| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.259 | | leniency | 0.519 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1360 | | totalAiIsmAdverbs | 3 | | 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) | |
| 8.09% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1360 | | totalAiIsms | 25 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "scanned" | | 1 | "fleeting" | | 2 | "pulse" | | 3 | "resolved" | | 4 | "familiar" | | 5 | "symphony" | | 6 | "sense of" | | 7 | "raced" | | 8 | "stark" | | 9 | "determined" | | 10 | "whisper" | | 11 | "jaw clenched" | | 12 | "unwavering" | | 13 | "racing" | | 14 | "oppressive" | | 15 | "pounding" | | 16 | "cacophony" | | 17 | "calculating" | | 18 | "depths" |
| |
| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 4 | | maxInWindow | 4 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 3 |
| | 1 | | label | "jaw/fists clenched" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" | | 2 | "jaw clenched" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 90 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1355 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1089 | | uniqueNames | 13 | | maxNameDensity | 1.84 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 20 | | Morris | 2 | | Raven | 2 | | Nest | 2 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Tomás | 5 | | Veil | 4 | | Market | 5 | | Tube | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Tomás" |
| | places | | | globalScore | 0.582 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 78 | | 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 | 1355 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 35.66 | | std | 24.19 | | cv | 0.678 | | sampleLengths | | 0 | 79 | | 1 | 101 | | 2 | 79 | | 3 | 67 | | 4 | 69 | | 5 | 10 | | 6 | 12 | | 7 | 19 | | 8 | 59 | | 9 | 16 | | 10 | 21 | | 11 | 16 | | 12 | 18 | | 13 | 6 | | 14 | 16 | | 15 | 53 | | 16 | 11 | | 17 | 23 | | 18 | 54 | | 19 | 59 | | 20 | 13 | | 21 | 20 | | 22 | 23 | | 23 | 21 | | 24 | 23 | | 25 | 15 | | 26 | 35 | | 27 | 52 | | 28 | 10 | | 29 | 37 | | 30 | 36 | | 31 | 16 | | 32 | 57 | | 33 | 65 | | 34 | 62 | | 35 | 15 | | 36 | 27 | | 37 | 40 |
| |
| 93.57% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 90 | | matches | | 0 | "been lost" | | 1 | "was involved" | | 2 | "was bathed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 164 | | matches | | 0 | "was, slipping" | | 1 | "was standing" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 110 | | ratio | 0.009 | | matches | | 0 | "The Raven's Nest was more than just a bar; it was a hub for the city's underworld, a place where the lines between human and supernatural blurred." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1093 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.03110704483074108 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.01463860933211345 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 12.32 | | std | 5.88 | | cv | 0.477 | | sampleLengths | | 0 | 16 | | 1 | 21 | | 2 | 16 | | 3 | 26 | | 4 | 10 | | 5 | 15 | | 6 | 22 | | 7 | 12 | | 8 | 18 | | 9 | 24 | | 10 | 13 | | 11 | 7 | | 12 | 20 | | 13 | 22 | | 14 | 17 | | 15 | 10 | | 16 | 27 | | 17 | 10 | | 18 | 20 | | 19 | 13 | | 20 | 13 | | 21 | 12 | | 22 | 7 | | 23 | 12 | | 24 | 12 | | 25 | 10 | | 26 | 6 | | 27 | 6 | | 28 | 5 | | 29 | 14 | | 30 | 12 | | 31 | 18 | | 32 | 9 | | 33 | 3 | | 34 | 17 | | 35 | 11 | | 36 | 5 | | 37 | 8 | | 38 | 13 | | 39 | 3 | | 40 | 13 | | 41 | 10 | | 42 | 8 | | 43 | 3 | | 44 | 3 | | 45 | 2 | | 46 | 14 | | 47 | 3 | | 48 | 16 | | 49 | 20 |
| |
| 43.64% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.2727272727272727 | | totalSentences | 110 | | uniqueOpeners | 30 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 89 | | matches | | 0 | "Her eyes, a sharp brown," | | 1 | "Her worn leather watch, ticking" | | 2 | "She pushed herself harder, the" | | 3 | "She had been here before," | | 4 | "She moved to the bar," | | 5 | "he asked, his voice low" | | 6 | "His Saint Christopher medallion glinted" | | 7 | "she said, her tone a" | | 8 | "He glanced around, his warm" | | 9 | "she said, her voice firm" | | 10 | "She pushed the heavy wooden" | | 11 | "She took a deep breath" | | 12 | "It moved every full moon," | | 13 | "she demanded, her voice low" | | 14 | "She wasn't afraid of crossing" | | 15 | "she said, her voice unwavering" | | 16 | "She took a deep breath," | | 17 | "She couldn't let the suspect" | | 18 | "She moved quickly, following the" | | 19 | "She emerged into a vast," |
| | ratio | 0.281 | |
| 10.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 80 | | totalSentences | 89 | | matches | | 0 | "The rain fell in relentless" | | 1 | "Detective Harlow Quinn, her closely" | | 2 | "Her eyes, a sharp brown," | | 3 | "The silhouette of the suspect," | | 4 | "Quinn's breath came in ragged" | | 5 | "Her worn leather watch, ticking" | | 6 | "The city around her was" | | 7 | "She pushed herself harder, the" | | 8 | "Morris, her partner, had been" | | 9 | "The supernatural element had always" | | 10 | "Quinn followed, her boots splashing" | | 11 | "The alley opened into a" | | 12 | "The dimly lit bar, a" | | 13 | "Quinn hesitated for a moment," | | 14 | "The Raven's Nest was more" | | 15 | "She had been here before," | | 16 | "The door creaked open, and" | | 17 | "The bar was a symphony" | | 18 | "Quinn's eyes adjusted to the" | | 19 | "The suspect was nowhere to" |
| | ratio | 0.899 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 89 | | matches | | 0 | "Before she could press further," | | 1 | "If the suspect had gone" | | 2 | "If the suspect was involved" |
| | ratio | 0.034 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 1 | | matches | | 0 | "The hidden room was likely part of the underground network that supported the city's shadowy dealings." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 7 | | matches | | 0 | "he asked, his voice low and gravelly" | | 1 | "she said, her tone a mix of surprise and suspicion" | | 2 | "she said, her voice firm" | | 3 | "the suspect said, his voice calm and controlled" | | 4 | "she demanded, her voice low and threatening" | | 5 | "she said, her voice unwavering" | | 6 | "she called, her voice cutting through the cacophony of the market" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 27 | | tagDensity | 0.259 | | leniency | 0.519 | | rawRatio | 0.143 | | effectiveRatio | 0.074 | |