| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 6 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 626 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 52.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 626 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoing" | | 2 | "standard" | | 3 | "constructed" | | 4 | "jaw clenched" | | 5 | "weight" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 46 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 46 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 50 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 630 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 53.85% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 572 | | uniqueNames | 11 | | maxNameDensity | 1.92 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 11 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Morris | 4 | | Tomás | 1 | | Herrera | 5 | | Underground | 2 | | Tube | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" |
| | places | | | globalScore | 0.538 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | 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 | 630 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 50 | | matches | (empty) | |
| 64.93% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 35 | | std | 13.2 | | cv | 0.377 | | sampleLengths | | 0 | 43 | | 1 | 43 | | 2 | 44 | | 3 | 6 | | 4 | 37 | | 5 | 40 | | 6 | 40 | | 7 | 9 | | 8 | 38 | | 9 | 48 | | 10 | 50 | | 11 | 39 | | 12 | 14 | | 13 | 36 | | 14 | 30 | | 15 | 37 | | 16 | 23 | | 17 | 53 |
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| 90.01% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 46 | | matches | | 0 | "been spotted" | | 1 | "was outnumbered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 102 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 50 | | ratio | 0.08 | | matches | | 0 | "The suspect's hood fell back revealing dark curls - Tomás Herrera." | | 1 | "Instead of surrendering, he picked up speed and descended a set of stairs that Quinn hadn't noticed before - steps that led to a defunct Underground station entrance." | | 2 | "But it was the people - if she could call them that - that made her grip her gun tighter." | | 3 | "Someone hissed - actually hissed - as she passed." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 568 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.02112676056338028 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0035211267605633804 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 50 | | echoCount | 0 | | echoWords | (empty) | |
| 89.98% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 50 | | mean | 12.6 | | std | 4.72 | | cv | 0.375 | | sampleLengths | | 0 | 15 | | 1 | 12 | | 2 | 16 | | 3 | 19 | | 4 | 14 | | 5 | 10 | | 6 | 12 | | 7 | 7 | | 8 | 11 | | 9 | 14 | | 10 | 6 | | 11 | 9 | | 12 | 28 | | 13 | 8 | | 14 | 9 | | 15 | 23 | | 16 | 8 | | 17 | 11 | | 18 | 21 | | 19 | 9 | | 20 | 11 | | 21 | 10 | | 22 | 17 | | 23 | 13 | | 24 | 12 | | 25 | 11 | | 26 | 12 | | 27 | 20 | | 28 | 8 | | 29 | 10 | | 30 | 12 | | 31 | 13 | | 32 | 12 | | 33 | 5 | | 34 | 9 | | 35 | 14 | | 36 | 6 | | 37 | 12 | | 38 | 18 | | 39 | 8 | | 40 | 12 | | 41 | 10 | | 42 | 14 | | 43 | 10 | | 44 | 13 | | 45 | 23 | | 46 | 13 | | 47 | 7 | | 48 | 17 | | 49 | 16 |
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| 91.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.56 | | totalSentences | 50 | | uniqueOpeners | 28 | |
| 72.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 46 | | matches | | 0 | "Instead of surrendering, he picked" |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 7 | | totalSentences | 46 | | matches | | 0 | "He glanced back, warm brown" | | 1 | "Her flashlight beam caught Herrera's" | | 2 | "He pressed his palm against" | | 3 | "He slipped through the gap," | | 4 | "She wedged herself through the" | | 5 | "He paused, looking back at" | | 6 | "Her sharp jaw clenched as" |
| | ratio | 0.152 | |
| 36.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 46 | | matches | | 0 | "Detective Quinn's boots splashed through" | | 1 | "Rain pelted her face, and" | | 2 | "The suspect darted between late-night" | | 3 | "Quinn vaulted over the obstacle," | | 4 | "The figure cut down an" | | 5 | "Quinn followed, hand brushing her" | | 6 | "The suspect's hood fell back" | | 7 | "The former paramedic who'd been" | | 8 | "He glanced back, warm brown" | | 9 | "Quinn paused at the top" | | 10 | "Morris had disappeared investigating rumors" | | 11 | "The stairwell reeked of mold" | | 12 | "Her flashlight beam caught Herrera's" | | 13 | "He pressed his palm against" | | 14 | "He slipped through the gap," | | 15 | "Quinn cursed and took the" | | 16 | "She wedged herself through the" | | 17 | "The passage opened into a" | | 18 | "Stalls constructed from salvaged materials" | | 19 | "Strings of mismatched lights crisscrossed" |
| | ratio | 0.848 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 99.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 2 | | matches | | 0 | "The suspect darted between late-night revelers, knocking over a bin that clattered across the wet pavement." | | 1 | "The passage opened into a cavernous space that had her questioning her sanity." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "Herrera backed, darkness swallowing him" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |