| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 27 | | tagDensity | 0.37 | | leniency | 0.741 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.56% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1552 | | totalAiIsmAdverbs | 2 | | 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) | |
| 61.34% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1552 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "treacherous" | | 1 | "familiar" | | 2 | "gloom" | | 3 | "echoing" | | 4 | "scanned" | | 5 | "clandestine" | | 6 | "potential" | | 7 | "unreadable" | | 8 | "unravel" | | 9 | "tapestry" | | 10 | "resolve" | | 11 | "sentinel" |
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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 | 111 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 111 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 62 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 4 | | totalWords | 1533 | | ratio | 0.003 | | matches | | 0 | "them" | | 1 | "The Raven’s Nest" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1178 | | uniqueNames | 12 | | maxNameDensity | 0.51 | | worstName | "Finch" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Tomás" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 5 | | Morris | 4 | | Finch | 6 | | Raven | 2 | | London | 1 | | Saint | 1 | | Christopher | 1 | | Spain | 1 | | Tomás | 4 | | Nest | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Finch" | | 4 | "Raven" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" | | 8 | "Nest" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | 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 | 1533 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 127 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 37 | | mean | 41.43 | | std | 26.7 | | cv | 0.644 | | sampleLengths | | 0 | 68 | | 1 | 69 | | 2 | 71 | | 3 | 74 | | 4 | 73 | | 5 | 79 | | 6 | 63 | | 7 | 76 | | 8 | 30 | | 9 | 56 | | 10 | 12 | | 11 | 36 | | 12 | 2 | | 13 | 32 | | 14 | 2 | | 15 | 41 | | 16 | 41 | | 17 | 6 | | 18 | 64 | | 19 | 29 | | 20 | 6 | | 21 | 6 | | 22 | 50 | | 23 | 28 | | 24 | 46 | | 25 | 44 | | 26 | 35 | | 27 | 12 | | 28 | 15 | | 29 | 14 | | 30 | 8 | | 31 | 42 | | 32 | 67 | | 33 | 91 | | 34 | 30 | | 35 | 22 | | 36 | 93 |
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| 95.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 111 | | matches | | 0 | "was gone" | | 1 | "was met" | | 2 | "got laughed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 194 | | matches | | |
| 75.37% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 127 | | ratio | 0.024 | | matches | | 0 | "Ahead, a dark shape—a man in a long coat—darted through the neon-lit gloom, a ghost slipping between the late-night crowds spilling from pubs." | | 1 | "The suspect—a weaselly informant named Finch who’d promised her answers about a new, potent alchemical poison hitting the streets—vanished down an alleyway too narrow for anything but desperation." | | 2 | "Tomás studied her face, and something in her expression—the desperation, the grim resolve—must have convinced him arguing was futile." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1197 | | adjectiveStacks | 2 | | stackExamples | | 0 | "new, potent alchemical poison" | | 1 | "warm, beer-soaked air" |
| | adverbCount | 34 | | adverbRatio | 0.028404344193817876 | | lyAdverbCount | 16 | | lyAdverbRatio | 0.013366750208855471 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 12.07 | | std | 10.26 | | cv | 0.85 | | sampleLengths | | 0 | 15 | | 1 | 30 | | 2 | 23 | | 3 | 22 | | 4 | 2 | | 5 | 34 | | 6 | 4 | | 7 | 4 | | 8 | 3 | | 9 | 28 | | 10 | 18 | | 11 | 22 | | 12 | 3 | | 13 | 10 | | 14 | 1 | | 15 | 3 | | 16 | 4 | | 17 | 9 | | 18 | 19 | | 19 | 12 | | 20 | 16 | | 21 | 4 | | 22 | 19 | | 23 | 2 | | 24 | 12 | | 25 | 14 | | 26 | 22 | | 27 | 27 | | 28 | 10 | | 29 | 4 | | 30 | 9 | | 31 | 18 | | 32 | 4 | | 33 | 7 | | 34 | 4 | | 35 | 21 | | 36 | 1 | | 37 | 4 | | 38 | 9 | | 39 | 10 | | 40 | 13 | | 41 | 1 | | 42 | 5 | | 43 | 4 | | 44 | 36 | | 45 | 9 | | 46 | 17 | | 47 | 5 | | 48 | 21 | | 49 | 9 |
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| 61.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4015748031496063 | | totalSentences | 127 | | uniqueOpeners | 51 | |
| 31.45% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 106 | | matches | | | ratio | 0.009 | |
| 31.32% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 106 | | matches | | 0 | "Her sharp jaw was set," | | 1 | "It had been three years" | | 2 | "She wouldn’t lose another." | | 3 | "Her eyes scanned the ground," | | 4 | "She was about to turn," | | 5 | "She knew the place." | | 6 | "She’d never had cause to" | | 7 | "Her hand went to the" | | 8 | "Her eyes swept the room," | | 9 | "She was a cop." | | 10 | "She stuck out like a" | | 11 | "He’d gone to ground." | | 12 | "She moved quickly, weaving between" | | 13 | "She ran her fingers along" | | 14 | "She was so close." | | 15 | "She knew, with a certainty" | | 16 | "She was about to give" | | 17 | "She spun, her hand instinctively" | | 18 | "He had warm brown eyes," | | 19 | "He smelled of antiseptic and" |
| | ratio | 0.472 | |
| 54.34% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 106 | | matches | | 0 | "The rain fell in silver" | | 1 | "Detective Harlow Quinn’s breath came" | | 2 | "Her sharp jaw was set," | | 3 | "It had been three years" | | 4 | "She wouldn’t lose another." | | 5 | "The suspect—a weaselly informant named" | | 6 | "Quinn followed, her shoulder scraping" | | 7 | "The alley dead-ended at a" | | 8 | "Finch was gone." | | 9 | "Her eyes scanned the ground," | | 10 | "A dead end." | | 11 | "A cul-de-sac of failure." | | 12 | "The rain plastered her salt-and-pepper" | | 13 | "She was about to turn," | | 14 | "A doorway, almost invisible in" | | 15 | "She knew the place." | | 16 | "A dive bar, a known" | | 17 | "She’d never had cause to" | | 18 | "Her hand went to the" | | 19 | "The interior was exactly as" |
| | ratio | 0.811 | |
| 47.17% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 106 | | matches | | 0 | "To supernatural patients." |
| | ratio | 0.009 | |
| 45.45% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 6 | | matches | | 0 | "Detective Harlow Quinn’s breath came in ragged clouds, her worn leather watch strap digging into her wrist as she pumped her arms, her legs burning with a famil…" | | 1 | "It had been three years since she’d lost Morris on a night not unlike this one, chasing a suspect who moved with that same unnatural grace into shadows that swa…" | | 2 | "The suspect—a weaselly informant named Finch who’d promised her answers about a new, potent alchemical poison hitting the streets—vanished down an alleyway too …" | | 3 | "Quinn followed, her shoulder scraping against damp brick, the sound of her own pursuit echoing back at her." | | 4 | "The alley dead-ended at a brick wall adorned with a faded, peeling poster for a circus that had left town decades ago." | | 5 | "She saw Finch’s smug face, knew he was down there right now, trading the secrets of a poison that could kill in ways her forensics lab couldn’t even comprehend." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 6 | | matches | | 0 | "Morris’s face flashed, his laugh, the way he’d always called her" | | 1 | "he said, his accent hinting at Spain" | | 2 | "she stated, her voice flat" | | 3 | "He grabbed, his grip surprisingly strong" | | 4 | "she said, the words feeling inadequate" | | 5 | "he whispered, his face pale" |
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| 75.93% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "she stated (state)" | | 1 | "he whispered (whisper)" |
| | dialogueSentences | 27 | | tagDensity | 0.185 | | leniency | 0.37 | | rawRatio | 0.4 | | effectiveRatio | 0.148 | |