| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.05% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1265 | | 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) | |
| 13.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1265 | | totalAiIsms | 22 | | found | | | highlights | | 0 | "flicked" | | 1 | "weight" | | 2 | "stomach" | | 3 | "chaotic" | | 4 | "traced" | | 5 | "scanning" | | 6 | "silence" | | 7 | "grave" | | 8 | "trembled" | | 9 | "tension" | | 10 | "mechanical" | | 11 | "footsteps" | | 12 | "glinting" | | 13 | "warmth" | | 14 | "chill" | | 15 | "unreadable" |
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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 | 49 | | matches | (empty) | |
| 55.39% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 49 | | filterMatches | | | hedgeMatches | | 0 | "managed to" | | 1 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 75 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1265 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 51.52% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 660 | | uniqueNames | 9 | | maxNameDensity | 1.97 | | worstName | "Rory" | | maxWindowNameDensity | 3 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 2 | | London | 2 | | Silas | 5 | | Rory | 13 | | Mayfair | 1 | | Cardiff | 1 | | Eva | 7 | | Soho | 1 |
| | persons | | 0 | "Raven" | | 1 | "Silas" | | 2 | "Rory" | | 3 | "Eva" |
| | places | | 0 | "London" | | 1 | "Mayfair" | | 2 | "Cardiff" | | 3 | "Soho" |
| | globalScore | 0.515 | | windowScore | 0.667 | |
| 30.95% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a polished blade, cold and te" | | 1 | "finality that seemed to rattle the old photographs on the walls" |
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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 | 1265 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 30.12 | | std | 20.17 | | cv | 0.67 | | sampleLengths | | 0 | 85 | | 1 | 50 | | 2 | 6 | | 3 | 26 | | 4 | 15 | | 5 | 105 | | 6 | 8 | | 7 | 33 | | 8 | 26 | | 9 | 28 | | 10 | 31 | | 11 | 23 | | 12 | 40 | | 13 | 48 | | 14 | 35 | | 15 | 46 | | 16 | 30 | | 17 | 24 | | 18 | 19 | | 19 | 27 | | 20 | 40 | | 21 | 39 | | 22 | 14 | | 23 | 10 | | 24 | 45 | | 25 | 26 | | 26 | 8 | | 27 | 52 | | 28 | 15 | | 29 | 39 | | 30 | 23 | | 31 | 14 | | 32 | 23 | | 33 | 25 | | 34 | 26 | | 35 | 68 | | 36 | 4 | | 37 | 27 | | 38 | 9 | | 39 | 18 | | 40 | 16 | | 41 | 19 |
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| 83.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 49 | | matches | | 0 | "been buried" | | 1 | "been replaced" | | 2 | "was sealed" |
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| 65.32% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 99 | | matches | | 0 | "was watching" | | 1 | "was currently polishing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 75 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 665 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, cream-colored wax." |
| | adverbCount | 12 | | adverbRatio | 0.01804511278195489 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.010526315789473684 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 75 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 75 | | mean | 16.87 | | std | 10.39 | | cv | 0.616 | | sampleLengths | | 0 | 20 | | 1 | 18 | | 2 | 9 | | 3 | 19 | | 4 | 19 | | 5 | 8 | | 6 | 28 | | 7 | 14 | | 8 | 6 | | 9 | 18 | | 10 | 8 | | 11 | 15 | | 12 | 3 | | 13 | 12 | | 14 | 8 | | 15 | 22 | | 16 | 3 | | 17 | 20 | | 18 | 10 | | 19 | 27 | | 20 | 8 | | 21 | 15 | | 22 | 18 | | 23 | 26 | | 24 | 7 | | 25 | 8 | | 26 | 13 | | 27 | 31 | | 28 | 23 | | 29 | 40 | | 30 | 7 | | 31 | 20 | | 32 | 21 | | 33 | 35 | | 34 | 46 | | 35 | 14 | | 36 | 12 | | 37 | 4 | | 38 | 24 | | 39 | 19 | | 40 | 27 | | 41 | 12 | | 42 | 5 | | 43 | 14 | | 44 | 9 | | 45 | 39 | | 46 | 14 | | 47 | 10 | | 48 | 45 | | 49 | 4 |
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| 56.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.37333333333333335 | | totalSentences | 75 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 49 | | matches | (empty) | | ratio | 0 | |
| 81.22% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 49 | | matches | | 0 | "She smelled of stale takeaway" | | 1 | "He nodded once, a brief" | | 2 | "She wore a tailored wool" | | 3 | "She checked her watch, the" | | 4 | "He gestured toward the booth" | | 5 | "She walked toward the booth," | | 6 | "It was Eva." | | 7 | "She traced the faint crescent" | | 8 | "It sat between them, a" | | 9 | "Her grip was iron, professional." | | 10 | "He was currently polishing a" | | 11 | "She looked down at Rory," | | 12 | "She stopped at the door," | | 13 | "She stepped out into the" | | 14 | "It was sealed with heavy," | | 15 | "He stopped at the edge" | | 16 | "He gestured to the envelope" |
| | ratio | 0.347 | |
| 11.02% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 44 | | totalSentences | 49 | | matches | | 0 | "The neon green sign buzzed," | | 1 | "Rory stepped inside, the humid" | | 2 | "She smelled of stale takeaway" | | 3 | "He nodded once, a brief" | | 4 | "A woman sat there, nursing" | | 5 | "She wore a tailored wool" | | 6 | "She checked her watch, the" | | 7 | "Silas set the glass down," | | 8 | "He gestured toward the booth" | | 9 | "Rory’s stomach tightened." | | 10 | "She walked toward the booth," | | 11 | "The sharp, feline tilt of" | | 12 | "It was Eva." | | 13 | "This woman looked like a" | | 14 | "The soft curve of her" | | 15 | "Rory slid into the opposite" | | 16 | "She traced the faint crescent" | | 17 | "Eva traced the rim of" | | 18 | "A small, humorless smile played" | | 19 | "A diamond stud caught the" |
| | ratio | 0.898 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 49 | | matches | | 0 | "Now, she was as steady" |
| | ratio | 0.02 | |
| 75.89% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 3 | | matches | | 0 | "She wore a tailored wool coat that cost more than Rory’s annual rent, her hair chopped into a sharp, architectural bob that screamed of high-end salons in Mayfa…" | | 1 | "The sharp, feline tilt of her eyes stirred a memory that had been buried under years of Cardiff rain and legal textbooks." | | 2 | "Silas was watching them, his hand resting idly on the counter near the hidden mechanism that triggered the bookshelf gate." |
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| 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 | |