| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.175 | | leniency | 0.35 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.48% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1050 | | totalAiIsmAdverbs | 2 | | 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) | |
| 47.62% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1050 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "echoed" | | 1 | "oppressive" | | 2 | "silence" | | 3 | "stark" | | 4 | "gloom" | | 5 | "scanned" | | 6 | "pulse" | | 7 | "etched" | | 8 | "racing" | | 9 | "raced" | | 10 | "determined" |
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| 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 | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" | | 2 | "The air was thick with" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 67 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 67 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | 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 | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 655 | | uniqueNames | 11 | | maxNameDensity | 3.51 | | worstName | "Harlow" | | maxWindowNameDensity | 6.5 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 23 | | Quinn | 1 | | Tube | 1 | | Camden | 1 | | Morris | 1 | | Veil | 1 | | Market | 1 | | Eva | 12 | | Kowalski | 1 | | Shadow | 1 | | Key | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Market" | | 4 | "Eva" | | 5 | "Kowalski" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 1050 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 99 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 25 | | std | 14.56 | | cv | 0.582 | | sampleLengths | | 0 | 79 | | 1 | 39 | | 2 | 44 | | 3 | 30 | | 4 | 42 | | 5 | 54 | | 6 | 23 | | 7 | 17 | | 8 | 46 | | 9 | 15 | | 10 | 36 | | 11 | 26 | | 12 | 31 | | 13 | 5 | | 14 | 32 | | 15 | 12 | | 16 | 18 | | 17 | 19 | | 18 | 11 | | 19 | 34 | | 20 | 7 | | 21 | 20 | | 22 | 29 | | 23 | 11 | | 24 | 25 | | 25 | 37 | | 26 | 17 | | 27 | 14 | | 28 | 27 | | 29 | 5 | | 30 | 28 | | 31 | 6 | | 32 | 17 | | 33 | 21 | | 34 | 24 | | 35 | 10 | | 36 | 22 | | 37 | 19 | | 38 | 20 | | 39 | 18 | | 40 | 44 | | 41 | 16 |
| |
| 94.79% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 67 | | matches | | 0 | "were filled" | | 1 | "was etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 118 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 99 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 656 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.019817073170731708 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.010670731707317074 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 10.61 | | std | 6.48 | | cv | 0.611 | | sampleLengths | | 0 | 25 | | 1 | 17 | | 2 | 20 | | 3 | 17 | | 4 | 17 | | 5 | 16 | | 6 | 6 | | 7 | 9 | | 8 | 13 | | 9 | 22 | | 10 | 6 | | 11 | 20 | | 12 | 4 | | 13 | 12 | | 14 | 30 | | 15 | 7 | | 16 | 14 | | 17 | 14 | | 18 | 19 | | 19 | 9 | | 20 | 14 | | 21 | 11 | | 22 | 6 | | 23 | 14 | | 24 | 15 | | 25 | 4 | | 26 | 5 | | 27 | 8 | | 28 | 2 | | 29 | 13 | | 30 | 5 | | 31 | 27 | | 32 | 4 | | 33 | 11 | | 34 | 3 | | 35 | 12 | | 36 | 10 | | 37 | 21 | | 38 | 3 | | 39 | 2 | | 40 | 14 | | 41 | 18 | | 42 | 10 | | 43 | 2 | | 44 | 13 | | 45 | 5 | | 46 | 13 | | 47 | 6 | | 48 | 3 | | 49 | 8 |
| |
| 51.18% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3434343434343434 | | totalSentences | 99 | | uniqueOpeners | 34 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 63 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 63 | | matches | | 0 | "Her boots echoed against the" | | 1 | "She adjusted the worn leather" | | 2 | "She adjusted her round glasses" | | 3 | "She respected Eva's expertise in" | | 4 | "She approached the body, a" | | 5 | "Her fingers brushed the man's" | | 6 | "She recognized it immediately." | | 7 | "She turned to the crowd" | | 8 | "She caught up to the" | | 9 | "she demanded, pinning him down" | | 10 | "She glanced at the body" | | 11 | "She would find the Shadow" |
| | ratio | 0.19 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 63 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "Her boots echoed against the" | | 2 | "She adjusted the worn leather" | | 3 | "The memory of her partner's" | | 4 | "The Veil Market, a hidden" | | 5 | "The market moved locations every" | | 6 | "Tonight, it was a crime" | | 7 | "Eva Kowalski emerged, her curly" | | 8 | "She adjusted her round glasses" | | 9 | "Harlow acknowledged, her tone clipped" | | 10 | "She respected Eva's expertise in" | | 11 | "Eva gestured to a cluster" | | 12 | "Harlow's sharp brown eyes scanned" | | 13 | "The stalls were filled with" | | 14 | "The air was thick with" | | 15 | "She approached the body, a" | | 16 | "Harlow asked, crouching beside the" | | 17 | "Her fingers brushed the man's" | | 18 | "Eva replied, her voice uncertain" | | 19 | "Harlow's gaze shifted to the" |
| | ratio | 0.968 | |
| 79.37% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 63 | | matches | | | ratio | 0.016 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 1 | | matches | | 0 | "The market moved locations every full moon, a fact that made it notoriously difficult to track." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 2 | | matches | | 0 | "Harlow acknowledged, her tone clipped" | | 1 | "Eva replied, her voice uncertain" |
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| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 4 | | fancyTags | | 0 | "Harlow acknowledged (acknowledge)" | | 1 | "she murmured (murmur)" | | 2 | "Harlow ordered (order)" | | 3 | "she demanded (demand)" |
| | dialogueSentences | 40 | | tagDensity | 0.15 | | leniency | 0.3 | | rawRatio | 0.667 | | effectiveRatio | 0.2 | |