| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1052 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1052 | | totalAiIsms | 29 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | |
| | highlights | | 0 | "scanned" | | 1 | "resolve" | | 2 | "calculated" | | 3 | "quickened" | | 4 | "depths" | | 5 | "footsteps" | | 6 | "echoing" | | 7 | "intrigue" | | 8 | "unwavering" | | 9 | "cacophony" | | 10 | "surreal" | | 11 | "familiar" | | 12 | "could feel" | | 13 | "flicker" | | 14 | "weight" | | 15 | "otherworldly" | | 16 | "reminder" | | 17 | "tension" | | 18 | "unspoken" | | 19 | "determined" | | 20 | "calculating" | | 21 | "tantalizing" | | 22 | "racing" |
| |
| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 4 | | maxInWindow | 4 | | found | | 0 | | label | "heart pounded in chest" | | count | 1 |
| | 1 | | label | "eyes widened/narrowed" | | count | 1 |
| | 2 | | label | "air was thick with" | | count | 1 |
| | 3 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "heart pounded in her chest" | | 1 | "eyes narrowed" | | 2 | "the air was thick with" | | 3 | "a flicker of recognition" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 70 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 70 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 72 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1050 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.50% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1018 | | uniqueNames | 11 | | maxNameDensity | 1.67 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Harlow | 2 | | Quinn | 17 | | Morris | 1 | | Veil | 2 | | Market | 2 | | Tomás | 1 | | Herrera | 1 | | Detective | 2 |
| | persons | | 0 | "Raven" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Tomás" | | 5 | "Herrera" |
| | places | | | globalScore | 0.665 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 67 | | glossingSentenceCount | 1 | | matches | | 0 | "something akin to respect" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1050 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 43.86% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 40.38 | | std | 12.26 | | cv | 0.304 | | sampleLengths | | 0 | 64 | | 1 | 64 | | 2 | 46 | | 3 | 55 | | 4 | 39 | | 5 | 41 | | 6 | 53 | | 7 | 9 | | 8 | 47 | | 9 | 44 | | 10 | 43 | | 11 | 49 | | 12 | 44 | | 13 | 46 | | 14 | 42 | | 15 | 35 | | 16 | 34 | | 17 | 49 | | 18 | 35 | | 19 | 17 | | 20 | 27 | | 21 | 34 | | 22 | 38 | | 23 | 27 | | 24 | 36 | | 25 | 32 |
| |
| 95.24% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 70 | | matches | | 0 | "was said" | | 1 | "was determined" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 156 | | matches | | 0 | "was gaining" | | 1 | "was heading" |
| |
| 63.49% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 72 | | ratio | 0.028 | | matches | | 0 | "She caught a glimpse of movement—a flash of a dark coat disappearing around the corner." | | 1 | "As she moved, she caught sight of a familiar face—Tomás Herrera, the former paramedic turned underground healer." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1020 | | adjectiveStacks | 1 | | stackExamples | | 0 | "former paramedic turned underground healer." |
| | adverbCount | 20 | | adverbRatio | 0.0196078431372549 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.008823529411764706 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 94.40% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 14.58 | | std | 5.63 | | cv | 0.386 | | sampleLengths | | 0 | 23 | | 1 | 16 | | 2 | 11 | | 3 | 14 | | 4 | 17 | | 5 | 11 | | 6 | 22 | | 7 | 14 | | 8 | 15 | | 9 | 13 | | 10 | 18 | | 11 | 14 | | 12 | 12 | | 13 | 29 | | 14 | 7 | | 15 | 12 | | 16 | 20 | | 17 | 8 | | 18 | 20 | | 19 | 13 | | 20 | 18 | | 21 | 12 | | 22 | 23 | | 23 | 7 | | 24 | 2 | | 25 | 11 | | 26 | 14 | | 27 | 22 | | 28 | 3 | | 29 | 21 | | 30 | 20 | | 31 | 13 | | 32 | 17 | | 33 | 13 | | 34 | 18 | | 35 | 13 | | 36 | 18 | | 37 | 11 | | 38 | 17 | | 39 | 16 | | 40 | 17 | | 41 | 16 | | 42 | 13 | | 43 | 6 | | 44 | 25 | | 45 | 11 | | 46 | 20 | | 47 | 7 | | 48 | 8 | | 49 | 14 |
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| 44.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.2916666666666667 | | totalSentences | 72 | | uniqueOpeners | 21 | |
| 48.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 69 | | matches | | 0 | "Suddenly, the suspect veered left," |
| | ratio | 0.014 | |
| 63.48% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 69 | | matches | | 0 | "Her eyes, dark and focused," | | 1 | "She adjusted the worn leather" | | 2 | "She caught a glimpse of" | | 3 | "She rounded the corner, her" | | 4 | "She could hear the distant" | | 5 | "She followed him through the" | | 6 | "She was gaining on him," | | 7 | "She knew the stories of" | | 8 | "She reached the bottom, finding" | | 9 | "She had heard of it," | | 10 | "It was said to be" | | 11 | "She could see the suspect" | | 12 | "He moved with purpose, weaving" | | 13 | "He was heading deeper into" | | 14 | "She could feel the eyes" | | 15 | "He stood by a stall," | | 16 | "He nodded subtly, a silent" | | 17 | "She hesitated, the weight of" | | 18 | "She had come too far," | | 19 | "She could hear snippets of" |
| | ratio | 0.391 | |
| 32.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 69 | | matches | | 0 | "Rain lashed the streets of" | | 1 | "Detective Harlow Quinn stood beneath" | | 2 | "The rain had soaked through" | | 3 | "Her eyes, dark and focused," | | 4 | "The suspect had slipped out" | | 5 | "Quinn had been close, too" | | 6 | "She adjusted the worn leather" | | 7 | "The memory of him, and" | | 8 | "She caught a glimpse of" | | 9 | "Quinn set off, her steps" | | 10 | "The rain pounded against her," | | 11 | "The streets were nearly deserted," | | 12 | "Quinn's breath came in steady" | | 13 | "She rounded the corner, her" | | 14 | "Quinn quickened her pace, her" | | 15 | "The alley was narrow, the" | | 16 | "She could hear the distant" | | 17 | "The suspect was fast, but" | | 18 | "She followed him through the" | | 19 | "She was gaining on him," |
| | ratio | 0.855 | |
| 72.46% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 69 | | matches | | 0 | "To follow him meant stepping" |
| | ratio | 0.014 | |
| 79.08% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 5 | | matches | | 0 | "Rain lashed the streets of Soho, turning the pavement into a slick, reflective surface that mirrored the neon glow of The Raven's Nest." | | 1 | "The memory of him, and the case that had taken him, fueled her resolve." | | 2 | "He was heading deeper into the market, towards a section that seemed even more shadowed, more secretive." | | 3 | "She could hear snippets of their conversation, words that hinted at secrets and deals that defied the mundane." | | 4 | "The suspect smiled, a slow, calculating expression that spoke of secrets and shadows." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 3 | | matches | | 0 | "he said, his voice smooth and unhurried" | | 1 | "she replied, her voice steady" | | 2 | "he said, his words a tantalizing promise" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |