| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "Ames said quietly [quietly]" |
| | dialogueSentences | 92 | | tagDensity | 0.174 | | leniency | 0.348 | | rawRatio | 0.063 | | effectiveRatio | 0.022 | |
| 88.16% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2112 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "sharply" | | 1 | "slightly" | | 2 | "suddenly" |
| |
| 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) | |
| 66.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2112 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "velvet" | | 1 | "silence" | | 2 | "weight" | | 3 | "porcelain" | | 4 | "perfect" | | 5 | "shattered" | | 6 | "etched" | | 7 | "wavering" | | 8 | "scanned" | | 9 | "pulse" | | 10 | "flicked" |
| |
| 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 | 200 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 200 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 275 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2112 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 52.85% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 70 | | wordCount | 1647 | | uniqueNames | 10 | | maxNameDensity | 1.94 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 32 | | Soho | 1 | | Camden | 1 | | Kowalski | 1 | | Ames | 12 | | Morris | 2 | | Eva | 16 | | Vale | 3 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Kowalski" | | 3 | "Ames" | | 4 | "Morris" | | 5 | "Eva" | | 6 | "Vale" | | 7 | "Market" |
| | places | | | globalScore | 0.529 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 110 | | 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 | 2112 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 275 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 153 | | mean | 13.8 | | std | 15.65 | | cv | 1.134 | | sampleLengths | | 0 | 17 | | 1 | 72 | | 2 | 12 | | 3 | 58 | | 4 | 5 | | 5 | 68 | | 6 | 22 | | 7 | 36 | | 8 | 4 | | 9 | 4 | | 10 | 8 | | 11 | 6 | | 12 | 6 | | 13 | 8 | | 14 | 42 | | 15 | 1 | | 16 | 3 | | 17 | 2 | | 18 | 56 | | 19 | 10 | | 20 | 54 | | 21 | 24 | | 22 | 1 | | 23 | 4 | | 24 | 6 | | 25 | 8 | | 26 | 34 | | 27 | 5 | | 28 | 7 | | 29 | 60 | | 30 | 23 | | 31 | 8 | | 32 | 4 | | 33 | 5 | | 34 | 5 | | 35 | 9 | | 36 | 52 | | 37 | 7 | | 38 | 46 | | 39 | 9 | | 40 | 1 | | 41 | 4 | | 42 | 4 | | 43 | 4 | | 44 | 10 | | 45 | 36 | | 46 | 29 | | 47 | 6 | | 48 | 8 | | 49 | 6 |
| |
| 89.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 9 | | totalSentences | 200 | | matches | | 0 | "been abandoned" | | 1 | "been taken" | | 2 | "been painted" | | 3 | "was hidden" | | 4 | "was clenched" | | 5 | "been heated" | | 6 | "was folded" | | 7 | "been written" | | 8 | "was etched" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 273 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 275 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1656 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 54 | | adverbRatio | 0.03260869565217391 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.007246376811594203 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 275 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 275 | | mean | 7.68 | | std | 6.03 | | cv | 0.785 | | sampleLengths | | 0 | 17 | | 1 | 26 | | 2 | 7 | | 3 | 28 | | 4 | 11 | | 5 | 12 | | 6 | 18 | | 7 | 18 | | 8 | 9 | | 9 | 13 | | 10 | 5 | | 11 | 10 | | 12 | 23 | | 13 | 9 | | 14 | 4 | | 15 | 4 | | 16 | 18 | | 17 | 22 | | 18 | 15 | | 19 | 8 | | 20 | 13 | | 21 | 4 | | 22 | 4 | | 23 | 8 | | 24 | 6 | | 25 | 6 | | 26 | 4 | | 27 | 4 | | 28 | 16 | | 29 | 21 | | 30 | 5 | | 31 | 1 | | 32 | 3 | | 33 | 2 | | 34 | 5 | | 35 | 8 | | 36 | 12 | | 37 | 6 | | 38 | 5 | | 39 | 20 | | 40 | 10 | | 41 | 7 | | 42 | 24 | | 43 | 15 | | 44 | 4 | | 45 | 4 | | 46 | 5 | | 47 | 19 | | 48 | 1 | | 49 | 4 |
| |
| 47.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.3236363636363636 | | totalSentences | 275 | | uniqueOpeners | 89 | |
| 78.90% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 169 | | matches | | 0 | "Somewhere behind the stalls, something" | | 1 | "Then she noticed the clock." | | 2 | "More importantly, they wanted Quinn" | | 3 | "Then the rift opened." |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 169 | | matches | | 0 | "It had been taken from" | | 1 | "It pressed against her shoulders" | | 2 | "Her round glasses had slipped" | | 3 | "Its face was hidden behind" | | 4 | "His right hand was clenched" | | 5 | "Her military posture remained even" | | 6 | "He had spent the descent" | | 7 | "She drew a penlight from" | | 8 | "They approached the body, stopped," | | 9 | "He looked wounded." | | 10 | "She shifted her attention to" | | 11 | "She used tweezers from her" | | 12 | "Her thumb tightened around the" | | 13 | "She took out her notebook" | | 14 | "It hung on the tiled" | | 15 | "He glanced down." | | 16 | "She had noticed it on" | | 17 | "She walked to the nearest" | | 18 | "She took a step back" | | 19 | "It described a body delivered" |
| | ratio | 0.166 | |
| 45.80% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 140 | | totalSentences | 169 | | matches | | 0 | "The station had been abandoned" | | 1 | "Detective Harlow Quinn descended the" | | 2 | "The token was warm from" | | 3 | "It had been taken from" | | 4 | "Tonight, it had opened a" | | 5 | "That was not a fact" | | 6 | "The old station signs had" | | 7 | "Someone had strung lanterns between" | | 8 | "The market occupied the platform." | | 9 | "Canvas awnings crowded the space" | | 10 | "Tables displayed bottled lightning, silver" | | 11 | "Vendors watched her from beneath" | | 12 | "None of them spoke." | | 13 | "The silence had weight." | | 14 | "It pressed against her shoulders" | | 15 | "Eva Kowalski stood over him," | | 16 | "Her round glasses had slipped" | | 17 | "A worn leather satchel hung" | | 18 | "Quinn looked past her." | | 19 | "Eva nodded toward a tall" |
| | ratio | 0.828 | |
| 29.59% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 169 | | matches | | 0 | "Whoever had made them had" |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 3 | | matches | | 0 | "At the center of the market, beneath a clock that ran backward, a man lay dead beside a stall selling bottled memories." | | 1 | "Thin, gray-faced, dressed in a brown suit that belonged to another decade." | | 2 | "She had noticed it on entering: the blue flames lit the market from the left, yet the shadows stretched right and slightly upward, as though the ground sloped t…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 92 | | tagDensity | 0.163 | | leniency | 0.326 | | rawRatio | 0 | | effectiveRatio | 0 | |