| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.278 | | leniency | 0.556 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.41% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 895 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 66.48% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 895 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "clandestine" | | 1 | "weight" | | 2 | "echoed" | | 3 | "intricate" | | 4 | "scanning" | | 5 | "raced" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 26 | | matches | (empty) | |
| 87.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 26 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 44 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 875 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 570 | | uniqueNames | 10 | | maxNameDensity | 2.11 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Evan | 5 | | Prague | 2 | | Old | 1 | | Eva | 11 | | Rory | 12 | | Cardiff | 6 | | Irish | 1 | | Cardiff-accented | 1 |
| | persons | | 0 | "Raven" | | 1 | "Evan" | | 2 | "Eva" | | 3 | "Rory" |
| | places | | 0 | "Prague" | | 1 | "Cardiff" | | 2 | "Cardiff-accented" |
| | globalScore | 0.447 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 25 | | 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 | 875 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 44 | | matches | (empty) | |
| 98.10% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 62.5 | | std | 30.84 | | cv | 0.493 | | sampleLengths | | 0 | 13 | | 1 | 121 | | 2 | 99 | | 3 | 33 | | 4 | 82 | | 5 | 45 | | 6 | 65 | | 7 | 68 | | 8 | 99 | | 9 | 53 | | 10 | 40 | | 11 | 37 | | 12 | 29 | | 13 | 91 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 26 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 91 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 2 | | flaggedSentences | 9 | | totalSentences | 44 | | ratio | 0.205 | | matches | | 0 | "She knelt in the hidden back room of The Raven’s Nest, the splintered wood of the cabinet digging into her left palm—she avoided rubbing the crescent scar there, a habit leftover from flinching away from Evan’s grabs." | | 1 | "Silas had hidden his signet ring’s backup mold in the cabinet after his Prague contact vanished last month, and he needed it for a clandestine meeting at dawn; the silver ring’s engravings held a cipher only his old MI6 network could decode." | | 2 | "The bookshelf crashed inward a fraction, and a figure stumbled through—Eva." | | 3 | "The lavender perfume Eva had worn since their Cardiff childhood clashed sharply with the back room’s stale scents, and a leather briefcase dangled from her right hand—stamped with the logo of Rory’s father’s law firm." | | 4 | "Eva’s jaw tightened; she’d always hated secrets, even the small ones they’d shared as teens (like the time they’d snuck into a Cardiff pub to drink cheap cider and complain about their parents)." | | 5 | "She’d left Cardiff in the middle of the night, Eva’s text (“Run to London—my cousin has a couch”) burning in her phone, but she’d never told Eva the full extent of Evan’s abuse—just that he’d gotten “too rough.” Eva’s voice softened, a crack in her barrister’s poise." | | 6 | "He didn’t speak—he never interrupted a private conversation unless it threatened his network—but his signet ring tapped faster against his glass, a silent warning that time was running out for his dawn meeting." | | 7 | "Eva’s face fell, and she pulled a crumpled letter from her briefcase—addressed to Rory in their father’s neat handwriting." | | 8 | "A loud pound shook the bar’s front door, followed by a yell—rough, Cardiff-accented, the same voice Rory had heard yelling her name at the train station two years ago." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 269 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.03345724907063197 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0037174721189591076 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 44 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 44 | | mean | 19.89 | | std | 11.58 | | cv | 0.582 | | sampleLengths | | 0 | 13 | | 1 | 37 | | 2 | 42 | | 3 | 24 | | 4 | 18 | | 5 | 11 | | 6 | 15 | | 7 | 38 | | 8 | 35 | | 9 | 5 | | 10 | 19 | | 11 | 9 | | 12 | 15 | | 13 | 33 | | 14 | 34 | | 15 | 19 | | 16 | 9 | | 17 | 8 | | 18 | 9 | | 19 | 18 | | 20 | 47 | | 21 | 41 | | 22 | 9 | | 23 | 18 | | 24 | 17 | | 25 | 12 | | 26 | 16 | | 27 | 29 | | 28 | 25 | | 29 | 20 | | 30 | 33 | | 31 | 16 | | 32 | 6 | | 33 | 16 | | 34 | 2 | | 35 | 19 | | 36 | 18 | | 37 | 15 | | 38 | 5 | | 39 | 9 | | 40 | 29 | | 41 | 1 | | 42 | 27 | | 43 | 34 |
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| 54.55% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.3409090909090909 | | totalSentences | 44 | | uniqueOpeners | 15 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 25 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 4 | | totalSentences | 25 | | matches | | 0 | "She knelt in the hidden" | | 1 | "She’d left Cardiff in the" | | 2 | "She’d traded her pre-law textbooks" | | 3 | "He didn’t speak—he never interrupted" |
| | ratio | 0.16 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 24 | | totalSentences | 25 | | matches | | 0 | "Rory jammed a butter knife" | | 1 | "She knelt in the hidden" | | 2 | "Silas had hidden his signet" | | 3 | "The room reeked of yellowed" | | 4 | "The bookshelf crashed inward a" | | 5 | "Rory froze, butter knife still" | | 6 | "Eva wore a tailored charcoal" | | 7 | "The lavender perfume Eva had" | | 8 | "Rory’s question hung in the" | | 9 | "Eva’s jaw tightened; she’d always" | | 10 | "Eva said, setting her briefcase" | | 11 | "Rory’s left hand curled into" | | 12 | "She’d left Cardiff in the" | | 13 | "The cabinet door finally gave" | | 14 | "The signet ring mold rolled" | | 15 | "Rory grabbed it, her fingers" | | 16 | "She’d traded her pre-law textbooks" | | 17 | "Eva had traded their cider-fueled" | | 18 | "He didn’t speak—he never interrupted" | | 19 | "Rory said, tucking the mold" |
| | ratio | 0.96 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 25 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 1 | | matches | | 0 | "Eva wore a tailored charcoal barrister’s suit (the kind Rory had once dreamed of owning before Evan derailed her pre-law degree), her auburn hair pulled into a …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0 | | effectiveRatio | 0 | |