| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.44 | | leniency | 0.88 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 77.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1343 | | totalAiIsmAdverbs | 6 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "deliberately" | | count | 2 |
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| | highlights | | 0 | "gently" | | 1 | "slightly" | | 2 | "very" | | 3 | "deliberately" |
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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) | |
| 47.88% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1343 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "whisper" | | 1 | "echo" | | 2 | "footsteps" | | 3 | "familiar" | | 4 | "weight" | | 5 | "chill" | | 6 | "constructed" | | 7 | "stomach" | | 8 | "raced" | | 9 | "crystal" | | 10 | "shimmered" | | 11 | "flickered" | | 12 | "tapestry" |
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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 | 121 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 121 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1331 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 1108 | | uniqueNames | 17 | | maxNameDensity | 1.17 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 13 | | Quinn | 1 | | Berwick | 1 | | Street | 1 | | Tube | 1 | | Tomás | 12 | | Herrera | 2 | | Raven | 1 | | Nest | 3 | | Saint | 2 | | Christopher | 2 | | Veil | 2 | | Market | 2 | | October | 1 | | Morris | 2 | | London | 2 | | Met | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | 0 | "Berwick" | | 1 | "Street" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "London" |
| | globalScore | 0.913 | | windowScore | 0.667 | |
| 83.33% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like a service entrance for the ol" | | 1 | "robes that seemed to absorb the light around them" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.751 | | wordCount | 1331 | | matches | | 0 | "not the expected abandoned station but a staircase carved directly into the bedrock" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 32.46 | | std | 23.43 | | cv | 0.722 | | sampleLengths | | 0 | 69 | | 1 | 94 | | 2 | 22 | | 3 | 9 | | 4 | 51 | | 5 | 44 | | 6 | 69 | | 7 | 8 | | 8 | 34 | | 9 | 15 | | 10 | 83 | | 11 | 31 | | 12 | 34 | | 13 | 3 | | 14 | 38 | | 15 | 56 | | 16 | 17 | | 17 | 12 | | 18 | 42 | | 19 | 57 | | 20 | 58 | | 21 | 6 | | 22 | 62 | | 23 | 6 | | 24 | 25 | | 25 | 10 | | 26 | 32 | | 27 | 19 | | 28 | 11 | | 29 | 51 | | 30 | 11 | | 31 | 69 | | 32 | 20 | | 33 | 24 | | 34 | 29 | | 35 | 22 | | 36 | 26 | | 37 | 18 | | 38 | 1 | | 39 | 38 | | 40 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 121 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 186 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 134 | | ratio | 0.06 | | matches | | 0 | "The suspect—male, early thirties, dark hoodie pulled low—veered left into an alley without breaking pace." | | 1 | "But the man matched the description Tomás Herrera had given her three hours earlier at the Raven’s Nest—scar on the left forearm, Saint Christopher medallion catching the bar’s green neon when he’d leaned forward to whisper about the Veil Market." | | 2 | "Symbols she didn’t recognize marked the walls at irregular intervals—some glowing faintly, others dull and inert." | | 3 | "She’d heard rumors for years—every cop in the Met had—but standing inside it was something else entirely." | | 4 | "Watched his hands tremble slightly as he’d described the scar on his forearm—a knife attack, he’d said, from his days as a paramedic." | | 5 | "The fabric showed scenes of hunts—hunters on horseback pursuing prey that looked uncomfortably human." | | 6 | "The fake description, the whispered tip at the Nest, the slow, obvious flight through the rain-soaked streets—it had all been an invitation." | | 7 | "A figure filled the entrance—tall, wrapped in robes that seemed to absorb the light around them." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1124 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 33 | | adverbRatio | 0.029359430604982206 | | lyAdverbCount | 20 | | lyAdverbRatio | 0.017793594306049824 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 9.93 | | std | 7.64 | | cv | 0.769 | | sampleLengths | | 0 | 27 | | 1 | 15 | | 2 | 2 | | 3 | 15 | | 4 | 10 | | 5 | 8 | | 6 | 4 | | 7 | 27 | | 8 | 7 | | 9 | 4 | | 10 | 1 | | 11 | 3 | | 12 | 40 | | 13 | 16 | | 14 | 6 | | 15 | 2 | | 16 | 1 | | 17 | 6 | | 18 | 20 | | 19 | 10 | | 20 | 8 | | 21 | 13 | | 22 | 4 | | 23 | 6 | | 24 | 5 | | 25 | 5 | | 26 | 5 | | 27 | 13 | | 28 | 4 | | 29 | 2 | | 30 | 8 | | 31 | 4 | | 32 | 22 | | 33 | 19 | | 34 | 16 | | 35 | 4 | | 36 | 4 | | 37 | 2 | | 38 | 20 | | 39 | 12 | | 40 | 15 | | 41 | 21 | | 42 | 27 | | 43 | 7 | | 44 | 8 | | 45 | 20 | | 46 | 3 | | 47 | 17 | | 48 | 11 | | 49 | 1 |
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| 73.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4626865671641791 | | totalSentences | 134 | | uniqueOpeners | 62 | |
| 93.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 107 | | matches | | 0 | "Then tailed the suspect solo" | | 1 | "Then resumed, faster now." | | 2 | "Just darkness and the suggestion" |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 107 | | matches | | 0 | "Her shoulder clipped a stack" | | 1 | "She hadn’t radioed backup." | | 2 | "She knew it." | | 3 | "She checked her sidearm." | | 4 | "She pushed it aside." | | 5 | "Her flashlight beam cut through" | | 6 | "She’d heard rumors for years—every" | | 7 | "He glanced over his shoulder." | | 8 | "Their eyes met." | | 9 | "His expression wasn’t surprised." | | 10 | "It was resigned." | | 11 | "She’d interviewed him at the" | | 12 | "Her boots squelched on ground" | | 13 | "She kept her flashlight off" | | 14 | "It marked her too clearly" | | 15 | "Her hand rested on her" | | 16 | "She could turn back." | | 17 | "She pushed through the beaded" | | 18 | "He turned and offered her" | | 19 | "His hand was steady now." |
| | ratio | 0.215 | |
| 72.15% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 107 | | matches | | 0 | "Detective Harlow Quinn’s lungs burned" | | 1 | "The suspect—male, early thirties, dark" | | 2 | "Her shoulder clipped a stack" | | 3 | "The sound ricocheted off the" | | 4 | "The alley spat them out" | | 5 | "The suspect vaulted a low" | | 6 | "Harlow’s worn leather watch read" | | 7 | "She hadn’t radioed backup." | | 8 | "She knew it." | | 9 | "Tomás had warned, his warm" | | 10 | "Harlow took the stairs two" | | 11 | "The concrete walls dripped with" | | 12 | "She checked her sidearm." | | 13 | "The familiar weight steadied her" | | 14 | "That split-second where Morris’s voice" | | 15 | "She pushed it aside." | | 16 | "The tunnel smelled of ozone" | | 17 | "Her flashlight beam cut through" | | 18 | "The steps were worn smooth" | | 19 | "Symbols she didn’t recognize marked" |
| | ratio | 0.776 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 107 | | matches | (empty) | | ratio | 0 | |
| 93.02% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 3 | | matches | | 0 | "Detective Harlow Quinn’s lungs burned as her boots hammered the wet pavement, each stride kicking up a spray of rainwater that glinted under the sickly orange s…" | | 1 | "Stalls constructed from salvaged wood and corrugated metal lined winding pathways lit by lanterns that burned with green and purple flame." | | 2 | "Vendors hawked their wares in languages that grated against her ears, their faces obscured by masks or veils or things she preferred not to examine too closely." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 70.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "Tomás had (have)" | | 1 | "the vendor hissed (hiss)" |
| | dialogueSentences | 25 | | tagDensity | 0.24 | | leniency | 0.48 | | rawRatio | 0.333 | | effectiveRatio | 0.16 | |