| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "He looked genuinely [genuinely]" |
| | dialogueSentences | 48 | | tagDensity | 0.396 | | leniency | 0.792 | | rawRatio | 0.053 | | effectiveRatio | 0.042 | |
| 90.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1084 | | 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) | |
| 35.42% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1084 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "weight" | | 1 | "gloom" | | 2 | "eyebrow" | | 3 | "intrigue" | | 4 | "whisper" | | 5 | "fleeting" | | 6 | "flicker" | | 7 | "silence" | | 8 | "traced" | | 9 | "unreadable" | | 10 | "encounter" |
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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 | 2 | | narrationSentences | 75 | | matches | | 0 | "a flicker of sympathy" | | 1 | "a spark of disbelief" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 75 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1082 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 710 | | uniqueNames | 12 | | maxNameDensity | 1.13 | | worstName | "Rory" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Aurora | 1 | | Carter | 1 | | Knightsbridge | 1 | | Mayfair | 1 | | Camden | 1 | | Town | 1 | | Evan | 5 | | Rory | 8 | | Silas | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Carter" | | 4 | "Evan" | | 5 | "Rory" | | 6 | "Silas" |
| | places | | 0 | "Soho" | | 1 | "Knightsbridge" | | 2 | "Camden" | | 3 | "Town" |
| | globalScore | 0.937 | | windowScore | 0.833 | |
| 62.28% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed faded" | | 1 | "tasted like ash" |
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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 | 1082 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 105 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 22.08 | | std | 14.88 | | cv | 0.674 | | sampleLengths | | 0 | 82 | | 1 | 44 | | 2 | 30 | | 3 | 26 | | 4 | 14 | | 5 | 7 | | 6 | 56 | | 7 | 5 | | 8 | 31 | | 9 | 22 | | 10 | 19 | | 11 | 31 | | 12 | 2 | | 13 | 41 | | 14 | 19 | | 15 | 16 | | 16 | 27 | | 17 | 14 | | 18 | 26 | | 19 | 12 | | 20 | 10 | | 21 | 9 | | 22 | 8 | | 23 | 20 | | 24 | 8 | | 25 | 16 | | 26 | 20 | | 27 | 35 | | 28 | 5 | | 29 | 23 | | 30 | 10 | | 31 | 32 | | 32 | 13 | | 33 | 24 | | 34 | 18 | | 35 | 7 | | 36 | 23 | | 37 | 6 | | 38 | 13 | | 39 | 25 | | 40 | 19 | | 41 | 29 | | 42 | 20 | | 43 | 33 | | 44 | 21 | | 45 | 24 | | 46 | 32 | | 47 | 53 | | 48 | 2 |
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| 95.91% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 75 | | matches | | 0 | "was covered" | | 1 | "was mixed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 127 | | matches | (empty) | |
| 61.22% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 105 | | ratio | 0.029 | | matches | | 0 | "Silas wasn't behind the bar tonight; a younger man, all sharp angles and nervous energy, wiped glasses." | | 1 | "The delivery route had been brutal—a penthouse in Knightsbridge with a resident who tipped in condescension, a flat in Mayfair smelling of old money and regret, and finally, a shouting match over lukewarm noodles in Camden Town." | | 2 | "“Okay, fair enough. You always were brutally honest.” He ran a hand through his hair—dark, but flecked with silver at the temples." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 723 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.03319502074688797 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.015214384508990318 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 10.3 | | std | 6.94 | | cv | 0.674 | | sampleLengths | | 0 | 17 | | 1 | 26 | | 2 | 14 | | 3 | 17 | | 4 | 1 | | 5 | 7 | | 6 | 4 | | 7 | 3 | | 8 | 37 | | 9 | 13 | | 10 | 6 | | 11 | 11 | | 12 | 16 | | 13 | 7 | | 14 | 3 | | 15 | 14 | | 16 | 6 | | 17 | 1 | | 18 | 2 | | 19 | 10 | | 20 | 9 | | 21 | 11 | | 22 | 14 | | 23 | 10 | | 24 | 5 | | 25 | 12 | | 26 | 19 | | 27 | 10 | | 28 | 8 | | 29 | 4 | | 30 | 6 | | 31 | 13 | | 32 | 6 | | 33 | 21 | | 34 | 4 | | 35 | 2 | | 36 | 9 | | 37 | 22 | | 38 | 10 | | 39 | 14 | | 40 | 5 | | 41 | 12 | | 42 | 4 | | 43 | 7 | | 44 | 11 | | 45 | 9 | | 46 | 10 | | 47 | 4 | | 48 | 14 | | 49 | 12 |
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| 56.19% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.37142857142857144 | | totalSentences | 105 | | uniqueOpeners | 39 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 69 | | matches | | 0 | "She shook the droplets from" | | 1 | "She wasn’t in the mood" | | 2 | "She needed a drink." | | 3 | "She settled onto a worn" | | 4 | "she told the bartender" | | 5 | "He poured without comment, sliding" | | 6 | "It couldn’t be." | | 7 | "She turned, and her breath" | | 8 | "His jaw was covered in" | | 9 | "He’d lost weight, the expensive" | | 10 | "He hadn't met her gaze." | | 11 | "Her voice came out sharper" | | 12 | "She took a long swallow" | | 13 | "He finally lifted his head," | | 14 | "She raised an eyebrow" | | 15 | "He ambled over, pulling up" | | 16 | "He flinched, a barely perceptible" | | 17 | "He ran a hand through" | | 18 | "She kept her voice level," | | 19 | "She wondered what he was" |
| | ratio | 0.609 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 67 | | totalSentences | 69 | | matches | | 0 | "The green neon sign of" | | 1 | "Rain slicked the black coat" | | 2 | "She shook the droplets from" | | 3 | "Silas wasn't behind the bar" | | 4 | "She wasn’t in the mood" | | 5 | "She needed a drink." | | 6 | "A strong one." | | 7 | "The delivery route had been" | | 8 | "She settled onto a worn" | | 9 | "she told the bartender" | | 10 | "He poured without comment, sliding" | | 11 | "A figure shifted in the" | | 12 | "Rory's hand stilled halfway to" | | 13 | "It couldn’t be." | | 14 | "The voice was raspy, roughened" | | 15 | "She turned, and her breath" | | 16 | "This Evan was all hard" | | 17 | "His jaw was covered in" | | 18 | "He’d lost weight, the expensive" | | 19 | "He hadn't met her gaze." |
| | ratio | 0.971 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 5 | | matches | | 0 | "She kept, refusing to give him any purchase" | | 1 | "She met, refusing to let him see any weakness" | | 2 | "She raised, a spark of disbelief flickering in her eyes" | | 3 | "He spoke, never taking his eyes off Rory" | | 4 | "He glanced, a flicker of something unreadable passing across his face" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Rory laughed (laugh)" | | 1 | "He spoke (speak)" |
| | dialogueSentences | 48 | | tagDensity | 0.063 | | leniency | 0.125 | | rawRatio | 0.667 | | effectiveRatio | 0.083 | |