| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 60 | | tagDensity | 0.133 | | leniency | 0.267 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1113 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 46.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1113 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "beacon" | | 1 | "velvet" | | 2 | "pulse" | | 3 | "tension" | | 4 | "scanned" | | 5 | "lilt" | | 6 | "eyebrow" | | 7 | "silence" | | 8 | "pulsed" | | 9 | "traced" |
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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 | 110 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 110 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 162 | | 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 | 1 | | markdownWords | 13 | | totalWords | 1104 | | ratio | 0.012 | | matches | | 0 | "If the world’s on fire, better to be the arsonist than the ash." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 708 | | uniqueNames | 14 | | maxNameDensity | 2.54 | | worstName | "Rory" | | maxWindowNameDensity | 6 | | worstWindowName | "Rory" | | discoveredNames | | Soho | 1 | | Golden | 1 | | Empress | 1 | | Raven | 2 | | Nest | 2 | | Eastern | 1 | | Europe | 1 | | Parliament | 1 | | Prague | 1 | | Rory | 18 | | Welsh | 1 | | Eva | 8 | | Evan | 1 | | Silas | 6 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Rory" | | 3 | "Eva" | | 4 | "Evan" | | 5 | "Silas" |
| | places | | 0 | "Soho" | | 1 | "Eastern" | | 2 | "Europe" | | 3 | "Prague" |
| | globalScore | 0.229 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1104 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 162 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 87 | | mean | 12.69 | | std | 12.12 | | cv | 0.955 | | sampleLengths | | 0 | 52 | | 1 | 53 | | 2 | 45 | | 3 | 30 | | 4 | 13 | | 5 | 20 | | 6 | 4 | | 7 | 14 | | 8 | 13 | | 9 | 28 | | 10 | 6 | | 11 | 2 | | 12 | 3 | | 13 | 28 | | 14 | 10 | | 15 | 10 | | 16 | 12 | | 17 | 11 | | 18 | 4 | | 19 | 1 | | 20 | 4 | | 21 | 6 | | 22 | 32 | | 23 | 3 | | 24 | 31 | | 25 | 8 | | 26 | 38 | | 27 | 2 | | 28 | 14 | | 29 | 1 | | 30 | 6 | | 31 | 4 | | 32 | 2 | | 33 | 10 | | 34 | 5 | | 35 | 7 | | 36 | 12 | | 37 | 6 | | 38 | 9 | | 39 | 13 | | 40 | 34 | | 41 | 10 | | 42 | 7 | | 43 | 5 | | 44 | 16 | | 45 | 12 | | 46 | 33 | | 47 | 3 | | 48 | 47 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 124 | | matches | (empty) | |
| 19.40% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 162 | | ratio | 0.043 | | matches | | 0 | "The neon from The Raven’s Nest bled through the fog—green, steady, a beacon in the grime." | | 1 | "The usual crowd—lifelong regulars nursing pints, a couple whispering in the corner booth, a tourist who clearly thought this was just another dive bar." | | 2 | "Not the full inspection, not tonight—but the quick sweep of a man who still noticed pulse points, stance shifts, micro-tremors in the breath." | | 3 | "Her eyes—still bright blue, still unnervingly direct—landed on Rory." | | 4 | "The way she stood—balanced on the balls of her feet, like she expected a fight." | | 5 | "“Did you?” The woman—Eva—leaned in." | | 6 | "Pulled out a slim silver device—no bigger than a USB drive." |
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| 92.41% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 719 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.048678720445062586 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005563282336578581 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 162 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 162 | | mean | 6.81 | | std | 6.21 | | cv | 0.911 | | sampleLengths | | 0 | 13 | | 1 | 19 | | 2 | 20 | | 3 | 14 | | 4 | 23 | | 5 | 16 | | 6 | 5 | | 7 | 8 | | 8 | 24 | | 9 | 2 | | 10 | 6 | | 11 | 26 | | 12 | 4 | | 13 | 6 | | 14 | 7 | | 15 | 9 | | 16 | 11 | | 17 | 4 | | 18 | 14 | | 19 | 8 | | 20 | 5 | | 21 | 5 | | 22 | 23 | | 23 | 6 | | 24 | 2 | | 25 | 3 | | 26 | 8 | | 27 | 14 | | 28 | 2 | | 29 | 2 | | 30 | 2 | | 31 | 10 | | 32 | 5 | | 33 | 4 | | 34 | 1 | | 35 | 2 | | 36 | 10 | | 37 | 10 | | 38 | 1 | | 39 | 4 | | 40 | 1 | | 41 | 4 | | 42 | 3 | | 43 | 3 | | 44 | 7 | | 45 | 2 | | 46 | 18 | | 47 | 5 | | 48 | 3 | | 49 | 3 |
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| 65.02% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.41975308641975306 | | totalSentences | 162 | | uniqueOpeners | 68 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 78 | | matches | | 0 | "Instead, she reached into her" | | 1 | "Then she walked over." | | 2 | "Then at Eva." |
| | ratio | 0.038 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 78 | | matches | | 0 | "She peeled off the stained" | | 1 | "She pulled her leather jacket" | | 2 | "She pushed the door open." | | 3 | "He didn’t look up." | | 4 | "His voice was low, gravel" | | 5 | "She smiled, but it didn’t" | | 6 | "She opened her mouth to" | | 7 | "His fingers stilled." | | 8 | "Her nails tapped once against" | | 9 | "She looked away." | | 10 | "She wore a tailored coat" | | 11 | "Her eyes—still bright blue, still" | | 12 | "Her voice was different." | | 13 | "It didn’t touch her eyes." | | 14 | "She walked out." | | 15 | "She didn’t answer." | | 16 | "It just finds them." |
| | ratio | 0.218 | |
| 81.79% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 78 | | matches | | 0 | "The knife clattered into the" | | 1 | "Rory wiped her hands on" | | 2 | "She peeled off the stained" | | 3 | "She pulled her leather jacket" | | 4 | "The neon from The Raven’s" | | 5 | "She pushed the door open." | | 6 | "The usual crowd—lifelong regulars nursing" | | 7 | "The Raven’s Nest didn’t serve" | | 8 | "Silas stood behind the bar," | | 9 | "He didn’t look up." | | 10 | "Rory slid onto her usual" | | 11 | "His voice was low, gravel" | | 12 | "She smiled, but it didn’t" | | 13 | "Silas finally looked at her." | | 14 | "She opened her mouth to" | | 15 | "Silas set the glass down." | | 16 | "His fingers stilled." | | 17 | "Her nails tapped once against" | | 18 | "She looked away." | | 19 | "The one with red pins" |
| | ratio | 0.756 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 78 | | matches | | 0 | "Now he picked it up," | | 1 | "Before she could speak, the" |
| | ratio | 0.026 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 1 | | matches | | 0 | "The girl who’d held her hair back when she vomited from Evan’s fists." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 60 | | tagDensity | 0.05 | | leniency | 0.1 | | rawRatio | 0 | | effectiveRatio | 0 | |