| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1157 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 48.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1157 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "electric" | | 1 | "lilt" | | 2 | "gloom" | | 3 | "pulsed" | | 4 | "flicked" | | 5 | "rhythmic" | | 6 | "pulse" | | 7 | "quickened" | | 8 | "vibrated" | | 9 | "etched" |
| |
| 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 | 1 | | narrationSentences | 169 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 169 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 189 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 3 | | totalWords | 1157 | | ratio | 0.003 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 760 | | uniqueNames | 17 | | maxNameDensity | 3.03 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Quinn | 23 | | Spanish | 1 | | Christopher | 2 | | Review | 1 | | Tomás | 16 | | Flat | 2 | | Saint | 2 | | Eyes | 3 | | Morris | 2 | | Glanced | 1 | | Veil | 1 | | Market | 1 | | Boots | 4 | | Smell | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Christopher" | | 2 | "Review" | | 3 | "Tomás" | | 4 | "Saint" | | 5 | "Eyes" | | 6 | "Morris" | | 7 | "Boots" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Veil" | | 4 | "Market" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 39 | | 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 | 1157 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 189 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 62 | | mean | 18.66 | | std | 12.93 | | cv | 0.693 | | sampleLengths | | 0 | 59 | | 1 | 34 | | 2 | 2 | | 3 | 27 | | 4 | 21 | | 5 | 23 | | 6 | 9 | | 7 | 31 | | 8 | 21 | | 9 | 29 | | 10 | 8 | | 11 | 7 | | 12 | 18 | | 13 | 34 | | 14 | 8 | | 15 | 28 | | 16 | 9 | | 17 | 2 | | 18 | 30 | | 19 | 33 | | 20 | 18 | | 21 | 10 | | 22 | 19 | | 23 | 7 | | 24 | 17 | | 25 | 23 | | 26 | 16 | | 27 | 8 | | 28 | 41 | | 29 | 23 | | 30 | 37 | | 31 | 7 | | 32 | 22 | | 33 | 34 | | 34 | 3 | | 35 | 46 | | 36 | 24 | | 37 | 7 | | 38 | 11 | | 39 | 54 | | 40 | 16 | | 41 | 25 | | 42 | 3 | | 43 | 22 | | 44 | 5 | | 45 | 3 | | 46 | 14 | | 47 | 23 | | 48 | 10 | | 49 | 24 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 169 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 161 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 189 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 762 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.011811023622047244 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003937007874015748 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 189 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 189 | | mean | 6.12 | | std | 5.97 | | cv | 0.976 | | sampleLengths | | 0 | 8 | | 1 | 19 | | 2 | 11 | | 3 | 8 | | 4 | 8 | | 5 | 5 | | 6 | 12 | | 7 | 8 | | 8 | 4 | | 9 | 8 | | 10 | 2 | | 11 | 2 | | 12 | 4 | | 13 | 11 | | 14 | 12 | | 15 | 2 | | 16 | 3 | | 17 | 4 | | 18 | 12 | | 19 | 4 | | 20 | 2 | | 21 | 6 | | 22 | 3 | | 23 | 2 | | 24 | 2 | | 25 | 1 | | 26 | 1 | | 27 | 2 | | 28 | 7 | | 29 | 2 | | 30 | 3 | | 31 | 5 | | 32 | 2 | | 33 | 2 | | 34 | 6 | | 35 | 11 | | 36 | 2 | | 37 | 14 | | 38 | 7 | | 39 | 2 | | 40 | 5 | | 41 | 22 | | 42 | 3 | | 43 | 5 | | 44 | 7 | | 45 | 3 | | 46 | 2 | | 47 | 6 | | 48 | 2 | | 49 | 5 |
| |
| 82.01% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5079365079365079 | | totalSentences | 189 | | uniqueOpeners | 96 | |
| 84.75% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 118 | | matches | | 0 | "Closely cropped salt-and-pepper hair plastered" | | 1 | "Bright white light." | | 2 | "Too many in one socket." |
| | ratio | 0.025 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 118 | | matches | | 0 | "He vaulted a barrier." | | 1 | "He deflected Quinn's grab, spun," | | 2 | "She swept a leg." | | 3 | "He hopped the arc, landed" | | 4 | "She caught herself on a" | | 5 | "He stepped back." | | 6 | "It bounced on the metal." | | 7 | "He tapped his temple" | | 8 | "He backed up the stairs" | | 9 | "He leaned against the doorframe." | | 10 | "He unbuttoned his collar." | | 11 | "He wasn't bluffing." | | 12 | "He was afraid." | | 13 | "He was also desperate." | | 14 | "He looked at her" | | 15 | "She'd spend years digging holes" | | 16 | "She reached up." | | 17 | "She stepped to the door." | | 18 | "He leaned in." | | 19 | "She pressed the bone to" |
| | ratio | 0.195 | |
| 57.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 118 | | matches | | 0 | "Rain needled the pavement of" | | 1 | "Neon signs bled across the" | | 2 | "Harlow Quinn cut through the" | | 3 | "Military bearing carried her over" | | 4 | "Eyes tracked the movement ahead." | | 5 | "A figure in a soaked" | | 6 | "He vaulted a barrier." | | 7 | "Boots hit the metal with" | | 8 | "Target raised a forearm." | | 9 | "A jagged white scar marring" | | 10 | "He deflected Quinn's grab, spun," | | 11 | "Neck muscles corded." | | 12 | "She swept a leg." | | 13 | "He hopped the arc, landed" | | 14 | "Momentum carried Quinn back." | | 15 | "She caught herself on a" | | 16 | "Boots scraped brick." | | 17 | "a voice called out" | | 18 | "The figure stood." | | 19 | "Face emerged from the shadows." |
| | ratio | 0.805 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 118 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 6 | | fancyTags | | 0 | "Tomás murmured (murmur)" | | 1 | "Tomás whispered (whisper)" | | 2 | "she demanded (demand)" | | 3 | "Quinn pressed (press)" | | 4 | "he whispered (whisper)" | | 5 | "he hissed (hiss)" |
| | dialogueSentences | 40 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.6 | | effectiveRatio | 0.3 | |