| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 97.12% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1736 | | 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) | |
| 76.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1736 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "footfall" | | 1 | "lilt" | | 2 | "weight" | | 3 | "tension" | | 4 | "dance" | | 5 | "throbbed" | | 6 | "footsteps" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 307 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 0 | | narrationSentences | 307 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 307 | | 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 | 1736 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 67 | | unquotedAttributions | 59 | | matches | | 0 | "You look different, she said." | | 1 | "Time has a way of doing that to people, Liam said." | | 2 | "Who is this, she asked." | | 3 | "Someone you know, Liam said." | | 4 | "I thought you were dead, Aurora said." | | 5 | "I thought you were married, Liam said." | | 6 | "I never married, she said." | | 7 | "You ran, Liam said." | | 8 | "Running preserves the life you have, Silas said." | | 9 | "This is where I work, she said." | | 10 | "Who is watching me, Aurora asked." | | 11 | "The men in the grey suits, Liam said." | | 12 | "Eva is safe, Aurora said." | | 13 | "Is she, Liam said." | | 14 | "What did you do, she said." | | 15 | "I saved her, Liam said." | | 16 | "Hands where I can see them, Silas said." | | 17 | "It opens the box, Liam said." | | 18 | "Why are you here, she said." | | 19 | "Because they are coming, Liam said." | | 20 | "Then you are late, Silas said." | | 21 | "You knew, she said." | | 22 | "I suspected, Liam said." | | 23 | "Get out, Silas said." | | 24 | "Then you should have told me sooner, she said." | | 25 | "Coordinates, Silas said." | | 26 | "We have to go, she said." | | 27 | "No, Silas said." | | 28 | "They are coming for me, she said." | | 29 | "They are coming for the box, Silas said." | | 30 | "If we stay, they will burn this place down, she said." | | 31 | "Then we give them something to burn, Silas said." | | 32 | "They are here, she said." | | 33 | "Then lock the door, Silas said." | | 34 | "They will break it down, she said." | | 35 | "Let them, Silas said." | | 36 | "You never told me you kept a gun, she said." | | 37 | "I told you I was retired, Silas said." | | 38 | "Then he knows we know, Silas said." | | 39 | "He saw me, she said." | | 40 | "He saw us, Silas said." | | 41 | "We are not hiding, Silas said." | | 42 | "What do we do, she said." | | 43 | "We wait, Silas said." | | 44 | "Can I help you, Silas said." | | 45 | "This is her, Silas said." | | 46 | "I have nothing, Aurora said." | | 47 | "I am a businessman, Silas said." | | 48 | "There is no box, she said." | | 49 | "They are coming, Silas said." | | 50 | "We need to leave, she said." | | 51 | "We cannot, Silas said." | | 52 | "Why, she said." | | 53 | "Because the exit is blocked, Silas said." | | 54 | "There, she said." | | 55 | "Crawl, Silas said." | | 56 | "Where to, he said." | | 57 | "To Cardiff, she said." | | 58 | "Then we go, he said." |
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| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 130 | | wordCount | 1736 | | uniqueNames | 13 | | maxNameDensity | 2.88 | | worstName | "Silas" | | maxWindowNameDensity | 6.5 | | worstWindowName | "Silas" | | discoveredNames | | Liam | 22 | | Welsh | 1 | | Aurora | 37 | | Cardiff | 3 | | Silas | 50 | | Blackwood | 1 | | Eva | 3 | | Tuesday | 1 | | Golden | 1 | | Empress | 1 | | London | 1 | | Wales | 1 | | You | 8 |
| | persons | | 0 | "Liam" | | 1 | "Aurora" | | 2 | "Silas" | | 3 | "Eva" | | 4 | "You" |
| | places | | 0 | "Cardiff" | | 1 | "London" | | 2 | "Wales" |
| | globalScore | 0.06 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 123 | | 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 | 1736 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 307 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 149 | | mean | 11.65 | | std | 9.68 | | cv | 0.831 | | sampleLengths | | 0 | 60 | | 1 | 15 | | 2 | 70 | | 3 | 9 | | 4 | 1 | | 5 | 11 | | 6 | 5 | | 7 | 11 | | 8 | 31 | | 9 | 33 | | 10 | 22 | | 11 | 13 | | 12 | 5 | | 13 | 39 | | 14 | 7 | | 15 | 11 | | 16 | 27 | | 17 | 5 | | 18 | 8 | | 19 | 3 | | 20 | 3 | | 21 | 22 | | 22 | 19 | | 23 | 24 | | 24 | 7 | | 25 | 10 | | 26 | 17 | | 27 | 7 | | 28 | 8 | | 29 | 32 | | 30 | 6 | | 31 | 19 | | 32 | 29 | | 33 | 5 | | 34 | 10 | | 35 | 14 | | 36 | 6 | | 37 | 11 | | 38 | 13 | | 39 | 8 | | 40 | 22 | | 41 | 6 | | 42 | 9 | | 43 | 6 | | 44 | 29 | | 45 | 14 | | 46 | 6 | | 47 | 12 | | 48 | 18 | | 49 | 27 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 307 | | matches | | 0 | "were married" | | 1 | "was retired" | | 2 | "is blocked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 416 | | matches | | 0 | "was polishing" | | 1 | "was writing" | | 2 | "was running" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 307 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1740 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 24 | | adverbRatio | 0.013793103448275862 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0022988505747126436 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 307 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 307 | | mean | 5.65 | | std | 3.04 | | cv | 0.537 | | sampleLengths | | 0 | 19 | | 1 | 11 | | 2 | 15 | | 3 | 3 | | 4 | 12 | | 5 | 5 | | 6 | 10 | | 7 | 20 | | 8 | 7 | | 9 | 8 | | 10 | 8 | | 11 | 15 | | 12 | 12 | | 13 | 9 | | 14 | 1 | | 15 | 7 | | 16 | 4 | | 17 | 5 | | 18 | 6 | | 19 | 5 | | 20 | 11 | | 21 | 15 | | 22 | 5 | | 23 | 4 | | 24 | 21 | | 25 | 8 | | 26 | 8 | | 27 | 8 | | 28 | 6 | | 29 | 5 | | 30 | 4 | | 31 | 4 | | 32 | 5 | | 33 | 5 | | 34 | 3 | | 35 | 10 | | 36 | 21 | | 37 | 7 | | 38 | 7 | | 39 | 4 | | 40 | 4 | | 41 | 8 | | 42 | 4 | | 43 | 11 | | 44 | 5 | | 45 | 2 | | 46 | 6 | | 47 | 3 | | 48 | 3 | | 49 | 4 |
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| 34.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 48 | | diversityRatio | 0.14332247557003258 | | totalSentences | 307 | | uniqueOpeners | 44 | |
| 80.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 289 | | matches | | 0 | "Just for a night." | | 1 | "Then you are late, Silas" | | 2 | "Then you should have told" | | 3 | "Then we give them something" | | 4 | "Then lock the door, Silas" | | 5 | "Then he knows we know," | | 6 | "Then we go, he said." |
| | ratio | 0.024 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 164 | | totalSentences | 289 | | matches | | 0 | "She knew the sound of" | | 1 | "It was the heavy, deliberate" | | 2 | "She set the rag down." | | 3 | "He did not move from" | | 4 | "His hazel eyes tracked the" | | 5 | "He wore a coat that" | | 6 | "His face was a map" | | 7 | "She swallowed it down." | | 8 | "You look different, she said." | | 9 | "It was not a smile." | | 10 | "His voice was rougher, stripped" | | 11 | "He reached into his pocket." | | 12 | "He kept his hand near" | | 13 | "We haven't met in seven" | | 14 | "He pulled out a folded" | | 15 | "He laid it on the" | | 16 | "It was a photograph." | | 17 | "He leaned in." | | 18 | "It was the smell of" | | 19 | "I thought you were dead," |
| | ratio | 0.567 | |
| 13.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 258 | | totalSentences | 289 | | matches | | 0 | "The bell above the door" | | 1 | "Aurora did not look up" | | 2 | "She knew the sound of" | | 3 | "This was neither." | | 4 | "It was the heavy, deliberate" | | 5 | "She set the rag down." | | 6 | "The cloth left a wet" | | 7 | "Silas stood behind the bar," | | 8 | "He did not move from" | | 9 | "His hazel eyes tracked the" | | 10 | "The man stopped three paces" | | 11 | "He wore a coat that" | | 12 | "His face was a map" | | 13 | "Aurora recognised the jawline before" | | 14 | "The name sat heavy in" | | 15 | "She swallowed it down." | | 16 | "You look different, she said." | | 17 | "The corner of his mouth" | | 18 | "It was not a smile." | | 19 | "Time has a way of" |
| | ratio | 0.893 | |
| 69.20% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 289 | | matches | | 0 | "Because they are coming, Liam" | | 1 | "If we stay, they will" | | 2 | "Because the exit is blocked," | | 3 | "To Cardiff, she said." |
| | ratio | 0.014 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 2 | | matches | | 0 | "It was the heavy, deliberate footfall of someone who owned the floorboards." | | 1 | "They felt the shift in the atmosphere, the sudden drop in pressure that preceded a storm." |
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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 | |