| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 72 | | tagDensity | 0.278 | | leniency | 0.556 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.52% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1437 | | 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) | |
| 37.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1437 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "chill" | | 1 | "footsteps" | | 2 | "weight" | | 3 | "silence" | | 4 | "scanned" | | 5 | "warmth" | | 6 | "dancing" | | 7 | "traced" | | 8 | "tension" | | 9 | "reminder" | | 10 | "flickered" | | 11 | "gloom" | | 12 | "gleaming" | | 13 | "throbbed" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "hung in the air" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 167 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 167 | | filterMatches | (empty) | | 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 | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1437 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 1131 | | uniqueNames | 7 | | maxNameDensity | 1.33 | | worstName | "Cian" | | maxWindowNameDensity | 4 | | worstWindowName | "Cian" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Cardiff | 1 | | Silas | 14 | | Aurora | 9 | | Cian | 15 |
| | persons | | 0 | "Raven" | | 1 | "Silas" | | 2 | "Aurora" | | 3 | "Cian" |
| | places | | | globalScore | 0.837 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 86 | | 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 | 1437 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 219 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 96 | | mean | 14.97 | | std | 15.89 | | cv | 1.062 | | sampleLengths | | 0 | 108 | | 1 | 67 | | 2 | 18 | | 3 | 15 | | 4 | 2 | | 5 | 53 | | 6 | 46 | | 7 | 39 | | 8 | 1 | | 9 | 47 | | 10 | 16 | | 11 | 3 | | 12 | 5 | | 13 | 36 | | 14 | 3 | | 15 | 17 | | 16 | 24 | | 17 | 7 | | 18 | 6 | | 19 | 11 | | 20 | 1 | | 21 | 34 | | 22 | 5 | | 23 | 19 | | 24 | 10 | | 25 | 38 | | 26 | 3 | | 27 | 16 | | 28 | 1 | | 29 | 6 | | 30 | 32 | | 31 | 1 | | 32 | 16 | | 33 | 9 | | 34 | 31 | | 35 | 4 | | 36 | 1 | | 37 | 15 | | 38 | 18 | | 39 | 9 | | 40 | 3 | | 41 | 16 | | 42 | 23 | | 43 | 12 | | 44 | 28 | | 45 | 4 | | 46 | 9 | | 47 | 20 | | 48 | 13 | | 49 | 5 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 167 | | matches | | 0 | "been carved" | | 1 | "was gone" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 217 | | 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 | 1134 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.021164021164021163 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006172839506172839 | |
| 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 | 6.56 | | std | 4.57 | | cv | 0.696 | | sampleLengths | | 0 | 18 | | 1 | 23 | | 2 | 13 | | 3 | 24 | | 4 | 14 | | 5 | 16 | | 6 | 17 | | 7 | 21 | | 8 | 4 | | 9 | 7 | | 10 | 18 | | 11 | 12 | | 12 | 6 | | 13 | 3 | | 14 | 12 | | 15 | 2 | | 16 | 2 | | 17 | 10 | | 18 | 20 | | 19 | 7 | | 20 | 14 | | 21 | 7 | | 22 | 6 | | 23 | 4 | | 24 | 11 | | 25 | 5 | | 26 | 13 | | 27 | 4 | | 28 | 12 | | 29 | 14 | | 30 | 9 | | 31 | 1 | | 32 | 9 | | 33 | 7 | | 34 | 3 | | 35 | 18 | | 36 | 10 | | 37 | 7 | | 38 | 5 | | 39 | 4 | | 40 | 3 | | 41 | 3 | | 42 | 2 | | 43 | 4 | | 44 | 4 | | 45 | 23 | | 46 | 5 | | 47 | 3 | | 48 | 7 | | 49 | 9 |
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| 32.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 38 | | diversityRatio | 0.2009132420091324 | | totalSentences | 219 | | uniqueOpeners | 44 | |
| 46.95% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 142 | | matches | | 0 | "Then the door opened again." | | 1 | "Really looked at her." |
| | ratio | 0.014 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 87 | | totalSentences | 142 | | matches | | 0 | "She pulled her collar up" | | 1 | "His grey-streaked auburn hair caught" | | 2 | "He didn’t look up." | | 3 | "He knew the rhythm of" | | 4 | "She walked to the end" | | 5 | "His voice was gravel, worn" | | 6 | "He set the glass down" | | 7 | "He knew the story behind" | | 8 | "He knew the stories behind" | | 9 | "She scanned the room." | | 10 | "He wore a tailored coat" | | 11 | "His hair was cut short," | | 12 | "He scanned the room, his" | | 13 | "He hadn’t changed in the" | | 14 | "He hadn’t gained weight or" | | 15 | "He had sharpened." | | 16 | "He walked toward the bar," | | 17 | "He stopped two stools down" | | 18 | "He didn’t look at her." | | 19 | "He looked at Silas." |
| | ratio | 0.613 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 137 | | totalSentences | 142 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Aurora pushed through the heavy" | | 2 | "She pulled her collar up" | | 3 | "The air inside smelled of" | | 4 | "Maps covered the walls, yellowed" | | 5 | "Silas stood behind the bar," | | 6 | "His grey-streaked auburn hair caught" | | 7 | "He didn’t look up." | | 8 | "He knew the rhythm of" | | 9 | "The slight limp in his" | | 10 | "She walked to the end" | | 11 | "The leather creaked under her" | | 12 | "His voice was gravel, worn" | | 13 | "The amber liquid hit the" | | 14 | "He set the glass down" | | 15 | "He knew the story behind" | | 16 | "He knew the stories behind" | | 17 | "Aurora wrapped her hands around" | | 18 | "The heat seeped into her" | | 19 | "She scanned the room." |
| | ratio | 0.965 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 142 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 2 | | matches | | 0 | "The air inside smelled of stale tobacco and polished wood, a scent that had settled into the grain of The Raven’s Nest over decades." | | 1 | "Silas stood behind the bar, polishing a glass with a rag that looked older than the building." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 19 | | fancyCount | 2 | | fancyTags | | 0 | "He repeated (repeat)" | | 1 | "Aurora repeated (repeat)" |
| | dialogueSentences | 72 | | tagDensity | 0.264 | | leniency | 0.528 | | rawRatio | 0.105 | | effectiveRatio | 0.056 | |