| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "She could almost [almost]" |
| | dialogueSentences | 24 | | tagDensity | 0.625 | | leniency | 1 | | rawRatio | 0.067 | | effectiveRatio | 0.067 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1039 | | 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) | |
| 71.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1039 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "etched" | | 1 | "scanned" | | 2 | "tracing" | | 3 | "vibrated" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "clenched jaw/fists" | | count | 1 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "clenched fist" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 61 | | matches | (empty) | |
| 96.02% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 61 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 71 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1024 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 63.15% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 806 | | uniqueNames | 11 | | maxNameDensity | 1.74 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Veil | 2 | | Market | 2 | | Met | 1 | | Eva | 11 | | Kowalski | 1 | | British | 1 | | Museum | 1 | | Harlow | 14 | | Morris | 5 |
| | persons | | 0 | "Market" | | 1 | "Met" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Museum" | | 5 | "Harlow" | | 6 | "Morris" |
| | places | | | globalScore | 0.632 | | windowScore | 0.833 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like warnings she’d ignored" |
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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 | 1024 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 71 | | matches | (empty) | |
| 96.10% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 10 | | mean | 102.4 | | std | 49.8 | | cv | 0.486 | | sampleLengths | | 0 | 37 | | 1 | 91 | | 2 | 60 | | 3 | 195 | | 4 | 94 | | 5 | 196 | | 6 | 90 | | 7 | 84 | | 8 | 76 | | 9 | 101 |
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| 99.51% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 61 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 139 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 1 | | flaggedSentences | 8 | | totalSentences | 71 | | ratio | 0.113 | | matches | | 0 | "Copper stung her nostrils—blood, recent, thick enough to cling to the damp stone walls of the abandoned Camden platform." | | 1 | "The Met had called Eva Kowalski—occult research assistant at the British Museum’s restricted archives—as a last-ditch consultant; no uniformed officer had dared touch the trinket after two reported sudden, blinding headaches." | | 2 | "Her fingers curled into a fist beneath her glove—she’d never told anyone about those symbols, had written them off as a killer’s sick joke until now." | | 3 | "The Veil Market’s entry requirement was a bone token—she’d heard rumors from informants, though she’d never been able to prove the market’s existence until now." | | 4 | "A small tear in the lining caught her eye—she reached in, pulling out a tiny, weathered bone token." | | 5 | "She’d never connected it to supernatural activity—until now." | | 6 | "The symbols, the trinket, the bone token—all of it tied back to the same hidden world Eva studied." | | 7 | "A cold wind blasts through the tunnel, carrying the scent of burnt sage and something darker—rot, and the faint scream of a voice she recognized as Morris’s." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 628 | | adjectiveStacks | 1 | | stackExamples | | 0 | "weathered leather-bound book" |
| | adverbCount | 7 | | adverbRatio | 0.011146496815286623 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0031847133757961785 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 71 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 71 | | mean | 14.42 | | std | 7.36 | | cv | 0.51 | | sampleLengths | | 0 | 18 | | 1 | 19 | | 2 | 14 | | 3 | 23 | | 4 | 23 | | 5 | 31 | | 6 | 24 | | 7 | 20 | | 8 | 16 | | 9 | 10 | | 10 | 3 | | 11 | 18 | | 12 | 26 | | 13 | 26 | | 14 | 7 | | 15 | 13 | | 16 | 28 | | 17 | 4 | | 18 | 8 | | 19 | 15 | | 20 | 5 | | 21 | 3 | | 22 | 10 | | 23 | 19 | | 24 | 16 | | 25 | 28 | | 26 | 25 | | 27 | 25 | | 28 | 4 | | 29 | 3 | | 30 | 7 | | 31 | 15 | | 32 | 7 | | 33 | 11 | | 34 | 18 | | 35 | 14 | | 36 | 8 | | 37 | 10 | | 38 | 25 | | 39 | 9 | | 40 | 12 | | 41 | 12 | | 42 | 8 | | 43 | 12 | | 44 | 21 | | 45 | 15 | | 46 | 20 | | 47 | 8 | | 48 | 18 | | 49 | 21 |
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| 69.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.43661971830985913 | | totalSentences | 71 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 48 | | matches | (empty) | | ratio | 0 | |
| 36.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 48 | | matches | | 0 | "She stepped down, her worn" | | 1 | "She tucked a strand behind" | | 2 | "She knelt, her sharp jaw" | | 3 | "Her fingers curled into a" | | 4 | "She closed her book, tapping" | | 5 | "She prodded the compass with" | | 6 | "She tucked another hair strand" | | 7 | "She scanned the tunnel, her" | | 8 | "She nodded at the victim’s" | | 9 | "She knelt again, running her" | | 10 | "It bore the same sigils" | | 11 | "She leaned in, her round" | | 12 | "She pointed to the deep" | | 13 | "She flipped open her book" | | 14 | "She’d never connected it to" | | 15 | "She could almost hear Morris’s" | | 16 | "She stared at the tunnel," | | 17 | "She knelt again, tracing the" | | 18 | "It started low, then grew" | | 19 | "It spun wildly for a" |
| | ratio | 0.458 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 48 | | matches | | 0 | "Harlow’s boot crunched through a" | | 1 | "Copper stung her nostrils—blood, recent," | | 2 | "She stepped down, her worn" | | 3 | "The platform reeked of burnt" | | 4 | "A street kid had stumbled" | | 5 | "The Met had called Eva" | | 6 | "Eva huddled by the body," | | 7 | "She tucked a strand behind" | | 8 | "The book’s cover bore the" | | 9 | "Eva said without looking up" | | 10 | "She knelt, her sharp jaw" | | 11 | "Verdigris patina coated its casing," | | 12 | "Her fingers curled into a" | | 13 | "She closed her book, tapping" | | 14 | "She prodded the compass with" | | 15 | "The metal point didn’t budge," | | 16 | "She tucked another hair strand" | | 17 | "Harlow stood, her military precision" | | 18 | "She scanned the tunnel, her" | | 19 | "The Veil Market’s entry requirement" |
| | ratio | 0.958 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 48 | | matches | | 0 | "Now those stories felt like" |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 2 | | matches | | 0 | "Verdigris patina coated its casing, sigils etched into the face that made her recall the unexplained symbols at her partner Morris’s death scene three years pri…" | | 1 | "The air felt thick, as if the platform held its breath, and the distant rumble of a passing train above made the walls shake faintly." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.583 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |