| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 75.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1008 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "gently" | | 1 | "perfectly" | | 2 | "carefully" | | 3 | "completely" |
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| 60.00% | 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) | |
| 65.28% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1008 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "echoing" | | 1 | "intricate" | | 2 | "standard" | | 3 | "magnetic" | | 4 | "dance" | | 5 | "racing" |
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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 | 56 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 56 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 86 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 995 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 30.57% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 628 | | uniqueNames | 7 | | maxNameDensity | 2.39 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Quinn | 15 | | Patel | 5 | | Marcus | 2 | | Whitmore | 4 | | Underground | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Patel" | | 2 | "Marcus" | | 3 | "Whitmore" |
| | places | (empty) | | globalScore | 0.306 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | 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 | 995 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 24.88 | | std | 16.38 | | cv | 0.658 | | sampleLengths | | 0 | 53 | | 1 | 35 | | 2 | 46 | | 3 | 4 | | 4 | 20 | | 5 | 36 | | 6 | 9 | | 7 | 47 | | 8 | 14 | | 9 | 12 | | 10 | 36 | | 11 | 8 | | 12 | 33 | | 13 | 39 | | 14 | 5 | | 15 | 14 | | 16 | 49 | | 17 | 7 | | 18 | 7 | | 19 | 36 | | 20 | 20 | | 21 | 48 | | 22 | 17 | | 23 | 13 | | 24 | 24 | | 25 | 43 | | 26 | 9 | | 27 | 18 | | 28 | 33 | | 29 | 40 | | 30 | 3 | | 31 | 32 | | 32 | 6 | | 33 | 54 | | 34 | 28 | | 35 | 2 | | 36 | 34 | | 37 | 6 | | 38 | 48 | | 39 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 56 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 112 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 86 | | ratio | 0.081 | | matches | | 0 | "The abandoned Camden Tube station reeked of stagnant water and something else—something metallic that made Quinn's teeth ache." | | 1 | "But something nagged at her—the position was too neat, too deliberate." | | 2 | "Manicured nails, soft palms—not the hands of someone who crawled through abandoned tunnels for thrills." | | 3 | "The spatter pattern bothered her—too uniform, too contained." | | 4 | "The face bore intricate etchings—symbols that definitely weren't standard navigation markings." | | 5 | "The drop was significant—easily enough to cause fatal injuries." | | 6 | "The trauma was severe—a depression in the skull roughly the size of her palm." |
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| 98.32% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 636 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.0330188679245283 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.0220125786163522 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 86 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 86 | | mean | 11.57 | | std | 6.31 | | cv | 0.545 | | sampleLengths | | 0 | 18 | | 1 | 15 | | 2 | 20 | | 3 | 21 | | 4 | 14 | | 5 | 9 | | 6 | 16 | | 7 | 10 | | 8 | 11 | | 9 | 4 | | 10 | 16 | | 11 | 4 | | 12 | 12 | | 13 | 16 | | 14 | 8 | | 15 | 9 | | 16 | 4 | | 17 | 15 | | 18 | 11 | | 19 | 17 | | 20 | 8 | | 21 | 6 | | 22 | 4 | | 23 | 8 | | 24 | 11 | | 25 | 8 | | 26 | 12 | | 27 | 5 | | 28 | 8 | | 29 | 7 | | 30 | 16 | | 31 | 10 | | 32 | 11 | | 33 | 11 | | 34 | 17 | | 35 | 5 | | 36 | 4 | | 37 | 10 | | 38 | 5 | | 39 | 13 | | 40 | 18 | | 41 | 13 | | 42 | 7 | | 43 | 7 | | 44 | 30 | | 45 | 6 | | 46 | 4 | | 47 | 16 | | 48 | 12 | | 49 | 9 |
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| 82.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5116279069767442 | | totalSentences | 86 | | uniqueOpeners | 44 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 56 | | matches | | 0 | "She descended the crumbling concrete" | | 1 | "She pulled on latex gloves" | | 2 | "She pointed to the mark" | | 3 | "She'd seen enough head trauma" | | 4 | "She crouched and used her" | | 5 | "He approached, tablet forgotten." | | 6 | "She blinked and the sensation" | | 7 | "She knelt and examined the" | | 8 | "She returned to the corpse" | | 9 | "He paused, studying the body" | | 10 | "She pointed to the victim's" | | 11 | "She crouched beside the head" |
| | ratio | 0.214 | |
| 40.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 56 | | matches | | 0 | "The abandoned Camden Tube station" | | 1 | "She descended the crumbling concrete" | | 2 | "Emergency lighting cast sickly yellow" | | 3 | "DS Patel emerged from the" | | 4 | "Quinn's sharp jaw tightened as" | | 5 | "The victim lay sprawled near" | | 6 | "Blood pooled beneath his head," | | 7 | "Patel adjusted his glasses, the" | | 8 | "Quinn knelt beside the body," | | 9 | "Marcus Whitmore wore an expensive" | | 10 | "Quinn studied Whitmore's hands." | | 11 | "She pulled on latex gloves" | | 12 | "The palm bore a small" | | 13 | "She pointed to the mark" | | 14 | "Patel leaned closer, squinting." | | 15 | "Quinn stood and walked the" | | 16 | "The spatter pattern bothered her—too" | | 17 | "She'd seen enough head trauma" | | 18 | "Quinn's brown eyes swept the" | | 19 | "She crouched and used her" |
| | ratio | 0.839 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 44.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 4 | | matches | | 0 | "Manicured nails, soft palms—not the hands of someone who crawled through abandoned tunnels for thrills." | | 1 | "The needle spun wildly before settling in a direction that had nothing to do with magnetic north." | | 2 | "She pointed to the victim's right hand, which clutched something small and white." | | 3 | "Using her pen, she pried open the stiff fingers to reveal a smooth piece of bone, carved with the same type of symbols that decorated the compass." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 2 | | matches | | 0 | "DS Patel emerged, tablet glowing in his hands" | | 1 | "Quinn bagged, her mind racing" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |