| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.13% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 721 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 721 | | totalAiIsms | 19 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "rhythmic" | | 1 | "echoed" | | 2 | "fleeting" | | 3 | "determined" | | 4 | "glinting" | | 5 | "treacherous" | | 6 | "depths" | | 7 | "clandestine" | | 8 | "macabre" | | 9 | "dancing" | | 10 | "pulse" | | 11 | "reminder" | | 12 | "measured" | | 13 | "weight" | | 14 | "pounding" | | 15 | "whisper" | | 16 | "echoes" | | 17 | "resolve" |
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| 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 | 39 | | matches | (empty) | |
| 0.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 39 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "try to" | | 2 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 40 | | 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 | 1 | | markdownWords | 9 | | totalWords | 724 | | ratio | 0.012 | | matches | | 0 | "I will uncover the truth, no matter the cost." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 63.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 695 | | uniqueNames | 19 | | maxNameDensity | 1.73 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 2 | | Quinn | 12 | | Old | 1 | | Compton | 1 | | Street | 1 | | Tomás | 1 | | Herrera | 10 | | Morris | 2 | | North | 1 | | London | 1 | | Tube | 2 | | Camden | 1 | | Veil | 2 | | Market | 2 | | Saint | 1 | | Christopher | 1 | | Zora | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Zora" |
| | places | | 0 | "Soho" | | 1 | "Old" | | 2 | "Compton" | | 3 | "Street" | | 4 | "North" | | 5 | "London" | | 6 | "Tube" |
| | globalScore | 0.637 | | windowScore | 0.833 | |
| 84.21% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 1 | | matches | | 0 | "eyes that seemed to hold a thousand secrets, watched Quinn's approach with an unblinking gaze" |
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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 | 724 | | matches | (empty) | |
| 83.33% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 40 | | matches | | |
| 0.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 13 | | mean | 55.69 | | std | 7.73 | | cv | 0.139 | | sampleLengths | | 0 | 49 | | 1 | 64 | | 2 | 59 | | 3 | 65 | | 4 | 46 | | 5 | 48 | | 6 | 58 | | 7 | 57 | | 8 | 61 | | 9 | 54 | | 10 | 46 | | 11 | 47 | | 12 | 70 |
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| 96.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 39 | | matches | | |
| 56.63% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 93 | | matches | | 0 | "was hiding" | | 1 | "were heading" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 40 | | ratio | 0.05 | | matches | | 0 | "She had heard whispers, but nothing concrete – until now." | | 1 | "To gain entry to the market, Quinn knew she needed a bone token – a macabre currency she didn't possess." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 697 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.02582496413199426 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.010043041606886656 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 40 | | echoCount | 0 | | echoWords | (empty) | |
| 69.41% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 40 | | mean | 18.1 | | std | 5.86 | | cv | 0.324 | | sampleLengths | | 0 | 19 | | 1 | 30 | | 2 | 23 | | 3 | 25 | | 4 | 16 | | 5 | 27 | | 6 | 13 | | 7 | 19 | | 8 | 18 | | 9 | 14 | | 10 | 15 | | 11 | 18 | | 12 | 15 | | 13 | 21 | | 14 | 10 | | 15 | 18 | | 16 | 30 | | 17 | 18 | | 18 | 22 | | 19 | 18 | | 20 | 22 | | 21 | 1 | | 22 | 13 | | 23 | 21 | | 24 | 20 | | 25 | 7 | | 26 | 18 | | 27 | 16 | | 28 | 16 | | 29 | 17 | | 30 | 21 | | 31 | 11 | | 32 | 11 | | 33 | 24 | | 34 | 17 | | 35 | 14 | | 36 | 16 | | 37 | 29 | | 38 | 19 | | 39 | 22 |
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| 83.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.5 | | totalSentences | 40 | | uniqueOpeners | 20 | |
| 87.72% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 38 | | matches | | 0 | "Then, a curly dark brown" |
| | ratio | 0.026 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 4 | | totalSentences | 38 | | matches | | 0 | "She had heard whispers, but" | | 1 | "Her presence, however, had already" | | 2 | "He raised his left arm," | | 3 | "She could retreat, return with" |
| | ratio | 0.105 | |
| 78.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 29 | | totalSentences | 38 | | matches | | 0 | "Rain lashed down on the" | | 1 | "Detective Harlow Quinn sprinted down" | | 2 | "Quinn's sharp jaw set in" | | 3 | "The memory of her fallen" | | 4 | "Quinn followed, her military-honed bearing" | | 5 | "The alley opened into a" | | 6 | "Herrera wove through the throngs" | | 7 | "The streetlights grew farther apart," | | 8 | "A faded sign creaked in" | | 9 | "This was the rumored site" | | 10 | "She had heard whispers, but" | | 11 | "Herrera vanished into the Tube" | | 12 | "Quinn slowed, her hand instinctively" | | 13 | "The air inside the abandoned" | | 14 | "A narrow corridor stretched before" | | 15 | "Quinn's eyes adjusted slowly to" | | 16 | "A sign above the stall" | | 17 | "The vendor, an ageless woman" | | 18 | "Her presence, however, had already" | | 19 | "Patrons of the Veil Market" |
| | ratio | 0.763 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 38 | | matches | | 0 | "To gain entry to the" | | 1 | "Or, she could follow Herrera," |
| | ratio | 0.053 | |
| 79.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 3 | | matches | | 0 | "Rain lashed down on the deserted streets of Soho, casting a rhythmic melody that echoed off the towering buildings." | | 1 | "The streetlights grew farther apart, casting long shadows that stretched and twisted in the rain." | | 2 | "Herrera vanished into the Tube station's entrance, a nondescript opening that blended seamlessly into the surrounding urban decay." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |