| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 17 | | tagDensity | 0.235 | | leniency | 0.471 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1138 | | 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) | |
| 56.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1138 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "flicker" | | 1 | "weight" | | 2 | "racing" | | 3 | "stomach" | | 4 | "lurch" | | 5 | "glinting" | | 6 | "lurching" | | 7 | "echoes" |
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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 | 52 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 52 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 68 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1122 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 69.15% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 804 | | uniqueNames | 11 | | maxNameDensity | 1.62 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Lucien" | | discoveredNames | | Eva | 8 | | Lucien | 12 | | Golden | 3 | | Empress | 3 | | Ptolemy | 4 | | Rory | 13 | | Camden | 1 | | Evan | 5 | | Cardiff | 1 | | Irish | 1 | | London | 1 |
| | persons | | 0 | "Eva" | | 1 | "Lucien" | | 2 | "Rory" | | 3 | "Evan" |
| | places | | 0 | "Camden" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0.692 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | 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 | 1122 | | matches | (empty) | |
| 68.63% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 68 | | matches | | 0 | "seen that glow" | | 1 | "tearing that choice" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 32.06 | | std | 20.64 | | cv | 0.644 | | sampleLengths | | 0 | 19 | | 1 | 68 | | 2 | 1 | | 3 | 3 | | 4 | 54 | | 5 | 11 | | 6 | 19 | | 7 | 78 | | 8 | 16 | | 9 | 34 | | 10 | 34 | | 11 | 35 | | 12 | 2 | | 13 | 55 | | 14 | 21 | | 15 | 11 | | 16 | 70 | | 17 | 42 | | 18 | 27 | | 19 | 53 | | 20 | 13 | | 21 | 27 | | 22 | 39 | | 23 | 58 | | 24 | 7 | | 25 | 25 | | 26 | 50 | | 27 | 34 | | 28 | 59 | | 29 | 6 | | 30 | 36 | | 31 | 18 | | 32 | 30 | | 33 | 48 | | 34 | 19 |
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| 98.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 52 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 115 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 2 | | flaggedSentences | 6 | | totalSentences | 68 | | ratio | 0.088 | | matches | | 0 | "Lucien’s heterochromatic eyes—one amber, one black—catch the neon curry house sign’s flicker through the gap." | | 1 | "Rory’s breath catches; she scratches her wrist’s scar, the old childhood accident’s ghost pain flaring." | | 2 | "The flat’s cramped space closes in—every surface stacked with books, scrolls tied with twine, Eva’s half-eaten plate of curry on the coffee table." | | 3 | "The revelation makes her head spin; she sits on the couch’s edge, the ink-stained armrest digging into her back." | | 4 | "The curry house’s clatters drift up—plates clinking, a chef yelling about overcooked naan—mixing with the faint hissing of Ptolemy from under the sink." | | 5 | "Her pre-law plans had been ruined long before Evan’s abuse—she’d hated every lecture, every textbook, every moment she’d tried to live up to her father’s barrister legacy." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 400 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.02 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 68 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 68 | | mean | 16.5 | | std | 9.03 | | cv | 0.547 | | sampleLengths | | 0 | 19 | | 1 | 11 | | 2 | 37 | | 3 | 20 | | 4 | 1 | | 5 | 3 | | 6 | 13 | | 7 | 15 | | 8 | 26 | | 9 | 11 | | 10 | 19 | | 11 | 18 | | 12 | 15 | | 13 | 45 | | 14 | 16 | | 15 | 17 | | 16 | 17 | | 17 | 34 | | 18 | 2 | | 19 | 17 | | 20 | 16 | | 21 | 2 | | 22 | 18 | | 23 | 20 | | 24 | 17 | | 25 | 7 | | 26 | 14 | | 27 | 11 | | 28 | 24 | | 29 | 23 | | 30 | 23 | | 31 | 20 | | 32 | 22 | | 33 | 27 | | 34 | 7 | | 35 | 16 | | 36 | 26 | | 37 | 4 | | 38 | 13 | | 39 | 13 | | 40 | 14 | | 41 | 39 | | 42 | 3 | | 43 | 13 | | 44 | 19 | | 45 | 23 | | 46 | 7 | | 47 | 25 | | 48 | 2 | | 49 | 27 |
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| 60.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.38235294117647056 | | totalSentences | 68 | | uniqueOpeners | 26 | |
| 66.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 50 | | matches | | 0 | "Then another, harder, the wood" |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 50 | | matches | | 0 | "His tailored charcoal suit fits" | | 1 | "She’d found three dead tabbies" | | 2 | "She’d blamed a stray dog," | | 3 | "It’s a flier for an" | | 4 | "She hesitates, then yanks the" | | 5 | "She ignores it, flipping through" | | 6 | "She’d seen that glow once" | | 7 | "He taps the crescent scar" | | 8 | "She’d never known her Irish" | | 9 | "Her pre-law plans had been" | | 10 | "She could use her Golden" | | 11 | "He slices his palm, black" | | 12 | "She’d never seen anything like" |
| | ratio | 0.26 | |
| 30.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 50 | | matches | | 0 | "Rory slams Eva’s flat door" | | 1 | "The three deadbolts she’d fumbled" | | 2 | "Ptolemy, Eva’s tabby cat, skitters" | | 3 | "Rory’s fingers tighten on the" | | 4 | "Lucien’s heterochromatic eyes—one amber, one" | | 5 | "His tailored charcoal suit fits" | | 6 | "The word hangs between them," | | 7 | "Rory’s breath catches; she scratches" | | 8 | "Lucien’s cane shifts, the thin" | | 9 | "The curry house’s clock ticks-tock" | | 10 | "She’d found three dead tabbies" | | 11 | "She’d blamed a stray dog," | | 12 | "Lucien pulls a crumpled piece" | | 13 | "It’s a flier for an" | | 14 | "A handwritten note scrawled in" | | 15 | "Rory snatches the flier, her" | | 16 | "The paper reeks of sulfur," | | 17 | "She hesitates, then yanks the" | | 18 | "The flat’s cramped space closes" | | 19 | "Ink smudges stain the couch’s" |
| | ratio | 0.86 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 50 | | matches | | 0 | "Now, she knows better." | | 1 | "Before she can speak, a" |
| | ratio | 0.04 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 2 | | matches | | 0 | "The curry house’s clock ticks-tock above the bar below, a steady beat that matches Rory’s racing heart." | | 1 | "He taps the crescent scar once, a gentle motion that makes her skin prickle." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 91.18% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "Last time they’d (would)" |
| | dialogueSentences | 17 | | tagDensity | 0.059 | | leniency | 0.118 | | rawRatio | 1 | | effectiveRatio | 0.118 | |