| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 39 | | tagDensity | 0.205 | | leniency | 0.41 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.14% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1297 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 3.62% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1297 | | totalAiIsms | 25 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | word | "down her spine" | | count | 1 |
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| | highlights | | 0 | "echoed" | | 1 | "oppressive" | | 2 | "silence" | | 3 | "scanned" | | 4 | "stark" | | 5 | "gloom" | | 6 | "navigating" | | 7 | "tinged" | | 8 | "flicked" | | 9 | "etched" | | 10 | "raced" | | 11 | "scanning" | | 12 | "determined" | | 13 | "quivered" | | 14 | "echoing" | | 15 | "magnetic" | | 16 | "whisper" | | 17 | "otherworldly" | | 18 | "pawn" | | 19 | "resolve" | | 20 | "chill" | | 21 | "down her spine" |
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| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 5 | | maxInWindow | 4 | | found | | 0 | | label | "heart pounded in chest" | | count | 1 |
| | 1 | | label | "eyes widened/narrowed" | | count | 3 |
| | 2 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "heart pounded in her chest" | | 1 | "eyes narrowed" | | 2 | "The air was thick with" |
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| 90.28% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 3 | | narrationSentences | 72 | | matches | | 0 | "e with fear" | | 1 | "t in determination" | | 2 | "felt a chill" |
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| 83.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 72 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1296 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 58 | | wordCount | 821 | | uniqueNames | 12 | | maxNameDensity | 3.29 | | worstName | "Harlow" | | maxWindowNameDensity | 7 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 27 | | Quinn | 1 | | Tube | 1 | | Camden | 1 | | Veil | 3 | | Market | 3 | | Detective | 2 | | Eva | 10 | | Kowalski | 1 | | Hale | 1 | | Lila | 6 | | Marcus | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Detective" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Hale" | | 6 | "Lila" | | 7 | "Marcus" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 1 | | matches | | 0 | "whisper that seemed to come from all directions at once" |
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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 | 1296 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 102 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 28.17 | | std | 14.27 | | cv | 0.506 | | sampleLengths | | 0 | 68 | | 1 | 61 | | 2 | 45 | | 3 | 10 | | 4 | 37 | | 5 | 51 | | 6 | 21 | | 7 | 14 | | 8 | 26 | | 9 | 17 | | 10 | 27 | | 11 | 12 | | 12 | 22 | | 13 | 10 | | 14 | 23 | | 15 | 41 | | 16 | 17 | | 17 | 17 | | 18 | 24 | | 19 | 46 | | 20 | 14 | | 21 | 25 | | 22 | 15 | | 23 | 30 | | 24 | 26 | | 25 | 16 | | 26 | 30 | | 27 | 9 | | 28 | 35 | | 29 | 23 | | 30 | 29 | | 31 | 38 | | 32 | 31 | | 33 | 49 | | 34 | 34 | | 35 | 39 | | 36 | 11 | | 37 | 33 | | 38 | 8 | | 39 | 37 | | 40 | 11 | | 41 | 47 | | 42 | 14 | | 43 | 22 | | 44 | 44 | | 45 | 37 |
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| 95.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 72 | | matches | | 0 | "been called" | | 1 | "was determined" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 148 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 102 | | ratio | 0.01 | | matches | | 0 | "The air was thick with the scent of damp earth and something else—something metallic and acrid that set her teeth on edge." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 824 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.014563106796116505 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007281553398058253 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 12.71 | | std | 7.87 | | cv | 0.62 | | sampleLengths | | 0 | 25 | | 1 | 17 | | 2 | 26 | | 3 | 23 | | 4 | 16 | | 5 | 22 | | 6 | 9 | | 7 | 14 | | 8 | 22 | | 9 | 6 | | 10 | 4 | | 11 | 18 | | 12 | 19 | | 13 | 13 | | 14 | 13 | | 15 | 25 | | 16 | 7 | | 17 | 14 | | 18 | 11 | | 19 | 3 | | 20 | 11 | | 21 | 15 | | 22 | 12 | | 23 | 5 | | 24 | 4 | | 25 | 23 | | 26 | 9 | | 27 | 3 | | 28 | 22 | | 29 | 6 | | 30 | 4 | | 31 | 10 | | 32 | 13 | | 33 | 19 | | 34 | 22 | | 35 | 9 | | 36 | 8 | | 37 | 8 | | 38 | 9 | | 39 | 8 | | 40 | 16 | | 41 | 7 | | 42 | 39 | | 43 | 3 | | 44 | 11 | | 45 | 3 | | 46 | 22 | | 47 | 5 | | 48 | 10 | | 49 | 4 |
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| 53.27% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3431372549019608 | | totalSentences | 102 | | uniqueOpeners | 35 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 71 | | matches | | 0 | "Her boots echoed against the" | | 1 | "She adjusted the worn leather" | | 2 | "She adjusted her round glasses" | | 3 | "She needed more information." | | 4 | "She returned to the body," | | 5 | "Her heart pounded in her" | | 6 | "She could hear the faint" | | 7 | "She reached a small chamber," | | 8 | "she demanded, her voice echoing" | | 9 | "She looked at the compass," | | 10 | "She knew that the path" | | 11 | "She had to be." | | 12 | "She had a mystery to" |
| | ratio | 0.183 | |
| 16.34% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 71 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "Her boots echoed against the" | | 2 | "She adjusted the worn leather" | | 3 | "The Veil Market was a" | | 4 | "Tonight, it was the scene" | | 5 | "The air was thick with" | | 6 | "Detective Eva Kowalski emerged, her" | | 7 | "She adjusted her round glasses" | | 8 | "Harlow acknowledged, her tone clipped" | | 9 | "Eva gestured to a makeshift" | | 10 | "Harlow approached the stall, her" | | 11 | "Marcus Hale lay sprawled on" | | 12 | "A pool of dark, viscous" | | 13 | "Eva said, noticing Harlow's gaze" | | 14 | "Harlow crouched beside the body," | | 15 | "Eva replied, her voice tinged" | | 16 | "Harlow's eyes flicked to the" | | 17 | "Eva shook her head." | | 18 | "Harlow's mind raced, piecing together" | | 19 | "Harlow stood, her bearing military" |
| | ratio | 0.887 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 2 | | matches | | 0 | "The Veil Market was a place she had only heard whispers about, a hidden supernatural black market that moved locations every full moon." | | 1 | "Harlow reached for the compass, her fingers brushing against the verdigris-covered casing." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 5 | | matches | | 0 | "Harlow acknowledged, her tone clipped" | | 1 | "Harlow said, her voice firm but not unkind" | | 2 | "Harlow replied, her tone softening slightly" | | 3 | "Harlow said, her tone dismissive" | | 4 | "she demanded, her voice echoing in the chamber" |
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| 98.72% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "Harlow acknowledged (acknowledge)" | | 1 | "she demanded (demand)" |
| | dialogueSentences | 39 | | tagDensity | 0.205 | | leniency | 0.41 | | rawRatio | 0.25 | | effectiveRatio | 0.103 | |