| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 30 | | adverbTagCount | 3 | | adverbTags | | 0 | "He walked back [back]" | | 1 | "she said again [again]" | | 2 | "Eva smiled faintly [faintly]" |
| | dialogueSentences | 108 | | tagDensity | 0.278 | | leniency | 0.556 | | rawRatio | 0.1 | | effectiveRatio | 0.056 | |
| 82.76% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2030 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "slowly" | | 1 | "quickly" | | 2 | "suddenly" | | 3 | "slightly" | | 4 | "perfectly" | | 5 | "carefully" | | 6 | "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) | |
| 90.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2030 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "eyebrow" | | 2 | "familiar" |
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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 | 141 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 141 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 219 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2030 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 43 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 126 | | wordCount | 1427 | | uniqueNames | 14 | | maxNameDensity | 3.64 | | worstName | "Eva" | | maxWindowNameDensity | 7 | | worstWindowName | "Eva" | | discoveredNames | | Rory | 50 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Silas | 11 | | London | 3 | | Eva | 52 | | Jennifer | 1 | | Brendan | 1 | | Thursday | 1 | | Golden | 1 | | Empress | 1 | | Paddington | 1 | | Yu-Fei | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Silas" | | 3 | "Eva" | | 4 | "Jennifer" | | 5 | "Brendan" | | 6 | "Yu-Fei" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Golden" | | 3 | "Paddington" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 93 | | 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 | 2030 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 219 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 122 | | mean | 16.64 | | std | 17.67 | | cv | 1.062 | | sampleLengths | | 0 | 50 | | 1 | 43 | | 2 | 16 | | 3 | 9 | | 4 | 5 | | 5 | 60 | | 6 | 15 | | 7 | 46 | | 8 | 68 | | 9 | 22 | | 10 | 1 | | 11 | 17 | | 12 | 1 | | 13 | 40 | | 14 | 8 | | 15 | 3 | | 16 | 32 | | 17 | 4 | | 18 | 10 | | 19 | 31 | | 20 | 7 | | 21 | 6 | | 22 | 10 | | 23 | 5 | | 24 | 32 | | 25 | 23 | | 26 | 4 | | 27 | 3 | | 28 | 5 | | 29 | 7 | | 30 | 53 | | 31 | 19 | | 32 | 3 | | 33 | 29 | | 34 | 25 | | 35 | 10 | | 36 | 3 | | 37 | 6 | | 38 | 19 | | 39 | 9 | | 40 | 64 | | 41 | 11 | | 42 | 21 | | 43 | 4 | | 44 | 1 | | 45 | 31 | | 46 | 7 | | 47 | 2 | | 48 | 65 | | 49 | 10 |
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| 97.80% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 141 | | matches | | 0 | "was cropped" | | 1 | "being sent" | | 2 | "been told" |
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| 78.35% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 274 | | matches | | 0 | "was adding" | | 1 | "was beginning" | | 2 | "was waiting" | | 3 | "was reading" | | 4 | "was laughing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 2 | | flaggedSentences | 2 | | totalSentences | 219 | | ratio | 0.009 | | matches | | 0 | "He had been on his feet since opening; she could see it in the care he took shifting his weight off his left leg." | | 1 | "A man at the counter was reading a newspaper; someone in the back was laughing, softly enough that Rory couldn’t hear why." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1430 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.03076923076923077 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.008391608391608392 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 219 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 219 | | mean | 9.27 | | std | 6.51 | | cv | 0.703 | | sampleLengths | | 0 | 32 | | 1 | 18 | | 2 | 17 | | 3 | 13 | | 4 | 13 | | 5 | 16 | | 6 | 5 | | 7 | 4 | | 8 | 5 | | 9 | 11 | | 10 | 24 | | 11 | 14 | | 12 | 11 | | 13 | 9 | | 14 | 6 | | 15 | 20 | | 16 | 6 | | 17 | 20 | | 18 | 17 | | 19 | 22 | | 20 | 6 | | 21 | 23 | | 22 | 9 | | 23 | 3 | | 24 | 10 | | 25 | 1 | | 26 | 17 | | 27 | 1 | | 28 | 7 | | 29 | 9 | | 30 | 6 | | 31 | 18 | | 32 | 8 | | 33 | 3 | | 34 | 17 | | 35 | 15 | | 36 | 4 | | 37 | 6 | | 38 | 4 | | 39 | 10 | | 40 | 21 | | 41 | 7 | | 42 | 6 | | 43 | 4 | | 44 | 6 | | 45 | 5 | | 46 | 5 | | 47 | 27 | | 48 | 4 | | 49 | 13 |
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| 42.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.2968036529680365 | | totalSentences | 219 | | uniqueOpeners | 65 | |
| 84.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 118 | | matches | | 0 | "Then she looked toward the" | | 1 | "Then Eva came around the" | | 2 | "Instead she said," |
| | ratio | 0.025 | |
| 94.58% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 118 | | matches | | 0 | "She pushed through the door" | | 1 | "His silver signet ring held" | | 2 | "He set down his pen" | | 3 | "He had been on his" | | 4 | "he said, nodding toward the" | | 5 | "Her flat was one floor" | | 6 | "She was going to say" | | 7 | "She wore a charcoal coat" | | 8 | "Her mouth opened." | | 9 | "She stood so quickly the" | | 10 | "He placed it on the" | | 11 | "He walked back toward the" | | 12 | "It was the first expression" | | 13 | "She had once told Rory" | | 14 | "It was hot enough to" | | 15 | "She could ask Eva about" | | 16 | "She could ask about the" | | 17 | "She had kept the postcard" | | 18 | "It had stood there through" | | 19 | "She’d looked at Eva’s handwriting" |
| | ratio | 0.314 | |
| 36.27% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 118 | | matches | | 0 | "The green neon sign threw" | | 1 | "She pushed through the door" | | 2 | "Rory carried the bag behind" | | 3 | "His silver signet ring held" | | 4 | "Silas looked into the bag." | | 5 | "He set down his pen" | | 6 | "He had been on his" | | 7 | "Rory had meant to tell" | | 8 | "he said, nodding toward the" | | 9 | "Her flat was one floor" | | 10 | "She was going to say" | | 11 | "The woman’s hair was cropped" | | 12 | "She wore a charcoal coat" | | 13 | "Rory had never seen either" | | 14 | "Eva turned, perhaps because she" | | 15 | "Her mouth opened." | | 16 | "She stood so quickly the" | | 17 | "Rory was suddenly aware of" | | 18 | "The embrace was firm and" | | 19 | "Rory held on a second" |
| | ratio | 0.847 | |
| 42.37% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 118 | | matches | | 0 | "By the time the postcard" |
| | ratio | 0.008 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 1 | | matches | | 0 | "She called goodnight to Silas, who raised the bag in reply." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 30 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 22 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 108 | | tagDensity | 0.204 | | leniency | 0.407 | | rawRatio | 0 | | effectiveRatio | 0 | |