| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.036 | | leniency | 0.071 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 84.06% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 941 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "softly" | | 1 | "slightly" | | 2 | "slowly" |
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| 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 | 941 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoing" | | 2 | "scanned" | | 3 | "clandestine" | | 4 | "etched" | | 5 | "wavering" | | 6 | "eyebrow" | | 7 | "determined" | | 8 | "familiar" | | 9 | "pang" | | 10 | "racing" | | 11 | "palpable" | | 12 | "resolve" |
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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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| 90.28% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 48 | | matches | | 0 | "d with confusion" | | 1 | "t with determination" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 48 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 74 | | 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 | 935 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 566 | | uniqueNames | 10 | | maxNameDensity | 2.83 | | worstName | "Quinn" | | maxWindowNameDensity | 5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 16 | | Tube | 1 | | Camden | 1 | | Veil | 1 | | Market | 4 | | Morris | 1 | | Jameson | 9 | | Eva | 1 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Morris" | | 4 | "Jameson" | | 5 | "Eva" | | 6 | "Kowalski" |
| | places | (empty) | | globalScore | 0.087 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | 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 | 935 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 32 | | mean | 29.22 | | std | 17.11 | | cv | 0.586 | | sampleLengths | | 0 | 70 | | 1 | 54 | | 2 | 15 | | 3 | 14 | | 4 | 66 | | 5 | 54 | | 6 | 53 | | 7 | 31 | | 8 | 13 | | 9 | 66 | | 10 | 35 | | 11 | 12 | | 12 | 32 | | 13 | 8 | | 14 | 30 | | 15 | 16 | | 16 | 36 | | 17 | 14 | | 18 | 32 | | 19 | 17 | | 20 | 32 | | 21 | 18 | | 22 | 29 | | 23 | 13 | | 24 | 29 | | 25 | 14 | | 26 | 26 | | 27 | 13 | | 28 | 25 | | 29 | 18 | | 30 | 21 | | 31 | 29 |
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| 97.95% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 48 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 94 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 74 | | ratio | 0.054 | | matches | | 0 | "The air was thick with the scent of damp earth and something else—something metallic and sharp." | | 1 | "The man's clothes were ordinary, but the items around him spoke of the supernatural—a world she had only begun to understand since the loss of her partner, DS Morris." | | 2 | "And the stones—she had seen their like before, in the archives Eva Kowalski had shown her." | | 3 | "\"There's more to this, Jameson. The Market moves every full moon. This man knew where it would be. He wasn't just a collector; he was searching for something.\"" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 570 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.01929824561403509 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.014035087719298246 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 74 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 74 | | mean | 12.64 | | std | 6.67 | | cv | 0.528 | | sampleLengths | | 0 | 24 | | 1 | 16 | | 2 | 10 | | 3 | 20 | | 4 | 18 | | 5 | 21 | | 6 | 15 | | 7 | 8 | | 8 | 7 | | 9 | 12 | | 10 | 2 | | 11 | 23 | | 12 | 17 | | 13 | 26 | | 14 | 12 | | 15 | 29 | | 16 | 13 | | 17 | 11 | | 18 | 14 | | 19 | 9 | | 20 | 19 | | 21 | 12 | | 22 | 19 | | 23 | 5 | | 24 | 8 | | 25 | 12 | | 26 | 10 | | 27 | 13 | | 28 | 15 | | 29 | 16 | | 30 | 7 | | 31 | 28 | | 32 | 5 | | 33 | 7 | | 34 | 9 | | 35 | 23 | | 36 | 5 | | 37 | 3 | | 38 | 5 | | 39 | 25 | | 40 | 9 | | 41 | 7 | | 42 | 10 | | 43 | 26 | | 44 | 10 | | 45 | 4 | | 46 | 15 | | 47 | 17 | | 48 | 8 | | 49 | 9 |
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| 50.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.3108108108108108 | | totalSentences | 74 | | uniqueOpeners | 23 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 48 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 48 | | matches | | 0 | "Her eyes, sharp and discerning," | | 1 | "She nodded to the officer" | | 2 | "he greeted, his voice wavering" | | 3 | "She felt a familiar pang" | | 4 | "It was DI Jameson, a" | | 5 | "He stepped forward, his gaze" | | 6 | "It pointed toward supernatural rifts," | | 7 | "She stood, her gaze sweeping" |
| | ratio | 0.167 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 48 | | matches | | 0 | "Detective Harlow Quinn stepped into" | | 1 | "The air was thick with" | | 2 | "Her eyes, sharp and discerning," | | 3 | "The Veil Market, usually bustling" | | 4 | "A cluster of uniformed officers" | | 5 | "Quinn approached them with the" | | 6 | "She nodded to the officer" | | 7 | "he greeted, his voice wavering" | | 8 | "Quinn raised an eyebrow, her" | | 9 | "The constable led her to" | | 10 | "A bone token, a small" | | 11 | "The victim, a middle-aged man" | | 12 | "Quinn crouched beside the body," | | 13 | "The man's clothes were ordinary," | | 14 | "She felt a familiar pang" | | 15 | "It was DI Jameson, a" | | 16 | "He stepped forward, his gaze" | | 17 | "Quinn shook her head, her" | | 18 | "Jameson shrugged, his expression dismissive." | | 19 | "Quinn ignored him, her mind" |
| | ratio | 0.938 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 48 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 1 | | matches | | 0 | "Quinn approached them with the military precision that had become her hallmark, her worn leather watch ticking softly against her wrist." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "he greeted, his voice wavering slightly" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |