| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 82.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 850 | | totalAiIsmAdverbs | 3 | | 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) | |
| 58.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 850 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "gloom" | | 1 | "weight" | | 2 | "scanned" | | 3 | "traced" | | 4 | "scanning" | | 5 | "chill" | | 6 | "silence" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 850 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.06% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 850 | | uniqueNames | 12 | | maxNameDensity | 1.06 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Oxford | 2 | | Circus | 1 | | Thai | 1 | | London | 2 | | Aurora | 9 | | Silas | 8 | | Cardiff | 4 | | You | 3 | | Julian | 7 |
| | persons | | 0 | "Aurora" | | 1 | "Silas" | | 2 | "You" | | 3 | "Julian" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Oxford" | | 3 | "London" | | 4 | "Cardiff" |
| | globalScore | 0.971 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | 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 | 850 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 21.79 | | std | 12.78 | | cv | 0.587 | | sampleLengths | | 0 | 38 | | 1 | 35 | | 2 | 45 | | 3 | 17 | | 4 | 21 | | 5 | 16 | | 6 | 36 | | 7 | 8 | | 8 | 35 | | 9 | 5 | | 10 | 55 | | 11 | 1 | | 12 | 28 | | 13 | 34 | | 14 | 2 | | 15 | 13 | | 16 | 7 | | 17 | 27 | | 18 | 16 | | 19 | 10 | | 20 | 15 | | 21 | 25 | | 22 | 21 | | 23 | 39 | | 24 | 20 | | 25 | 27 | | 26 | 10 | | 27 | 19 | | 28 | 11 | | 29 | 37 | | 30 | 36 | | 31 | 13 | | 32 | 17 | | 33 | 13 | | 34 | 4 | | 35 | 31 | | 36 | 11 | | 37 | 20 | | 38 | 32 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 128 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 83 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 855 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 16 | | adverbRatio | 0.01871345029239766 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.00935672514619883 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 10.24 | | std | 5.26 | | cv | 0.513 | | sampleLengths | | 0 | 8 | | 1 | 13 | | 2 | 17 | | 3 | 9 | | 4 | 17 | | 5 | 9 | | 6 | 10 | | 7 | 11 | | 8 | 12 | | 9 | 12 | | 10 | 6 | | 11 | 11 | | 12 | 5 | | 13 | 6 | | 14 | 10 | | 15 | 9 | | 16 | 7 | | 17 | 8 | | 18 | 14 | | 19 | 14 | | 20 | 8 | | 21 | 16 | | 22 | 19 | | 23 | 5 | | 24 | 20 | | 25 | 17 | | 26 | 8 | | 27 | 10 | | 28 | 1 | | 29 | 9 | | 30 | 6 | | 31 | 13 | | 32 | 7 | | 33 | 18 | | 34 | 9 | | 35 | 2 | | 36 | 4 | | 37 | 9 | | 38 | 7 | | 39 | 14 | | 40 | 13 | | 41 | 5 | | 42 | 11 | | 43 | 3 | | 44 | 7 | | 45 | 9 | | 46 | 6 | | 47 | 7 | | 48 | 18 | | 49 | 7 |
| |
| 71.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.43373493975903615 | | totalSentences | 83 | | uniqueOpeners | 36 | |
| 41.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 81 | | matches | | 0 | "Bright blue eyes scanned the" |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 81 | | matches | | 0 | "His left leg dragged three" | | 1 | "She unzipped the canvas delivery" | | 2 | "He set the tumbler beside" | | 3 | "You trade pre-law textbooks for" | | 4 | "Her left thumb traced the" | | 5 | "He paused, scanning the dim" | | 6 | "Her hand froze over her" | | 7 | "His gaze locked onto Aurora," | | 8 | "Her fingers curled into the" | | 9 | "You lost your address six" | | 10 | "He shed no coat, carrying" | | 11 | "Your father still asks if" | | 12 | "He reached out, his manicured" | | 13 | "He mentions the wedding occasionally." | | 14 | "He leaned his hip against" | | 15 | "He did not acknowledge the" | | 16 | "She left Cardiff with a" | | 17 | "You owe them that much" | | 18 | "I owe Cardiff nothing." | | 19 | "He turned on his heel," |
| | ratio | 0.272 | |
| 27.90% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 81 | | matches | | 0 | "Maps crinkled behind glass frames" | | 1 | "Silas wiped a tumbler with" | | 2 | "His left leg dragged three" | | 3 | "A silver signet ring tapped" | | 4 | "Aurora pushed through the door," | | 5 | "She unzipped the canvas delivery" | | 6 | "The delivery route ran late" | | 7 | "Traffic stalled near Oxford Circus," | | 8 | "Glass squeaked under the linen." | | 9 | "Silas did not look up" | | 10 | "He set the tumbler beside" | | 11 | "Yu-Fei expects the receipt back" | | 12 | "Silas rested his palms on" | | 13 | "Hazel eyes narrowed past the" | | 14 | "You trade pre-law textbooks for" | | 15 | "Aurora pulled a crumpled slip" | | 16 | "Her left thumb traced the" | | 17 | "Cardiff courtrooms lost their appeal." | | 18 | "A heavy oak door swung" | | 19 | "The man wore a sharply" |
| | ratio | 0.864 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |