| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.458 | | leniency | 0.917 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1258 | | totalAiIsmAdverbs | 2 | | 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) | |
| 56.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1258 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "tension" | | 1 | "warmth" | | 2 | "lilt" | | 3 | "echoed" | | 4 | "eyebrow" | | 5 | "pawn" | | 6 | "whisper" | | 7 | "porcelain" | | 8 | "vibrated" | | 9 | "shattered" | | 10 | "silence" |
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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 | 1 | | narrationSentences | 60 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 73 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 57 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1258 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 59 | | wordCount | 1035 | | uniqueNames | 13 | | maxNameDensity | 2.32 | | worstName | "Aurora" | | maxWindowNameDensity | 4 | | worstWindowName | "Aurora" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 24 | | Eva | 16 | | Evan | 2 | | Belfast | 1 | | Cardiff | 2 | | Silas | 6 | | Prague | 2 | | Service | 1 | | Soho | 1 | | Golden | 1 | | Empress | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Eva" | | 4 | "Evan" | | 5 | "Silas" |
| | places | | 0 | "Belfast" | | 1 | "Cardiff" | | 2 | "Prague" | | 3 | "Soho" |
| | globalScore | 0.341 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | 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 | 1258 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 73 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 41.93 | | std | 28.72 | | cv | 0.685 | | sampleLengths | | 0 | 81 | | 1 | 100 | | 2 | 1 | | 3 | 22 | | 4 | 7 | | 5 | 62 | | 6 | 35 | | 7 | 24 | | 8 | 50 | | 9 | 76 | | 10 | 10 | | 11 | 26 | | 12 | 47 | | 13 | 74 | | 14 | 19 | | 15 | 40 | | 16 | 75 | | 17 | 11 | | 18 | 106 | | 19 | 56 | | 20 | 62 | | 21 | 42 | | 22 | 67 | | 23 | 41 | | 24 | 15 | | 25 | 11 | | 26 | 14 | | 27 | 57 | | 28 | 11 | | 29 | 16 |
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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 | 168 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 73 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1038 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.027938342967244702 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005780346820809248 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 73 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 73 | | mean | 17.23 | | std | 11.79 | | cv | 0.684 | | sampleLengths | | 0 | 27 | | 1 | 23 | | 2 | 17 | | 3 | 14 | | 4 | 18 | | 5 | 30 | | 6 | 23 | | 7 | 29 | | 8 | 1 | | 9 | 9 | | 10 | 13 | | 11 | 7 | | 12 | 4 | | 13 | 31 | | 14 | 27 | | 15 | 30 | | 16 | 5 | | 17 | 4 | | 18 | 17 | | 19 | 3 | | 20 | 17 | | 21 | 17 | | 22 | 16 | | 23 | 14 | | 24 | 13 | | 25 | 49 | | 26 | 5 | | 27 | 5 | | 28 | 9 | | 29 | 17 | | 30 | 4 | | 31 | 7 | | 32 | 36 | | 33 | 19 | | 34 | 32 | | 35 | 23 | | 36 | 5 | | 37 | 9 | | 38 | 5 | | 39 | 20 | | 40 | 20 | | 41 | 10 | | 42 | 41 | | 43 | 24 | | 44 | 6 | | 45 | 5 | | 46 | 53 | | 47 | 28 | | 48 | 25 | | 49 | 8 |
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| 58.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3835616438356164 | | totalSentences | 73 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 81.82% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 55 | | matches | | 0 | "She reached for a polishing" | | 1 | "She wore a charcoal wool" | | 2 | "Her hair, once chestnut and" | | 3 | "She held a tumbler of" | | 4 | "She set the crate down" | | 5 | "Her eyes, green and sharp" | | 6 | "She wore no makeup, her" | | 7 | "She had not heard that" | | 8 | "Her voice cracked on the" | | 9 | "She wiped her mouth with" | | 10 | "It glinted under the bar" | | 11 | "Her voice came out thin." | | 12 | "She adjusted her coat, the" | | 13 | "She thought of her flat" | | 14 | "She thought of the delivery" | | 15 | "His eyes swept the room," | | 16 | "His voice was soft, educated," | | 17 | "He smiled, revealing teeth too" | | 18 | "He carried a tea tray" |
| | ratio | 0.345 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 55 | | matches | | 0 | "Aurora balanced the wooden crate" | | 1 | "The Raven's Nest smelled of" | | 2 | "She reached for a polishing" | | 3 | "She wore a charcoal wool" | | 4 | "The sleeves were pushed up" | | 5 | "Her hair, once chestnut and" | | 6 | "She held a tumbler of" | | 7 | "The name lodged in Aurora's" | | 8 | "She set the crate down" | | 9 | "Eva turned her head." | | 10 | "Her eyes, green and sharp" | | 11 | "A muscle jumped in her" | | 12 | "The word came out flat," | | 13 | "Aurora touched the fabric." | | 14 | "Eva had given it to" | | 15 | "Eva set the whisky down" | | 16 | "She wore no makeup, her" | | 17 | "The mention of his name" | | 18 | "Aurora's fingers curled around the" | | 19 | "She had not heard that" |
| | ratio | 0.945 | |
| 90.91% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 55 | | matches | | 0 | "Before Eva could answer, the" |
| | ratio | 0.018 | |
| 61.22% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 4 | | matches | | 0 | "She wore a charcoal wool coat with sharp shoulders, the kind that cost more than Aurora's monthly rent." | | 1 | "She held a tumbler of whisky like a weapon, her thumb resting on the rim, her eyes fixed on the door with a tension that made Aurora's skin prickle." | | 2 | "A man entered, tall and thin, wearing a charcoal grey overcoat that hung from his narrow shoulders like a shroud." | | 3 | "But Eva was already moving, knocking over the corner stool with a crash that shattered the silence as she pulled the pistol from her waistband, her face transfo…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |