| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 1 | | adverbTags | | 0 | "Silas turned back [back]" |
| | dialogueSentences | 67 | | tagDensity | 0.313 | | leniency | 0.627 | | rawRatio | 0.048 | | effectiveRatio | 0.03 | |
| 89.44% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1421 | | totalAiIsmAdverbs | 3 | | 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) | |
| 64.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1421 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "scanning" | | 1 | "weight" | | 2 | "familiar" | | 3 | "flicked" | | 4 | "echoed" | | 5 | "silence" |
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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 | 86 | | matches | (empty) | |
| 93.02% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 86 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 132 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1421 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 39 | | wordCount | 868 | | uniqueNames | 8 | | maxNameDensity | 2.42 | | worstName | "Silas" | | maxWindowNameDensity | 5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Europe | 1 | | London | 1 | | Evan | 1 | | Silas | 21 | | Aurora | 12 | | Prague | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Evan" | | 3 | "Silas" | | 4 | "Aurora" |
| | places | | 0 | "Europe" | | 1 | "London" | | 2 | "Prague" |
| | globalScore | 0.29 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a cage, from the expectations" |
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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 | 1421 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 132 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 55 | | mean | 25.84 | | std | 23.64 | | cv | 0.915 | | sampleLengths | | 0 | 108 | | 1 | 82 | | 2 | 93 | | 3 | 52 | | 4 | 20 | | 5 | 47 | | 6 | 27 | | 7 | 28 | | 8 | 2 | | 9 | 8 | | 10 | 5 | | 11 | 34 | | 12 | 6 | | 13 | 49 | | 14 | 3 | | 15 | 30 | | 16 | 59 | | 17 | 20 | | 18 | 20 | | 19 | 34 | | 20 | 5 | | 21 | 28 | | 22 | 6 | | 23 | 2 | | 24 | 37 | | 25 | 22 | | 26 | 33 | | 27 | 46 | | 28 | 3 | | 29 | 25 | | 30 | 4 | | 31 | 15 | | 32 | 3 | | 33 | 2 | | 34 | 19 | | 35 | 39 | | 36 | 18 | | 37 | 7 | | 38 | 3 | | 39 | 14 | | 40 | 50 | | 41 | 3 | | 42 | 34 | | 43 | 6 | | 44 | 4 | | 45 | 62 | | 46 | 49 | | 47 | 12 | | 48 | 11 | | 49 | 37 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 86 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 143 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 132 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 871 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.029850746268656716 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.008036739380022962 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 132 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 132 | | mean | 10.77 | | std | 6.95 | | cv | 0.645 | | sampleLengths | | 0 | 15 | | 1 | 17 | | 2 | 17 | | 3 | 13 | | 4 | 25 | | 5 | 21 | | 6 | 16 | | 7 | 19 | | 8 | 31 | | 9 | 16 | | 10 | 14 | | 11 | 10 | | 12 | 8 | | 13 | 21 | | 14 | 35 | | 15 | 5 | | 16 | 5 | | 17 | 12 | | 18 | 9 | | 19 | 4 | | 20 | 5 | | 21 | 17 | | 22 | 9 | | 23 | 11 | | 24 | 6 | | 25 | 6 | | 26 | 21 | | 27 | 14 | | 28 | 18 | | 29 | 9 | | 30 | 14 | | 31 | 4 | | 32 | 6 | | 33 | 4 | | 34 | 2 | | 35 | 8 | | 36 | 5 | | 37 | 12 | | 38 | 7 | | 39 | 15 | | 40 | 6 | | 41 | 15 | | 42 | 12 | | 43 | 22 | | 44 | 3 | | 45 | 11 | | 46 | 7 | | 47 | 12 | | 48 | 8 | | 49 | 4 |
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| 36.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.2196969696969697 | | totalSentences | 132 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 49.27% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 82 | | matches | | 0 | "She paused just inside the" | | 1 | "His grey-streaked auburn hair was" | | 2 | "He moved with a deliberate" | | 3 | "She tucked her hand into" | | 4 | "She had not seen him" | | 5 | "She had told herself she" | | 6 | "She had not told Silas." | | 7 | "He set it down on" | | 8 | "His gaze locked onto hers," | | 9 | "He did not smile." | | 10 | "He did not look surprised." | | 11 | "He looked resigned, as if" | | 12 | "His voice was lower, rougher" | | 13 | "She stopped at a stool" | | 14 | "He glanced at her left" | | 15 | "He saw the scar." | | 16 | "His eyes flicked back to" | | 17 | "He pushed a bottle of" | | 18 | "She poured a measure, the" | | 19 | "She didn't drink." |
| | ratio | 0.427 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 82 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Aurora pushed through the heavy" | | 2 | "The air inside smelled of" | | 3 | "She paused just inside the" | | 4 | "The Raven's Nest looked exactly" | | 5 | "The walls, plastered with faded" | | 6 | "Silas stood behind the bar," | | 7 | "His grey-streaked auburn hair was" | | 8 | "The neat beard covered the" | | 9 | "He moved with a deliberate" | | 10 | "Aurora felt the familiar pull" | | 11 | "She tucked her hand into" | | 12 | "She had not seen him" | | 13 | "She had told herself she" | | 14 | "She had not told Silas." | | 15 | "Silas stopped wiping the glass." | | 16 | "He set it down on" | | 17 | "His gaze locked onto hers," | | 18 | "He did not smile." | | 19 | "He did not look surprised." |
| | ratio | 0.988 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 3 | | matches | | 0 | "His grey-streaked auburn hair was shorter than she recalled, pulled back from a face that had deepened in shadow." | | 1 | "The neat beard covered the lower half of his face, but the hazel eyes remained sharp, scanning the room with the instinct of a man who spent too long watching c…" | | 2 | "He reached for a rag and began wiping a spot on the counter that was already clean." |
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| 77.38% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 2 | | matches | | 0 | "Silas turned back, his hand gripping the handle" | | 1 | "Silas replied, his voice barely audible over the pour" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 67 | | tagDensity | 0.075 | | leniency | 0.149 | | rawRatio | 0 | | effectiveRatio | 0 | |