| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.053 | | leniency | 0.105 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.50% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 909 | | totalAiIsmAdverbs | 1 | | 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) | |
| 44.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 909 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "pulse" | | 1 | "warmth" | | 2 | "flicked" | | 3 | "silence" | | 4 | "whisper" | | 5 | "intricate" | | 6 | "echoed" | | 7 | "scanned" |
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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 | 73 | | matches | (empty) | |
| 84.15% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 73 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 891 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.82% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 24 | | wordCount | 762 | | uniqueNames | 8 | | maxNameDensity | 1.44 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Tube | 1 | | Tomás | 7 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Herrera" | | 5 | "Tomás" |
| | places | (empty) | | globalScore | 0.778 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | glossingSentenceCount | 1 | | matches | | 0 | "jewelry that seemed to pulse with its own heartbeat, books bound in what looked like human skin" | | 1 | "looked like human skin" |
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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 | 891 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 92 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 21.73 | | std | 15.78 | | cv | 0.726 | | sampleLengths | | 0 | 49 | | 1 | 3 | | 2 | 54 | | 3 | 39 | | 4 | 39 | | 5 | 28 | | 6 | 44 | | 7 | 5 | | 8 | 27 | | 9 | 17 | | 10 | 6 | | 11 | 14 | | 12 | 33 | | 13 | 11 | | 14 | 11 | | 15 | 31 | | 16 | 36 | | 17 | 16 | | 18 | 12 | | 19 | 5 | | 20 | 7 | | 21 | 9 | | 22 | 51 | | 23 | 10 | | 24 | 17 | | 25 | 33 | | 26 | 19 | | 27 | 36 | | 28 | 3 | | 29 | 64 | | 30 | 11 | | 31 | 21 | | 32 | 20 | | 33 | 26 | | 34 | 16 | | 35 | 9 | | 36 | 11 | | 37 | 9 | | 38 | 32 | | 39 | 4 | | 40 | 3 |
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| 95.65% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 73 | | matches | | 0 | "being hunted" | | 1 | "was carved" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 136 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 13 | | semicolonCount | 0 | | flaggedSentences | 12 | | totalSentences | 92 | | ratio | 0.13 | | matches | | 0 | "Harlow Quinn’s boots splashed through puddles as she sprinted after the figure darting ahead—hood up, shoulders hunched, moving with the desperate speed of someone who knew they were being hunted." | | 1 | "Ahead, the suspect ducked into a recessed doorway—just a shadow against darker shadows." | | 2 | "The green neon sign above the entrance buzzed faintly—The Raven’s Nest." | | 3 | "Then, from the corner of the room—a scrape of chair legs." | | 4 | "She crossed the room, running her fingers along the spines of the books until she found the trigger—a slight give beneath her touch." | | 5 | "The scent of damp earth and something sharper—ozone, maybe—rose to meet her." | | 6 | "Voices echoed ahead—laughter, bartering, the occasional sharp disagreement." | | 7 | "They rounded a bend, and the tunnel opened into a cavernous space—an abandoned Tube station repurposed into a sprawling market." | | 8 | "Vendors hawked wares that made no sense—glass vials filled with swirling mist, jewelry that seemed to pulse with its own heartbeat, books bound in what looked like human skin." | | 9 | "There—near a stall selling what appeared to be teeth strung on wire." | | 10 | "A vendor blocked her path—a hunched figure with skin like tree bark." | | 11 | "The curtain wasn’t fabric—it was cold, slick, like touching the surface of still water." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 734 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.02452316076294278 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0027247956403269754 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 9.68 | | std | 5.72 | | cv | 0.59 | | sampleLengths | | 0 | 19 | | 1 | 30 | | 2 | 3 | | 3 | 11 | | 4 | 18 | | 5 | 11 | | 6 | 14 | | 7 | 11 | | 8 | 15 | | 9 | 13 | | 10 | 13 | | 11 | 11 | | 12 | 15 | | 13 | 3 | | 14 | 11 | | 15 | 7 | | 16 | 7 | | 17 | 9 | | 18 | 10 | | 19 | 14 | | 20 | 11 | | 21 | 5 | | 22 | 12 | | 23 | 15 | | 24 | 5 | | 25 | 12 | | 26 | 3 | | 27 | 3 | | 28 | 12 | | 29 | 2 | | 30 | 4 | | 31 | 11 | | 32 | 11 | | 33 | 7 | | 34 | 6 | | 35 | 5 | | 36 | 6 | | 37 | 5 | | 38 | 13 | | 39 | 10 | | 40 | 8 | | 41 | 3 | | 42 | 23 | | 43 | 10 | | 44 | 4 | | 45 | 12 | | 46 | 5 | | 47 | 7 | | 48 | 5 | | 49 | 7 |
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| 70.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.45652173913043476 | | totalSentences | 92 | | uniqueOpeners | 42 | |
| 46.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 72 | | matches | | 0 | "Then, from the corner of" |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 72 | | matches | | 0 | "Her quarry skidded around a" | | 1 | "She approached, cautious." | | 2 | "His gaze flicked to her" | | 3 | "Her fingers tapped once against" | | 4 | "She turned toward him, shoulders" | | 5 | "She crossed the room, running" | | 6 | "She met his warm brown" | | 7 | "He hesitated, then reached into" | | 8 | "They rounded a bend, and" | | 9 | "She scanned the crowd, searching" | | 10 | "She shook him off but" | | 11 | "She shrugged him off and" |
| | ratio | 0.167 | |
| 1.67% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 72 | | matches | | 0 | "The rain hammered down on" | | 1 | "Harlow Quinn’s boots splashed through" | | 2 | "The command ripped from her" | | 3 | "The suspect vaulted over a" | | 4 | "Quinn followed, her sharp jaw" | | 5 | "Military training kept her pace" | | 6 | "The alley stank of wet" | | 7 | "Her quarry skidded around a" | | 8 | "Wood splintered under Quinn’s boots" | | 9 | "Quinn slowed, hand hovering near" | | 10 | "The door creaked open, revealing" | | 11 | "She approached, cautious." | | 12 | "The green neon sign above" | | 13 | "The door didn’t resist when" | | 14 | "Warmth and the murmur of" | | 15 | "The bar was half-empty, patrons" | | 16 | "Maps and photographs covered the" | | 17 | "A bartender with a tattooed" | | 18 | "His gaze flicked to her" | | 19 | "Quinn strode to the counter." |
| | ratio | 0.917 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 72 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 2 | | matches | | 0 | "Harlow Quinn’s boots splashed through puddles as she sprinted after the figure darting ahead—hood up, shoulders hunched, moving with the desperate speed of some…" | | 1 | "Vendors hawked wares that made no sense—glass vials filled with swirling mist, jewelry that seemed to pulse with its own heartbeat, books bound in what looked l…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "Tomás Herrera said, voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.053 | | leniency | 0.105 | | rawRatio | 0 | | effectiveRatio | 0 | |