| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.393 | | leniency | 0.786 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 78.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1189 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slightly" | | 1 | "softly" | | 2 | "really" |
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| 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) | |
| 28.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1189 | | totalAiIsms | 17 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | word | "down her spine" | | count | 1 |
| | 6 | | | 7 | | | 8 | | | 9 | | | 10 | |
| | highlights | | 0 | "silence" | | 1 | "profound" | | 2 | "scanning" | | 3 | "etched" | | 4 | "chill" | | 5 | "down her spine" | | 6 | "echoing" | | 7 | "eyebrow" | | 8 | "racing" | | 9 | "could feel" | | 10 | "whisper" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 98 | | matches | | 0 | "e with surprise" | | 1 | "felt a prickle" |
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| 99.13% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 98 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "happened to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1191 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1010 | | uniqueNames | 13 | | maxNameDensity | 1.78 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Eva" | | discoveredNames | | Harlow | 18 | | Quinn | 3 | | Tube | 1 | | Camden | 1 | | Veil | 4 | | Market | 3 | | Evans | 4 | | Compass | 1 | | Morris | 2 | | Eva | 12 | | Kowalski | 2 | | Aurora | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Evans" | | 3 | "Morris" | | 4 | "Eva" | | 5 | "Kowalski" | | 6 | "Aurora" |
| | places | | | globalScore | 0.609 | | windowScore | 0.333 | |
| 83.33% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 2 | | matches | | 0 | "It was as if the entire market had fled, leaving only the body behind" | | 1 | "looked like spellwork" |
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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 | 1191 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 115 | | matches | | 0 | "seen that symbol" | | 1 | "killed that man" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 35.03 | | std | 19.87 | | cv | 0.567 | | sampleLengths | | 0 | 73 | | 1 | 51 | | 2 | 35 | | 3 | 38 | | 4 | 55 | | 5 | 15 | | 6 | 49 | | 7 | 9 | | 8 | 42 | | 9 | 19 | | 10 | 56 | | 11 | 15 | | 12 | 51 | | 13 | 37 | | 14 | 76 | | 15 | 18 | | 16 | 21 | | 17 | 18 | | 18 | 13 | | 19 | 12 | | 20 | 50 | | 21 | 9 | | 22 | 13 | | 23 | 43 | | 24 | 21 | | 25 | 53 | | 26 | 8 | | 27 | 18 | | 28 | 36 | | 29 | 31 | | 30 | 33 | | 31 | 41 | | 32 | 56 | | 33 | 76 |
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| 87.36% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 98 | | matches | | 0 | "was positioned" | | 1 | "was hunched" | | 2 | "was filled" | | 3 | "been carved" | | 4 | "been seen" | | 5 | "been awakened" |
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| 88.27% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 179 | | matches | | 0 | "was hiding" | | 1 | "was going" | | 2 | "was hiding" |
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| 93.17% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 115 | | ratio | 0.017 | | matches | | 0 | "But it was the wound that caught Harlow's attention - a deep gash across his throat, clean and precise." | | 1 | "But the needle pointed in the same direction as hers - towards the old platform." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1010 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, leather-bound book" |
| | adverbCount | 29 | | adverbRatio | 0.028712871287128714 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.01089108910891089 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 10.36 | | std | 5.26 | | cv | 0.508 | | sampleLengths | | 0 | 26 | | 1 | 22 | | 2 | 25 | | 3 | 17 | | 4 | 15 | | 5 | 19 | | 6 | 15 | | 7 | 13 | | 8 | 5 | | 9 | 2 | | 10 | 4 | | 11 | 11 | | 12 | 7 | | 13 | 16 | | 14 | 16 | | 15 | 18 | | 16 | 19 | | 17 | 2 | | 18 | 7 | | 19 | 8 | | 20 | 7 | | 21 | 20 | | 22 | 11 | | 23 | 11 | | 24 | 5 | | 25 | 4 | | 26 | 11 | | 27 | 14 | | 28 | 11 | | 29 | 6 | | 30 | 10 | | 31 | 9 | | 32 | 6 | | 33 | 20 | | 34 | 11 | | 35 | 13 | | 36 | 5 | | 37 | 1 | | 38 | 9 | | 39 | 6 | | 40 | 11 | | 41 | 9 | | 42 | 14 | | 43 | 6 | | 44 | 3 | | 45 | 5 | | 46 | 3 | | 47 | 18 | | 48 | 9 | | 49 | 10 |
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| 46.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3217391304347826 | | totalSentences | 115 | | uniqueOpeners | 37 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 95 | | matches | (empty) | | ratio | 0 | |
| 30.53% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 95 | | matches | | 0 | "She glanced at the bone" | | 1 | "It moved locations every full" | | 2 | "She spotted DS Evans, her" | | 3 | "He looked up as she" | | 4 | "he acknowledged, standing straight" | | 5 | "She was never late, not" | | 6 | "She just preferred to make" | | 7 | "She stepped closer to the" | | 8 | "His eyes were open, staring" | | 9 | "She leaned in closer, her" | | 10 | "She pointed at the wound" | | 11 | "She pulled out her Veil" | | 12 | "His body was never found," | | 13 | "She just couldn't prove it." | | 14 | "she admitted, tucking the compass" | | 15 | "She moved away from the" | | 16 | "It was as if the" | | 17 | "Her gaze fell on a" | | 18 | "She could see a faint" | | 19 | "She walked towards it, her" |
| | ratio | 0.474 | |
| 49.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 95 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "The air was thick with" | | 2 | "She glanced at the bone" | | 3 | "The Veil Market was a" | | 4 | "It moved locations every full" | | 5 | "Harlow's watch, a worn leather" | | 6 | "She spotted DS Evans, her" | | 7 | "He looked up as she" | | 8 | "he acknowledged, standing straight" | | 9 | "Harlow ignored the jab." | | 10 | "She was never late, not" | | 11 | "She just preferred to make" | | 12 | "She stepped closer to the" | | 13 | "The victim was a man," | | 14 | "His eyes were open, staring" | | 15 | "Evans said, crouching beside her" | | 16 | "Harlow nodded, but something didn't" | | 17 | "She leaned in closer, her" | | 18 | "She pointed at the wound" | | 19 | "Evans frowned, following her gaze." |
| | ratio | 0.821 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 95 | | matches | | 0 | "Because Harlow Quinn never left" | | 1 | "No matter what she had" | | 2 | "Because that was who she" |
| | ratio | 0.032 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn stepped off the creaking wooden planks of the makeshift bridge, her boots sinking slightly into the damp earth of the abandoned Tube stat…" | | 1 | "But it was the wound that caught Harlow's attention - a deep gash across his throat, clean and precise." | | 2 | "Three years ago, her partner, DS Morris, had vanished during a case that had led them to the Veil Market." |
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| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice steady despite the fear in her eyes" |
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| 42.86% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 3 | | fancyTags | | 0 | "he acknowledged (acknowledge)" | | 1 | "she admitted (admit)" | | 2 | "Eva hesitated (hesitate)" |
| | dialogueSentences | 28 | | tagDensity | 0.357 | | leniency | 0.714 | | rawRatio | 0.3 | | effectiveRatio | 0.214 | |