| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.37% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1310 | | 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) | |
| 65.65% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1310 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "traced" | | 1 | "chill" | | 2 | "weight" | | 3 | "could feel" | | 4 | "warmth" | | 5 | "flicked" | | 6 | "dance" | | 7 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 219 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 6 | | hedgeCount | 0 | | narrationSentences | 219 | | filterMatches | | | hedgeMatches | (empty) | |
| 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 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1310 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 14 | | matches | | 0 | "I work in delivery, Rory said." | | 1 | "I know the place, he said." | | 2 | "Security, Rory said." | | 3 | "I did save the world, Tom said." | | 4 | "You have scars, he said." | | 5 | "I have a flat, she said." | | 6 | "We all find the people who fit the mold, Tom said." | | 7 | "I stopped smoking, he said." | | 8 | "You still see me, she said." | | 9 | "I help delivery people get back to their bikes when they slip in the rain, she said." | | 10 | "I had to choose something, she said." | | 11 | "You know him, Tom said." | | 12 | "I know the city, Silas said." | | 13 | "You have work, Tom said." |
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| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 71 | | wordCount | 1310 | | uniqueNames | 12 | | maxNameDensity | 1.45 | | worstName | "Tom" | | maxWindowNameDensity | 3 | | worstWindowName | "You" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Silas | 10 | | Rory | 14 | | Tom | 19 | | City | 1 | | London | 1 | | English | 1 | | Chelsea | 1 | | Cardiff | 3 | | Evan | 3 | | You | 16 |
| | persons | | 0 | "Nest" | | 1 | "Silas" | | 2 | "Rory" | | 3 | "Tom" | | 4 | "Chelsea" | | 5 | "Evan" | | 6 | "You" |
| | places | | 0 | "Raven" | | 1 | "City" | | 2 | "London" | | 3 | "Cardiff" |
| | globalScore | 0.775 | | windowScore | 0.667 | |
| 72.68% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 97 | | glossingSentenceCount | 3 | | matches | | 0 | "tasted like copper" | | 1 | "felt like" | | 2 | "looked like he had aged ten years in five" |
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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 | 1310 | | 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 | 65 | | mean | 20.15 | | std | 14.54 | | cv | 0.721 | | sampleLengths | | 0 | 75 | | 1 | 53 | | 2 | 1 | | 3 | 43 | | 4 | 24 | | 5 | 30 | | 6 | 13 | | 7 | 15 | | 8 | 10 | | 9 | 52 | | 10 | 6 | | 11 | 28 | | 12 | 33 | | 13 | 42 | | 14 | 8 | | 15 | 4 | | 16 | 21 | | 17 | 22 | | 18 | 19 | | 19 | 40 | | 20 | 31 | | 21 | 22 | | 22 | 9 | | 23 | 19 | | 24 | 5 | | 25 | 13 | | 26 | 37 | | 27 | 21 | | 28 | 4 | | 29 | 7 | | 30 | 45 | | 31 | 9 | | 32 | 22 | | 33 | 10 | | 34 | 8 | | 35 | 16 | | 36 | 18 | | 37 | 18 | | 38 | 19 | | 39 | 6 | | 40 | 7 | | 41 | 1 | | 42 | 22 | | 43 | 4 | | 44 | 32 | | 45 | 7 | | 46 | 17 | | 47 | 19 | | 48 | 9 | | 49 | 12 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 219 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 278 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 219 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1313 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.01904036557501904 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.002284843869002285 | |
| 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 | 5.98 | | std | 3.59 | | cv | 0.601 | | sampleLengths | | 0 | 16 | | 1 | 19 | | 2 | 13 | | 3 | 18 | | 4 | 9 | | 5 | 15 | | 6 | 5 | | 7 | 5 | | 8 | 4 | | 9 | 13 | | 10 | 11 | | 11 | 1 | | 12 | 4 | | 13 | 6 | | 14 | 10 | | 15 | 3 | | 16 | 8 | | 17 | 12 | | 18 | 4 | | 19 | 6 | | 20 | 14 | | 21 | 7 | | 22 | 4 | | 23 | 19 | | 24 | 3 | | 25 | 5 | | 26 | 5 | | 27 | 2 | | 28 | 5 | | 29 | 3 | | 30 | 5 | | 31 | 6 | | 32 | 4 | | 33 | 5 | | 34 | 9 | | 35 | 8 | | 36 | 11 | | 37 | 11 | | 38 | 4 | | 39 | 4 | | 40 | 6 | | 41 | 21 | | 42 | 4 | | 43 | 3 | | 44 | 5 | | 45 | 4 | | 46 | 6 | | 47 | 5 | | 48 | 5 | | 49 | 4 |
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| 29.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 44 | | diversityRatio | 0.2054794520547945 | | totalSentences | 219 | | uniqueOpeners | 45 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 196 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 124 | | totalSentences | 196 | | matches | | 0 | "She traced the rim of" | | 1 | "He paused, watching the door." | | 2 | "He shook the water off" | | 3 | "He looked older." | | 4 | "His eyes were tired, lined" | | 5 | "He walked toward her." | | 6 | "He leaned against the bar," | | 7 | "He didn't hug her." | | 8 | "He looked at her hands," | | 9 | "She pushed the chair back." | | 10 | "It screeched against the floorboards." | | 11 | "It didn't reach his eyes." | | 12 | "You look different." | | 13 | "You look like a ghost." | | 14 | "I work in delivery, Rory" | | 15 | "You used to hate running." | | 16 | "He pulled out the chair" | | 17 | "He tapped the table with" | | 18 | "I hate sitting still now," | | 19 | "He looked at Silas." |
| | ratio | 0.633 | |
| 5.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 178 | | totalSentences | 196 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Rory sat at the corner" | | 2 | "She traced the rim of" | | 3 | "The glass felt cold, grounding" | | 4 | "Silas wiped the counter behind" | | 5 | "He paused, watching the door." | | 6 | "The bell above it jingled." | | 7 | "A man walked in." | | 8 | "Rain dripped from a wool" | | 9 | "He shook the water off" | | 10 | "Rory lifted her head." | | 11 | "The man stopped in his" | | 12 | "Recognition hit his face hard," | | 13 | "He looked older." | | 14 | "The sharp angles of his" | | 15 | "His eyes were tired, lined" | | 16 | "He walked toward her." | | 17 | "Silas watched them, stopping his" | | 18 | "He leaned against the bar," | | 19 | "Tom stopped in front of" |
| | ratio | 0.908 | |
| 76.53% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 196 | | matches | | 0 | "Before I came down here." | | 1 | "Now you wear a helmet" | | 2 | "Before I got a job" |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 1 | | matches | | 0 | "We all find the people who fit the mold, Tom said." |
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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 | |