| 75.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 3 | | adverbTags | | 0 | "Silas said softly [softly]" | | 1 | "Silas turned back [back]" | | 2 | "she lied again [again]" |
| | dialogueSentences | 48 | | tagDensity | 0.208 | | leniency | 0.417 | | rawRatio | 0.3 | | effectiveRatio | 0.125 | |
| 87.60% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1613 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "barely above a whisper" | | count | 1 |
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| | highlights | | 0 | "slightly" | | 1 | "really" | | 2 | "softly" | | 3 | "barely above a whisper" |
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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) | |
| 50.40% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1613 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "warmth" | | 1 | "chill" | | 2 | "ensure" | | 3 | "weight" | | 4 | "familiar" | | 5 | "flickered" | | 6 | "glinting" | | 7 | "tracing" | | 8 | "grave" | | 9 | "whisper" | | 10 | "profound" |
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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 | 113 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 113 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 151 | | 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 | 1604 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 1233 | | uniqueNames | 14 | | maxNameDensity | 0.89 | | worstName | "Silas" | | maxWindowNameDensity | 2 | | worstWindowName | "Silas" | | discoveredNames | | London | 4 | | Carter | 1 | | Soho | 2 | | Silas | 11 | | Blackwood | 1 | | Prague | 1 | | Evan | 2 | | Yu-Fei | 1 | | Cardiff | 1 | | Brendan | 1 | | Jennifer | 1 | | Islington | 1 | | Nest | 1 | | Rory | 3 |
| | persons | | 0 | "Carter" | | 1 | "Silas" | | 2 | "Blackwood" | | 3 | "Evan" | | 4 | "Yu-Fei" | | 5 | "Brendan" | | 6 | "Jennifer" | | 7 | "Rory" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Prague" | | 3 | "Cardiff" | | 4 | "Islington" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.623 | | wordCount | 1604 | | matches | | 0 | "Not the night you ran to Eva's, but the weeks after" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 151 | | matches | | 0 | "know that reaction" | | 1 | "offer that comfort" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 65 | | mean | 24.68 | | std | 23.6 | | cv | 0.956 | | sampleLengths | | 0 | 69 | | 1 | 63 | | 2 | 58 | | 3 | 70 | | 4 | 1 | | 5 | 26 | | 6 | 34 | | 7 | 8 | | 8 | 16 | | 9 | 2 | | 10 | 74 | | 11 | 8 | | 12 | 35 | | 13 | 5 | | 14 | 2 | | 15 | 21 | | 16 | 83 | | 17 | 2 | | 18 | 4 | | 19 | 25 | | 20 | 5 | | 21 | 52 | | 22 | 51 | | 23 | 8 | | 24 | 2 | | 25 | 4 | | 26 | 29 | | 27 | 4 | | 28 | 47 | | 29 | 7 | | 30 | 2 | | 31 | 4 | | 32 | 47 | | 33 | 5 | | 34 | 33 | | 35 | 43 | | 36 | 3 | | 37 | 18 | | 38 | 44 | | 39 | 3 | | 40 | 52 | | 41 | 32 | | 42 | 20 | | 43 | 70 | | 44 | 6 | | 45 | 8 | | 46 | 51 | | 47 | 8 | | 48 | 1 | | 49 | 1 |
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| 99.05% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 113 | | matches | | 0 | "being followed" | | 1 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 206 | | matches | | 0 | "was wiping" | | 1 | "was squatting" | | 2 | "was watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 151 | | ratio | 0.013 | | matches | | 0 | "Rory watched his hands—gnarled, veins blue against the skin, the ring she’d admired as a teenager, a heavy silver band, now a symbol of a past life she had run from." | | 1 | "But something in her chest had felt wrong lately—a tightness that tea couldn't fix." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1241 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.0249798549556809 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.009669621273166801 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 151 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 151 | | mean | 10.62 | | std | 8.68 | | cv | 0.818 | | sampleLengths | | 0 | 15 | | 1 | 17 | | 2 | 17 | | 3 | 20 | | 4 | 9 | | 5 | 4 | | 6 | 15 | | 7 | 35 | | 8 | 11 | | 9 | 21 | | 10 | 26 | | 11 | 9 | | 12 | 18 | | 13 | 11 | | 14 | 4 | | 15 | 4 | | 16 | 7 | | 17 | 17 | | 18 | 1 | | 19 | 10 | | 20 | 4 | | 21 | 12 | | 22 | 34 | | 23 | 5 | | 24 | 3 | | 25 | 9 | | 26 | 5 | | 27 | 2 | | 28 | 2 | | 29 | 5 | | 30 | 5 | | 31 | 31 | | 32 | 13 | | 33 | 20 | | 34 | 4 | | 35 | 4 | | 36 | 25 | | 37 | 10 | | 38 | 5 | | 39 | 2 | | 40 | 11 | | 41 | 10 | | 42 | 18 | | 43 | 5 | | 44 | 11 | | 45 | 19 | | 46 | 14 | | 47 | 16 | | 48 | 2 | | 49 | 4 |
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| 30.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 30 | | diversityRatio | 0.26490066225165565 | | totalSentences | 151 | | uniqueOpeners | 40 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 108 | | matches | | 0 | "Really looked at her." | | 1 | "Maybe being a messenger for" |
| | ratio | 0.019 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 69 | | totalSentences | 108 | | matches | | 0 | "She didn’t look back out" | | 1 | "She knew the routine." | | 2 | "She knew the reflex to" | | 3 | "It was muscle memory now," | | 4 | "It smelled of lemon polish," | | 5 | "She walked to the far" | | 6 | "He didn’t turn immediately." | | 7 | "He knew her gait." | | 8 | "He knew the hesitation before" | | 9 | "He’d taught her enough about" | | 10 | "He didn't look up." | | 11 | "His hand tightened on the" | | 12 | "His voice was lower than" | | 13 | "She slid onto the stool." | | 14 | "Her voice was steady, flat." | | 15 | "He put the glass down." | | 16 | "It clinked against the wood." | | 17 | "He turned, his movements fluid" | | 18 | "He looked at her." | | 19 | "He laughed, a short, sharp" |
| | ratio | 0.639 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 96 | | totalSentences | 108 | | matches | | 0 | "The neon green sign above" | | 1 | "The bell above the frame" | | 2 | "The door pushed open, letting" | | 3 | "Rory Carter stepped inside, the" | | 4 | "She didn’t look back out" | | 5 | "She knew the routine." | | 6 | "She knew the reflex to" | | 7 | "It was muscle memory now," | | 8 | "The room inside was a" | | 9 | "The walls, covered in peeling" | | 10 | "It smelled of lemon polish," | | 11 | "She walked to the far" | | 12 | "The floorboards groaned under her" | | 13 | "Silas Blackwood was wiping down" | | 14 | "He didn’t turn immediately." | | 15 | "He knew her gait." | | 16 | "He knew the hesitation before" | | 17 | "He’d taught her enough about" | | 18 | "The single word was rough," | | 19 | "He didn't look up." |
| | ratio | 0.889 | |
| 92.59% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 108 | | matches | | 0 | "Before she’d met Silas." | | 1 | "Before she’d run to London." |
| | ratio | 0.019 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 1 | | matches | | 0 | "The blue light of the sign overhead flickered, casting long shadows across his grey-streaked auburn hair and the neatly trimmed beard that matched in color." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 3 | | matches | | 0 | "He trailed, his hand tracing the edge of a coaster" | | 1 | "he said, his voice barely above a whisper" | | 2 | "he said, not looking at her" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "she lied (lie)" | | 1 | "she lied again (lie)" |
| | dialogueSentences | 48 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0.333 | | effectiveRatio | 0.083 | |