| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva asked softly [softly]" | | 1 | "Eva said finally [finally]" |
| | dialogueSentences | 52 | | tagDensity | 0.385 | | leniency | 0.769 | | rawRatio | 0.1 | | effectiveRatio | 0.077 | |
| 79.78% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1484 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "really" | | 1 | "suddenly" | | 2 | "softly" |
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| 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) | |
| 76.42% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1484 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "familiar" | | 1 | "glinting" | | 2 | "silence" | | 3 | "weight" |
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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 | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 105 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 137 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1485 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 84 | | wordCount | 1147 | | uniqueNames | 18 | | maxNameDensity | 2.44 | | worstName | "Aurora" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Soho | 2 | | Golden | 1 | | Empress | 1 | | Raven | 1 | | Nest | 2 | | London | 4 | | Cardiff | 2 | | Silas | 7 | | October | 1 | | Aurora | 28 | | Yu-Fei | 1 | | Pre-Law | 1 | | Brendan | 2 | | Carter | 2 | | Jennifer | 1 | | Ellis | 1 | | Eva | 25 | | Evan | 2 |
| | persons | | 0 | "Raven" | | 1 | "Silas" | | 2 | "Aurora" | | 3 | "Brendan" | | 4 | "Carter" | | 5 | "Jennifer" | | 6 | "Ellis" | | 7 | "Eva" | | 8 | "Evan" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Cardiff" |
| | globalScore | 0.279 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like it had kept some of her with" |
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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 | 1485 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 137 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 25.6 | | std | 24.58 | | cv | 0.96 | | sampleLengths | | 0 | 54 | | 1 | 119 | | 2 | 12 | | 3 | 50 | | 4 | 17 | | 5 | 26 | | 6 | 114 | | 7 | 15 | | 8 | 22 | | 9 | 11 | | 10 | 10 | | 11 | 66 | | 12 | 31 | | 13 | 1 | | 14 | 20 | | 15 | 17 | | 16 | 87 | | 17 | 9 | | 18 | 27 | | 19 | 12 | | 20 | 26 | | 21 | 11 | | 22 | 9 | | 23 | 25 | | 24 | 42 | | 25 | 16 | | 26 | 51 | | 27 | 4 | | 28 | 16 | | 29 | 15 | | 30 | 29 | | 31 | 56 | | 32 | 9 | | 33 | 10 | | 34 | 19 | | 35 | 6 | | 36 | 23 | | 37 | 27 | | 38 | 6 | | 39 | 18 | | 40 | 19 | | 41 | 44 | | 42 | 15 | | 43 | 15 | | 44 | 16 | | 45 | 4 | | 46 | 65 | | 47 | 10 | | 48 | 7 | | 49 | 25 |
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| 95.24% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 105 | | matches | | 0 | "was used" | | 1 | "was used" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 215 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 137 | | ratio | 0.007 | | matches | | 0 | "She pushed it back and caught her own reflection in the bar mirror — bright blue eyes tired at the edges, the small crescent scar on her left wrist pale against her skin where her sleeve had ridden up." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1154 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 41 | | adverbRatio | 0.03552859618717504 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.00779896013864818 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 137 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 137 | | mean | 10.84 | | std | 8.38 | | cv | 0.773 | | sampleLengths | | 0 | 25 | | 1 | 21 | | 2 | 8 | | 3 | 43 | | 4 | 16 | | 5 | 37 | | 6 | 23 | | 7 | 6 | | 8 | 6 | | 9 | 31 | | 10 | 19 | | 11 | 9 | | 12 | 8 | | 13 | 7 | | 14 | 19 | | 15 | 31 | | 16 | 14 | | 17 | 39 | | 18 | 8 | | 19 | 22 | | 20 | 15 | | 21 | 5 | | 22 | 6 | | 23 | 11 | | 24 | 11 | | 25 | 8 | | 26 | 2 | | 27 | 18 | | 28 | 12 | | 29 | 16 | | 30 | 11 | | 31 | 9 | | 32 | 6 | | 33 | 20 | | 34 | 5 | | 35 | 1 | | 36 | 16 | | 37 | 4 | | 38 | 12 | | 39 | 5 | | 40 | 19 | | 41 | 3 | | 42 | 2 | | 43 | 40 | | 44 | 14 | | 45 | 9 | | 46 | 9 | | 47 | 5 | | 48 | 14 | | 49 | 8 |
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| 36.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.24087591240875914 | | totalSentences | 137 | | uniqueOpeners | 33 | |
| 37.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 90 | | matches | | | ratio | 0.011 | |
| 46.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 90 | | matches | | 0 | "She smelled of fryer oil" | | 1 | "His auburn hair was more" | | 2 | "He limped a little more" | | 3 | "he said, not looking up" | | 4 | "She smiled because it was" | | 5 | "He handed her a glass" | | 6 | "She meant it" | | 7 | "She slid onto a stool" | | 8 | "Her hair was damp, black" | | 9 | "She pushed it back and" | | 10 | "She had told Silas once," | | 11 | "She was used to the" | | 12 | "She was used to the" | | 13 | "It was too familiar and" | | 14 | "She was shorter than Aurora" | | 15 | "Her hair was cut blunt" | | 16 | "She was cool-headed by reputation," | | 17 | "He set down the cloth." | | 18 | "They moved as if to" | | 19 | "She’d come to London because" |
| | ratio | 0.433 | |
| 4.44% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 82 | | totalSentences | 90 | | matches | | 0 | "The green neon over the" | | 1 | "Aurora ducked under it with" | | 2 | "She smelled of fryer oil" | | 3 | "The Raven’s Nest was the" | | 4 | "Silas was behind the bar," | | 5 | "His auburn hair was more" | | 6 | "He limped a little more" | | 7 | "he said, not looking up" | | 8 | "She smiled because it was" | | 9 | "He handed her a glass" | | 10 | "She meant it" | | 11 | "The Nest was the only" | | 12 | "She slid onto a stool" | | 13 | "Her hair was damp, black" | | 14 | "She pushed it back and" | | 15 | "A childhood accident, a kitchen" | | 16 | "She had told Silas once," | | 17 | "That was when the bell" | | 18 | "Aurora didn’t turn right away." | | 19 | "She was used to the" |
| | ratio | 0.911 | |
| 55.56% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 90 | | matches | | 0 | "Now her throat felt dry." |
| | ratio | 0.011 | |
| 59.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 5 | | matches | | 0 | "The green neon over the door had buzzed since she was nineteen, a thin sickly light that cut the Soho wet to something almost soft." | | 1 | "She slid onto a stool at the far end, near the bookshelf that was always a little crooked, the one everyone knew led somewhere else if you knew the right pressu…" | | 2 | "Eva was standing under the neon’s spill, shaking water from a coat that was too thin for October." | | 3 | "Her hair was cut blunt at her jaw, dyed a dark auburn that didn’t suit her." | | 4 | "Since the night Aurora had packed a duffel bag, left Pre-Law mid-term, left Brendan Carter’s house with the barrister father who never quite understood why she …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, not looking up" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 52 | | tagDensity | 0.269 | | leniency | 0.538 | | rawRatio | 0 | | effectiveRatio | 0 | |