| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.316 | | leniency | 0.632 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1161 | | 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) | |
| 5.25% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1161 | | totalAiIsms | 22 | | found | | | highlights | | 0 | "shimmered" | | 1 | "beacon" | | 2 | "flicker" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "scanned" | | 6 | "intricate" | | 7 | "unreadable" | | 8 | "tension" | | 9 | "eyebrow" | | 10 | "could feel" | | 11 | "quickened" | | 12 | "pulse" | | 13 | "racing" | | 14 | "jaw clenched" | | 15 | "flickered" | | 16 | "pounding" |
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| 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 | "jaw/fists clenched" | | count | 1 |
| | 3 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "heart pounded in her chest" | | 1 | "eyes narrowed" | | 2 | "jaw clenched" | | 3 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 95 | | matches | | |
| 52.63% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 3 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1157 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 1054 | | uniqueNames | 11 | | maxNameDensity | 0.57 | | worstName | "Tomás" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 5 | | Quinn | 1 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Veil | 3 | | Market | 3 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Tomás | 6 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Herrera" | | 6 | "Tomás" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 61.76% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 3 | | matches | | 0 | "graffiti that seemed to shift under the flicker of her flashlight" | | 1 | "looked like human skin" | | 2 | "sounded like warnings" |
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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 | 1157 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 107 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 28.22 | | std | 18.44 | | cv | 0.653 | | sampleLengths | | 0 | 72 | | 1 | 64 | | 2 | 32 | | 3 | 70 | | 4 | 43 | | 5 | 50 | | 6 | 50 | | 7 | 11 | | 8 | 49 | | 9 | 51 | | 10 | 2 | | 11 | 56 | | 12 | 14 | | 13 | 14 | | 14 | 12 | | 15 | 19 | | 16 | 18 | | 17 | 16 | | 18 | 24 | | 19 | 3 | | 20 | 20 | | 21 | 44 | | 22 | 29 | | 23 | 11 | | 24 | 28 | | 25 | 30 | | 26 | 17 | | 27 | 20 | | 28 | 25 | | 29 | 7 | | 30 | 32 | | 31 | 17 | | 32 | 14 | | 33 | 12 | | 34 | 18 | | 35 | 12 | | 36 | 16 | | 37 | 38 | | 38 | 20 | | 39 | 57 | | 40 | 20 |
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| 90.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 95 | | matches | | 0 | "were lined" | | 1 | "were lined" | | 2 | "was clipped" | | 3 | "were gone" |
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| 96.37% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 193 | | matches | | 0 | "were heading" | | 1 | "wasn’t leaving" | | 2 | "was chasing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 107 | | ratio | 0.009 | | matches | | 0 | "Something caught her eye—a bone token, its surface carved with intricate runes." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1058 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.0330812854442344 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004725897920604915 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 10.81 | | std | 5.09 | | cv | 0.471 | | sampleLengths | | 0 | 16 | | 1 | 20 | | 2 | 21 | | 3 | 15 | | 4 | 9 | | 5 | 14 | | 6 | 15 | | 7 | 13 | | 8 | 13 | | 9 | 11 | | 10 | 4 | | 11 | 17 | | 12 | 13 | | 13 | 19 | | 14 | 18 | | 15 | 20 | | 16 | 8 | | 17 | 10 | | 18 | 11 | | 19 | 14 | | 20 | 6 | | 21 | 23 | | 22 | 11 | | 23 | 10 | | 24 | 13 | | 25 | 15 | | 26 | 11 | | 27 | 11 | | 28 | 6 | | 29 | 5 | | 30 | 16 | | 31 | 12 | | 32 | 13 | | 33 | 8 | | 34 | 10 | | 35 | 31 | | 36 | 10 | | 37 | 2 | | 38 | 6 | | 39 | 9 | | 40 | 16 | | 41 | 25 | | 42 | 2 | | 43 | 6 | | 44 | 6 | | 45 | 14 | | 46 | 4 | | 47 | 8 | | 48 | 12 | | 49 | 7 |
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| 38.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.2803738317757009 | | totalSentences | 107 | | uniqueOpeners | 30 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 94 | | matches | | 0 | "Then he sighed, shaking his" | | 1 | "Then she moved past him," | | 2 | "Then they disappeared into a" | | 3 | "Then they reached into their" | | 4 | "Then she stepped past him," | | 5 | "Then she stepped forward, her" |
| | ratio | 0.064 | |
| 66.81% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 94 | | matches | | 0 | "She gritted her teeth and" | | 1 | "She slid around the corner," | | 2 | "She adjusted her worn leather" | | 3 | "Her voice cut through the" | | 4 | "She followed, her military precision" | | 5 | "They were heading toward The" | | 6 | "Her instincts screamed caution, but" | | 7 | "She took the steps two" | | 8 | "She followed the sound, her" | | 9 | "She’d heard whispers of it," | | 10 | "Her heart pounded in her" | | 11 | "She stepped forward, her boots" | | 12 | "Her sharp gaze swept over" | | 13 | "She turned, her hand instinctively" | | 14 | "He wore a Saint Christopher" | | 15 | "She recognized him from the" | | 16 | "Her voice was calm, but" | | 17 | "She didn’t respond, her gaze" | | 18 | "Her tone was clipped, her" | | 19 | "He hesitated, then gestured to" |
| | ratio | 0.383 | |
| 39.79% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 94 | | matches | | 0 | "The rain hammered down, turning" | | 1 | "Detective Harlow Quinn sprinted across" | | 2 | "She gritted her teeth and" | | 3 | "The alley was narrow, the" | | 4 | "She slid around the corner," | | 5 | "The suspect was already halfway" | | 6 | "Harlow’s breath came in sharp" | | 7 | "She adjusted her worn leather" | | 8 | "Her voice cut through the" | | 9 | "The figure didn’t slow." | | 10 | "She followed, her military precision" | | 11 | "Water dripped from her closely" | | 12 | "They were heading toward The" | | 13 | "Harlow hesitated for a fraction" | | 14 | "The stairs were steep, the" | | 15 | "Her instincts screamed caution, but" | | 16 | "She took the steps two" | | 17 | "The underground was a different" | | 18 | "The air smelled damp and" | | 19 | "The suspect was still ahead," |
| | ratio | 0.84 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 94 | | matches | | 0 | "If anything, they moved faster," | | 1 | "Before she could react, they" |
| | ratio | 0.021 | |
| 76.72% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 5 | | matches | | 0 | "Detective Harlow Quinn sprinted across the road, her boots splashing through puddles that shimmered like oil under the flickering streetlights." | | 1 | "The suspect was already halfway down, their silhouette weaving through discarded crates and overflowing bins." | | 2 | "But instead of stopping, the figure veered left, disappearing down a set of stairs that led into the dark below." | | 3 | "The air smelled damp and stale, and the walls were lined with graffiti that seemed to shift under the flicker of her flashlight." | | 4 | "She quickened her pace, her boots splashing through puddles that glimmered with an unnatural light." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 4 | | matches | | 0 | "Tomás said, his voice low" | | 1 | "she called out, her voice echoing in the cavernous space" | | 2 | "she said, her voice steady despite the adrenaline coursing through her veins" | | 3 | "she said, her voice firm" |
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| 97.37% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "she called out (call out)" |
| | dialogueSentences | 19 | | tagDensity | 0.211 | | leniency | 0.421 | | rawRatio | 0.25 | | effectiveRatio | 0.105 | |