| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.243 | | leniency | 0.486 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1537 | | 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) | |
| 2.41% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1537 | | totalAiIsms | 30 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | | | 23 | |
| | highlights | | 0 | "flickered" | | 1 | "footsteps" | | 2 | "trembled" | | 3 | "weight" | | 4 | "glint" | | 5 | "etched" | | 6 | "pulsed" | | 7 | "measured" | | 8 | "flicked" | | 9 | "charged" | | 10 | "could feel" | | 11 | "mosaic" | | 12 | "grappling" | | 13 | "echoing" | | 14 | "cacophony" | | 15 | "silence" | | 16 | "stark" | | 17 | "dancing" | | 18 | "eyebrow" | | 19 | "echoed" | | 20 | "reminder" | | 21 | "flicker" | | 22 | "gloom" | | 23 | "sanctuary" |
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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 | 149 | | matches | (empty) | |
| 85.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 4 | | narrationSentences | 149 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 176 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 8 | | markdownWords | 8 | | totalWords | 1528 | | ratio | 0.005 | | matches | | 0 | "did" | | 1 | "was" | | 2 | "knew" | | 3 | "do" | | 4 | "many" | | 5 | "taken" | | 6 | "eaten" | | 7 | "things" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1253 | | uniqueNames | 11 | | maxNameDensity | 1.6 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 20 | | Quinn | 1 | | Veil | 1 | | Market | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 2 | | Tomás | 15 | | Raven | 1 | | Nest | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Morris" | | 6 | "Tomás" | | 7 | "Raven" |
| | places | (empty) | | globalScore | 0.702 | | windowScore | 0.5 | |
| 95.05% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | glossingSentenceCount | 2 | | matches | | 0 | "pit that seemed to stare back" | | 1 | "seemed longer now, the walls pressing in, the symbols screaming silent warnings" |
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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 | 1528 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 176 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 69 | | mean | 22.14 | | std | 15.91 | | cv | 0.718 | | sampleLengths | | 0 | 48 | | 1 | 10 | | 2 | 42 | | 3 | 17 | | 4 | 51 | | 5 | 61 | | 6 | 50 | | 7 | 23 | | 8 | 9 | | 9 | 50 | | 10 | 8 | | 11 | 41 | | 12 | 13 | | 13 | 25 | | 14 | 15 | | 15 | 26 | | 16 | 17 | | 17 | 39 | | 18 | 32 | | 19 | 3 | | 20 | 11 | | 21 | 30 | | 22 | 32 | | 23 | 38 | | 24 | 3 | | 25 | 5 | | 26 | 56 | | 27 | 27 | | 28 | 42 | | 29 | 30 | | 30 | 22 | | 31 | 11 | | 32 | 11 | | 33 | 9 | | 34 | 30 | | 35 | 20 | | 36 | 9 | | 37 | 7 | | 38 | 30 | | 39 | 2 | | 40 | 48 | | 41 | 3 | | 42 | 50 | | 43 | 3 | | 44 | 31 | | 45 | 1 | | 46 | 34 | | 47 | 39 | | 48 | 4 | | 49 | 22 |
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| 95.84% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 149 | | matches | | 0 | "was gone" | | 1 | "were lined" | | 2 | "were made" | | 3 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 200 | | matches | | 0 | "were exhaling" | | 1 | "was coming" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 176 | | ratio | 0.051 | | matches | | 0 | "And beneath it, a faint vibration—footsteps." | | 1 | "The tunnel below was narrow, the walls lined with moss and the occasional glint of something unnatural—symbols carved into the stone, their edges worn smooth by time." | | 2 | "The walls here were different—smooth, almost polished, as if carved by deliberate hands." | | 3 | "Figures emerged from the shadows—some human, some not." | | 4 | "Harlow’s torchlight caught a glint of metal deeper in—handcuffs, half-buried in the dirt." | | 5 | "She could feel it—the same prickle at the back of her neck she’d felt the night Morris vanished." | | 6 | "The suspect’s footsteps were clearer here, but so were others—too many to count." | | 7 | "His face was wrong—too smooth, too symmetrical, as if he’d shed his skin and left the mask beneath." | | 8 | "Something moved within it—something with too many eyes, too many teeth." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1264 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.03322784810126582 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.006329113924050633 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 176 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 176 | | mean | 8.68 | | std | 6.04 | | cv | 0.696 | | sampleLengths | | 0 | 21 | | 1 | 15 | | 2 | 12 | | 3 | 3 | | 4 | 5 | | 5 | 2 | | 6 | 14 | | 7 | 14 | | 8 | 9 | | 9 | 5 | | 10 | 9 | | 11 | 1 | | 12 | 1 | | 13 | 6 | | 14 | 12 | | 15 | 10 | | 16 | 14 | | 17 | 9 | | 18 | 6 | | 19 | 27 | | 20 | 13 | | 21 | 21 | | 22 | 9 | | 23 | 12 | | 24 | 13 | | 25 | 13 | | 26 | 3 | | 27 | 15 | | 28 | 2 | | 29 | 6 | | 30 | 4 | | 31 | 5 | | 32 | 8 | | 33 | 16 | | 34 | 14 | | 35 | 10 | | 36 | 2 | | 37 | 6 | | 38 | 2 | | 39 | 2 | | 40 | 17 | | 41 | 22 | | 42 | 6 | | 43 | 7 | | 44 | 16 | | 45 | 9 | | 46 | 6 | | 47 | 6 | | 48 | 3 | | 49 | 11 |
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| 39.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.2784090909090909 | | totalSentences | 176 | | uniqueOpeners | 49 | |
| 76.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 130 | | matches | | 0 | "Then the passage opened into" | | 1 | "Then the torch flared back" | | 2 | "Then Tomás’s grip on her" |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 130 | | matches | | 0 | "She gained ground, her sharp" | | 1 | "She dropped to a crouch," | | 2 | "She hesitated, then swung her" | | 3 | "Her torch flickered to life," | | 4 | "She pocketed it without breaking" | | 5 | "His Saint Christopher medallion glinted" | | 6 | "Her voice was low, measured" | | 7 | "He nodded toward a narrow" | | 8 | "He crossed his arms" | | 9 | "She didn’t deny it." | | 10 | "He stepped closer, lowering his" | | 11 | "Her fingers twitched." | | 12 | "She adjusted her grip on" | | 13 | "He jerked his chin toward" | | 14 | "She could feel it—the same" | | 15 | "She stepped forward." | | 16 | "His face was wrong—too smooth," | | 17 | "His smile was a knife" | | 18 | "he said, his voice layered," | | 19 | "She didn’t lower the torch." |
| | ratio | 0.215 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 117 | | totalSentences | 130 | | matches | | 0 | "The pavement spat rainwater into" | | 1 | "The suspect’s silhouette flickered under" | | 2 | "She gained ground, her sharp" | | 3 | "A left turn." | | 4 | "A fire escape groaned above." | | 5 | "Harlow skidded to a halt," | | 6 | "The alley stretched empty, the" | | 7 | "A rusted drain cover, slightly" | | 8 | "She dropped to a crouch," | | 9 | "The drain cover shifted with" | | 10 | "A ladder descended into blackness," | | 11 | "The scent of damp earth" | | 12 | "She hesitated, then swung her" | | 13 | "The ladder trembled under her" | | 14 | "The tunnel below was narrow," | | 15 | "Her torch flickered to life," | | 16 | "The beam caught movement ahead:" | | 17 | "Harlow pursued, her breath steady" | | 18 | "The tunnel opened into a" | | 19 | "The walls here were different—smooth," |
| | ratio | 0.9 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 130 | | matches | (empty) | | ratio | 0 | |
| 46.70% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 7 | | matches | | 0 | "Her torch flickered to life, casting long shadows that danced like restless spirits." | | 1 | "The walls here were different—smooth, almost polished, as if carved by deliberate hands." | | 2 | "The air turned thick, humid, as if the walls themselves were exhaling." | | 3 | "The suspect stood at its centre, his back to her, his hands raised as if in surrender." | | 4 | "His face was wrong—too smooth, too symmetrical, as if he’d shed his skin and left the mask beneath." | | 5 | "Then the torch flared back to life, its beam catching Tomás grappling with the suspect, his medallion swinging wildly." | | 6 | "He snarled, his body writhing unnaturally, as if his bones were made of rubber." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 95.95% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "Tomás hissed (hiss)" | | 1 | "she demanded (demand)" |
| | dialogueSentences | 37 | | tagDensity | 0.108 | | leniency | 0.216 | | rawRatio | 0.5 | | effectiveRatio | 0.108 | |