | 9 | "The usual graffiti had been" | | 10 | "A summoning circle, half-finished, its" | | 11 | "Cole muttered, stepping forward" | | 12 | "His boot scuffed against a" | | 13 | "Harlow stood, her sharp jaw" | | 14 | "She didn’t answer." | | 15 | "The victim—a woman, mid-thirties, dressed" | | 16 | "Her limbs were twisted at" | | 17 | "Harlow said quietly" | | 18 | "Harlow gestured to the body" | | 19 | "Cole’s radio crackled." |
| | ratio | 0.902 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 2 | | matches | | 0 | "The air smelled of wet earth and something older—something that didn’t belong in the modern world." | | 1 | "Her limbs were twisted at unnatural angles, her fingers splayed as if reaching for something just out of sight." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 70.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Cole muttered (mutter)" | | 1 | "Cole whispered (whisper)" |
| | dialogueSentences | 25 | | tagDensity | 0.12 | | leniency | 0.24 | | rawRatio | 0.667 | | effectiveRatio | 0.16 | |