| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva’s voice caught then [then]" |
| | dialogueSentences | 212 | | tagDensity | 0.085 | | leniency | 0.17 | | rawRatio | 0.056 | | effectiveRatio | 0.009 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2334 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 82.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2334 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "warmth" | | 1 | "familiar" | | 2 | "flicked" | | 3 | "flicker" | | 4 | "trembled" | | 5 | "silence" | | 6 | "pulsed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "flicker of emotion" | | count | 1 |
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| | highlights | | 0 | "A flicker of amusement" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 136 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 136 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 330 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 52 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2334 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 47 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 98 | | wordCount | 1115 | | uniqueNames | 8 | | maxNameDensity | 4.04 | | worstName | "Eva" | | maxWindowNameDensity | 7 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Silas | 11 | | Rory | 37 | | Charing | 1 | | Cross | 1 | | Eva | 45 | | Cardiff | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Silas" | | 3 | "Rory" | | 4 | "Eva" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like the old one for a second: qui" |
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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 | 2334 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 330 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 243 | | mean | 9.6 | | std | 10.84 | | cv | 1.128 | | sampleLengths | | 0 | 35 | | 1 | 17 | | 2 | 29 | | 3 | 38 | | 4 | 4 | | 5 | 20 | | 6 | 5 | | 7 | 4 | | 8 | 9 | | 9 | 15 | | 10 | 7 | | 11 | 19 | | 12 | 65 | | 13 | 9 | | 14 | 11 | | 15 | 1 | | 16 | 9 | | 17 | 1 | | 18 | 4 | | 19 | 38 | | 20 | 8 | | 21 | 3 | | 22 | 11 | | 23 | 3 | | 24 | 1 | | 25 | 12 | | 26 | 18 | | 27 | 2 | | 28 | 2 | | 29 | 2 | | 30 | 2 | | 31 | 1 | | 32 | 4 | | 33 | 1 | | 34 | 1 | | 35 | 2 | | 36 | 26 | | 37 | 13 | | 38 | 3 | | 39 | 2 | | 40 | 9 | | 41 | 9 | | 42 | 17 | | 43 | 5 | | 44 | 6 | | 45 | 6 | | 46 | 4 | | 47 | 6 | | 48 | 4 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 136 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 201 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 330 | | ratio | 0.003 | | matches | | 0 | "His grey-streaked hair caught the amber light; his silver signet ring flashed as he turned the cloth." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1118 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.027728085867620753 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 330 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 330 | | mean | 7.07 | | std | 6.11 | | cv | 0.864 | | sampleLengths | | 0 | 16 | | 1 | 19 | | 2 | 17 | | 3 | 3 | | 4 | 26 | | 5 | 10 | | 6 | 17 | | 7 | 11 | | 8 | 4 | | 9 | 20 | | 10 | 5 | | 11 | 4 | | 12 | 5 | | 13 | 4 | | 14 | 8 | | 15 | 7 | | 16 | 7 | | 17 | 17 | | 18 | 2 | | 19 | 14 | | 20 | 22 | | 21 | 16 | | 22 | 13 | | 23 | 9 | | 24 | 4 | | 25 | 7 | | 26 | 1 | | 27 | 9 | | 28 | 1 | | 29 | 4 | | 30 | 5 | | 31 | 13 | | 32 | 20 | | 33 | 8 | | 34 | 3 | | 35 | 11 | | 36 | 3 | | 37 | 1 | | 38 | 12 | | 39 | 8 | | 40 | 7 | | 41 | 3 | | 42 | 2 | | 43 | 2 | | 44 | 2 | | 45 | 2 | | 46 | 1 | | 47 | 4 | | 48 | 1 | | 49 | 1 |
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| 45.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.21515151515151515 | | totalSentences | 330 | | uniqueOpeners | 71 | |
| 29.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 114 | | matches | | | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 114 | | matches | | 0 | "she muttered to the lock" | | 1 | "He was wiping down a" | | 2 | "His grey-streaked hair caught the" | | 3 | "She wore a dark green" | | 4 | "Her hair, once a heavy" | | 5 | "Her smile faltered, then returned" | | 6 | "She had been taller than" | | 7 | "He moved to the taps." | | 8 | "It looked like the old" | | 9 | "She tucked the pale hair" | | 10 | "His gaze passed over them" | | 11 | "She had spoken in careful" | | 12 | "She put her mug down." | | 13 | "Her fingers moved to the" | | 14 | "It took some of the" | | 15 | "Her thumb rubbed the edge" | | 16 | "He had disappeared through the" | | 17 | "She could hear a drawer" | | 18 | "He didn’t speak." | | 19 | "She could go up and" |
| | ratio | 0.193 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 114 | | matches | | 0 | "The green neon sign above" | | 1 | "Rory stood beneath it, counting" | | 2 | "she muttered to the lock" | | 3 | "The key turned." | | 4 | "The door gave way, and" | | 5 | "He was wiping down a" | | 6 | "His grey-streaked hair caught the" | | 7 | "A football match murmured from" | | 8 | "Silas set the glass aside." | | 9 | "Rory dropped her delivery bag" | | 10 | "A woman at the far" | | 11 | "The sound slipped between the" | | 12 | "The woman sat alone beneath" | | 13 | "She wore a dark green" | | 14 | "Her hair, once a heavy" | | 15 | "A white streak ran from" | | 16 | "Rory’s hand closed around the" | | 17 | "The woman looked up." | | 18 | "Her smile faltered, then returned" | | 19 | "Rory stared at her for" |
| | ratio | 0.947 | |
| 43.86% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 114 | | matches | | 0 | "Now she stood straight, shoulders" |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 1 | | matches | | 0 | "The scar on her left wrist caught against its handle, a pale crescent that vanished beneath her sleeve when she shifted." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "Rory added (add)" |
| | dialogueSentences | 212 | | tagDensity | 0.071 | | leniency | 0.142 | | rawRatio | 0.133 | | effectiveRatio | 0.019 | |