| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 64 | | tagDensity | 0.328 | | leniency | 0.656 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.85% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 971 | | 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) | |
| 84.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 971 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "weight" | | 1 | "potential" | | 2 | "reminder" |
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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 | 60 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 60 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 103 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 971 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 544 | | uniqueNames | 7 | | maxNameDensity | 0.74 | | worstName | "Silas" | | maxWindowNameDensity | 1.5 | | worstWindowName | "James" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Silas | 4 | | James | 4 | | Gold | 1 | | Rory | 3 | | Cardiff | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "James" | | 4 | "Rory" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 21.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | glossingSentenceCount | 2 | | matches | | 0 | "quite healed" | | 1 | "appeared beside her, silent as always" |
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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 | 971 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 103 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 63 | | mean | 15.41 | | std | 14.35 | | cv | 0.931 | | sampleLengths | | 0 | 41 | | 1 | 6 | | 2 | 58 | | 3 | 1 | | 4 | 25 | | 5 | 11 | | 6 | 11 | | 7 | 36 | | 8 | 6 | | 9 | 16 | | 10 | 41 | | 11 | 5 | | 12 | 23 | | 13 | 7 | | 14 | 11 | | 15 | 25 | | 16 | 2 | | 17 | 36 | | 18 | 5 | | 19 | 12 | | 20 | 55 | | 21 | 3 | | 22 | 6 | | 23 | 24 | | 24 | 9 | | 25 | 1 | | 26 | 20 | | 27 | 2 | | 28 | 19 | | 29 | 43 | | 30 | 7 | | 31 | 3 | | 32 | 21 | | 33 | 23 | | 34 | 2 | | 35 | 21 | | 36 | 9 | | 37 | 3 | | 38 | 4 | | 39 | 56 | | 40 | 9 | | 41 | 7 | | 42 | 5 | | 43 | 15 | | 44 | 17 | | 45 | 25 | | 46 | 21 | | 47 | 2 | | 48 | 14 | | 49 | 11 |
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| 99.42% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 60 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 102 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 103 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 549 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.030965391621129327 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.007285974499089253 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 103 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 103 | | mean | 9.43 | | std | 7.51 | | cv | 0.796 | | sampleLengths | | 0 | 10 | | 1 | 19 | | 2 | 12 | | 3 | 6 | | 4 | 21 | | 5 | 13 | | 6 | 3 | | 7 | 21 | | 8 | 1 | | 9 | 5 | | 10 | 20 | | 11 | 9 | | 12 | 2 | | 13 | 5 | | 14 | 6 | | 15 | 17 | | 16 | 19 | | 17 | 6 | | 18 | 14 | | 19 | 2 | | 20 | 11 | | 21 | 23 | | 22 | 2 | | 23 | 2 | | 24 | 3 | | 25 | 5 | | 26 | 20 | | 27 | 1 | | 28 | 2 | | 29 | 7 | | 30 | 11 | | 31 | 12 | | 32 | 13 | | 33 | 2 | | 34 | 17 | | 35 | 19 | | 36 | 5 | | 37 | 5 | | 38 | 7 | | 39 | 6 | | 40 | 38 | | 41 | 4 | | 42 | 7 | | 43 | 3 | | 44 | 6 | | 45 | 7 | | 46 | 12 | | 47 | 5 | | 48 | 9 | | 49 | 1 |
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| 74.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.4563106796116505 | | totalSentences | 103 | | uniqueOpeners | 47 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 49 | | matches | (empty) | | ratio | 0 | |
| 15.92% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 49 | | matches | | 0 | "She didn't recognise him at" | | 1 | "His hair had receded, what" | | 2 | "He stopped mid-step, head turning." | | 3 | "His voice cracked on the" | | 4 | "She set down the cloth." | | 5 | "He slid onto a stool," | | 6 | "He gestured at the green" | | 7 | "He took in James with" | | 8 | "He said it like she'd" | | 9 | "He pulled at his collar," | | 10 | "His laugh came out hollow" | | 11 | "He held up his left" | | 12 | "She poured him a whiskey" | | 13 | "He wrapped his fingers around" | | 14 | "He drank the whiskey in" | | 15 | "They'd been eighteen, invincible, full" | | 16 | "She touched the scar on" | | 17 | "She said it flatly, the" | | 18 | "His voice rose, drawing a" | | 19 | "She refilled his glass" |
| | ratio | 0.51 | |
| 0.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 49 | | matches | | 0 | "The Raven's Nest smelled of" | | 1 | "A jazz record crackled through" | | 2 | "Rory wiped down the bar" | | 3 | "She didn't recognise him at" | | 4 | "The man who walked in" | | 5 | "His hair had receded, what" | | 6 | "That loose, easy stride that" | | 7 | "He stopped mid-step, head turning." | | 8 | "Those eyes still had the" | | 9 | "His voice cracked on the" | | 10 | "She set down the cloth." | | 11 | "He slid onto a stool," | | 12 | "He gestured at the green" | | 13 | "Silas emerged from the back" | | 14 | "He took in James with" | | 15 | "Silas nodded once, retreated to" | | 16 | "He said it like she'd" | | 17 | "He pulled at his collar," | | 18 | "The air between them thickened." | | 19 | "The lie sat heavy on" |
| | ratio | 0.918 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 49 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 3 | | matches | | 0 | "The man who walked in wore a thousand-pound suit, charcoal grey, cut to hide the weight he'd put on since university." | | 1 | "He took in James with a single sweep of his hazel eyes, cataloguing him the way he catalogued everyone who entered his bar." | | 2 | "Silas nodded once, retreated to the far end of the bar, and began polishing a glass that didn't need polishing." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 1 | | matches | | 0 | "He slid, the leather creaking under him" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 64 | | tagDensity | 0.141 | | leniency | 0.281 | | rawRatio | 0 | | effectiveRatio | 0 | |