| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "Evie nodded solemnly [solemnly]" |
| | dialogueSentences | 29 | | tagDensity | 0.276 | | leniency | 0.552 | | rawRatio | 0.125 | | effectiveRatio | 0.069 | |
| 76.85% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 864 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slowly" | | 1 | "completely" | | 2 | "very" |
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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) | |
| 18.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 864 | | totalAiIsms | 14 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | word | "down her spine" | | count | 1 |
| | 8 | | | 9 | | | 10 | | | 11 | |
| | highlights | | 0 | "scanning" | | 1 | "gloom" | | 2 | "familiar" | | 3 | "beacon" | | 4 | "furrowed" | | 5 | "whisper" | | 6 | "chill" | | 7 | "down her spine" | | 8 | "gleaming" | | 9 | "trepidation" | | 10 | "raced" | | 11 | "glinting" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 37 | | matches | | |
| 27.03% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 37 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 58 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 860 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 460 | | uniqueNames | 11 | | maxNameDensity | 3.48 | | worstName | "Harlow" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Harlow" | | discoveredNames | | Detective | 1 | | Harlow | 16 | | Quinn | 1 | | Tube | 1 | | Camden | 1 | | London | 1 | | Eva | 1 | | Kowalski | 1 | | Evie | 11 | | Veil | 2 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Kowalski" | | 4 | "Evie" | | 5 | "Market" |
| | places | | | globalScore | 0 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 31 | | 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 | 860 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 58 | | matches | (empty) | |
| 75.43% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 31.85 | | std | 13.19 | | cv | 0.414 | | sampleLengths | | 0 | 53 | | 1 | 56 | | 2 | 34 | | 3 | 29 | | 4 | 18 | | 5 | 45 | | 6 | 31 | | 7 | 40 | | 8 | 16 | | 9 | 52 | | 10 | 23 | | 11 | 17 | | 12 | 6 | | 13 | 28 | | 14 | 51 | | 15 | 29 | | 16 | 23 | | 17 | 45 | | 18 | 17 | | 19 | 19 | | 20 | 45 | | 21 | 29 | | 22 | 41 | | 23 | 32 | | 24 | 36 | | 25 | 30 | | 26 | 15 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 37 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 75 | | matches | | |
| 44.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 58 | | ratio | 0.034 | | matches | | 0 | "\"Well, the body itself is completely charred, as if it was exposed to an intense heat source. But there are no signs of an explosion or any kind of accelerant. And the scorch marks—\" Evie gestured to the floor \"—they radiate out from a single point, like some sort of...focused energy discharge.\"" | | 1 | "But Evie was right—the evidence did seem to point to something beyond the natural order." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 450 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.02 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.008888888888888889 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 58 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 58 | | mean | 14.83 | | std | 8.66 | | cv | 0.584 | | sampleLengths | | 0 | 20 | | 1 | 16 | | 2 | 17 | | 3 | 20 | | 4 | 17 | | 5 | 19 | | 6 | 21 | | 7 | 9 | | 8 | 4 | | 9 | 16 | | 10 | 13 | | 11 | 12 | | 12 | 6 | | 13 | 30 | | 14 | 15 | | 15 | 18 | | 16 | 13 | | 17 | 17 | | 18 | 23 | | 19 | 3 | | 20 | 13 | | 21 | 52 | | 22 | 9 | | 23 | 14 | | 24 | 6 | | 25 | 11 | | 26 | 4 | | 27 | 2 | | 28 | 15 | | 29 | 13 | | 30 | 8 | | 31 | 24 | | 32 | 19 | | 33 | 15 | | 34 | 14 | | 35 | 12 | | 36 | 11 | | 37 | 13 | | 38 | 32 | | 39 | 3 | | 40 | 14 | | 41 | 6 | | 42 | 13 | | 43 | 3 | | 44 | 20 | | 45 | 22 | | 46 | 7 | | 47 | 22 | | 48 | 9 | | 49 | 32 |
| |
| 75.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.46551724137931033 | | totalSentences | 58 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 37 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 37 | | matches | | 0 | "She grabbed her umbrella and" | | 1 | "she called out, striding over" | | 2 | "It was too closely tied" | | 3 | "She gestured to the scorch" | | 4 | "She'd made several attempts to" | | 5 | "She rose to her feet," |
| | ratio | 0.162 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 35 | | totalSentences | 37 | | matches | | 0 | "The rain was coming down" | | 1 | "She grabbed her umbrella and" | | 2 | "This was an unusual place" | | 3 | "The musty scent of decay" | | 4 | "A crowd of forensics techs" | | 5 | "Harlow spotted a familiar figure" | | 6 | "she called out, striding over" | | 7 | "Eva Kowalski looked up, her" | | 8 | "Harlow knelt down beside her," | | 9 | "Evie gestured to a form" | | 10 | "Harlow frowned, her sharp gaze" | | 11 | "Evie worried her lower lip," | | 12 | "Harlow nodded slowly." | | 13 | "Evie gestured to the floor" | | 14 | "Harlow's brow furrowed as she" | | 15 | "Evie chewed on her lip," | | 16 | "Harlow arched a brow." | | 17 | "Evie's voice lowered to a" | | 18 | "Harlow felt a chill run" | | 19 | "The Veil, a mysterious plane" |
| | ratio | 0.946 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 37 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 1 | | matches | | 0 | "A crowd of forensics techs in protective suits were already combing the scene, their flashlights cutting through the gloom." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "she called out (call out)" |
| | dialogueSentences | 29 | | tagDensity | 0.034 | | leniency | 0.069 | | rawRatio | 1 | | effectiveRatio | 0.069 | |