| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.36 | | leniency | 0.72 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 66.02% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 883 | | totalAiIsmAdverbs | 6 | | found | | 0 | | | 1 | | | 2 | | adverb | "barely above a whisper" | | count | 1 |
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| | highlights | | 0 | "very" | | 1 | "slowly" | | 2 | "barely above a whisper" |
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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) | |
| 20.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 883 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "glinting" | | 1 | "wavered" | | 2 | "flawless" | | 3 | "perfect" | | 4 | "tracing" | | 5 | "dancing" | | 6 | "silk" | | 7 | "flickered" | | 8 | "silence" | | 9 | "stomach" | | 10 | "scanning" | | 11 | "whisper" | | 12 | "could feel" | | 13 | "echoed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
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| | highlights | | 0 | "the air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 61 | | matches | (empty) | |
| 72.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 61 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 76 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 11 | | totalWords | 874 | | ratio | 0.013 | | matches | | 0 | "ambush" | | 1 | "trap" | | 2 | "Information Specialist" | | 3 | "You should have stayed in the rain." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 716 | | uniqueNames | 10 | | maxNameDensity | 0.84 | | worstName | "Tomás" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Chen" | | discoveredNames | | Quinn | 1 | | Herrera | 2 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Tomás | 6 | | Morris | 1 | | Marcus | 2 | | Chen | 6 | | Harlow | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Tomás" | | 3 | "Morris" | | 4 | "Marcus" | | 5 | "Chen" | | 6 | "Harlow" |
| | places | | | globalScore | 1 | | windowScore | 0.833 | |
| 38.89% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like curiosities from another worl" | | 1 | "coins that seemed to shift colour in the lantern light" |
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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 | 874 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 76 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 20.81 | | std | 17.68 | | cv | 0.85 | | sampleLengths | | 0 | 57 | | 1 | 45 | | 2 | 4 | | 3 | 66 | | 4 | 14 | | 5 | 24 | | 6 | 2 | | 7 | 7 | | 8 | 6 | | 9 | 50 | | 10 | 4 | | 11 | 9 | | 12 | 47 | | 13 | 31 | | 14 | 11 | | 15 | 48 | | 16 | 53 | | 17 | 33 | | 18 | 9 | | 19 | 4 | | 20 | 9 | | 21 | 3 | | 22 | 20 | | 23 | 12 | | 24 | 13 | | 25 | 9 | | 26 | 15 | | 27 | 4 | | 28 | 52 | | 29 | 15 | | 30 | 14 | | 31 | 4 | | 32 | 23 | | 33 | 21 | | 34 | 14 | | 35 | 28 | | 36 | 11 | | 37 | 41 | | 38 | 12 | | 39 | 7 | | 40 | 3 | | 41 | 20 |
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| 82.25% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 61 | | matches | | 0 | "was, cornered" | | 1 | "was connected" | | 2 | "were required" | | 3 | "were gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 134 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 76 | | ratio | 0.092 | | matches | | 0 | "Instead, she stepped back, her worn leather watch glinting as she checked the time—02:47 AM." | | 1 | "The victim’s sister had recognized him from security footage—Herrera, the paramedic who’d vanished after treating the wounded man at an abandoned warehouse." | | 2 | "But that missing man’s sister had shown her surveillance photos—eyes watching from shadows, movements too fluid for ordinary people." | | 3 | "At the bottom, he pushed open another door—wooden, unmarked, bearing no signs of commercial activity." | | 4 | "And at the back, beneath a sign that read *Information Specialist*, stood the missing man—Marcus Chen, his eyes now a shade too pale, his movements too precise." | | 5 | "The curiosities remained, but the people who sold them—if they were even people—had vanished into thin air." | | 6 | "Nothing—except the sound of her own heartbeat hammering against her ribs like a trapped bird." |
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| 99.95% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 724 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.04005524861878453 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.012430939226519336 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 76 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 76 | | mean | 11.5 | | std | 7.59 | | cv | 0.66 | | sampleLengths | | 0 | 24 | | 1 | 12 | | 2 | 15 | | 3 | 6 | | 4 | 23 | | 5 | 22 | | 6 | 3 | | 7 | 1 | | 8 | 29 | | 9 | 22 | | 10 | 15 | | 11 | 14 | | 12 | 21 | | 13 | 3 | | 14 | 2 | | 15 | 3 | | 16 | 4 | | 17 | 4 | | 18 | 2 | | 19 | 4 | | 20 | 8 | | 21 | 19 | | 22 | 19 | | 23 | 4 | | 24 | 9 | | 25 | 18 | | 26 | 14 | | 27 | 15 | | 28 | 20 | | 29 | 11 | | 30 | 11 | | 31 | 4 | | 32 | 5 | | 33 | 22 | | 34 | 17 | | 35 | 23 | | 36 | 30 | | 37 | 27 | | 38 | 6 | | 39 | 9 | | 40 | 4 | | 41 | 9 | | 42 | 3 | | 43 | 4 | | 44 | 16 | | 45 | 10 | | 46 | 2 | | 47 | 7 | | 48 | 6 | | 49 | 9 |
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| 72.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.47368421052631576 | | totalSentences | 76 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 57 | | matches | | 0 | "Instead, she stepped back, her" | | 1 | "Too late for a routine" | | 2 | "Only the three of them" | | 3 | "Instead, a voice echoed from" |
| | ratio | 0.07 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 57 | | matches | | 0 | "His olive skin was slick" | | 1 | "She’d been tracking him for" | | 2 | "she demanded, her accent cutting" | | 3 | "His jaw tightened." | | 4 | "she said, stepping into the" | | 5 | "Her hand drifted to her" | | 6 | "She should have run." | | 7 | "He smiled when he saw" | | 8 | "she asked, not turning around" | | 9 | "she asked, her voice steady" | | 10 | "he said, taking a step" | | 11 | "she asked, her voice barely" | | 12 | "Her pistol spoke twice, twice," | | 13 | "She turned slowly, weapon trained" | | 14 | "He wasn’t there." |
| | ratio | 0.263 | |
| 74.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 44 | | totalSentences | 57 | | matches | | 0 | "The knife flashed in the" | | 1 | "Harlow Quinn’s hand instinctively moved" | | 2 | "Tomás Herrera’s voice carried a" | | 3 | "His olive skin was slick" | | 4 | "The knife wavered." | | 5 | "She’d been tracking him for" | | 6 | "The victim’s sister had recognized" | | 7 | "she demanded, her accent cutting" | | 8 | "Tomás nodded toward the alley’s" | | 9 | "His jaw tightened." | | 10 | "The lie was flawless." | | 11 | "Harlow’s training kicked in." | | 12 | "Every instinct screamed *ambush*, every" | | 13 | "Whatever was happening down there," | | 14 | "The door creaked open." | | 15 | "she said, stepping into the" | | 16 | "The descent was steep, concrete" | | 17 | "Tomás followed at her pace," | | 18 | "Her hand drifted to her" | | 19 | "Tomás whispered, as if the" |
| | ratio | 0.772 | |
| 87.72% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 57 | | matches | | 0 | "Now, here he was, cornered" |
| | ratio | 0.018 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 6 | | matches | | 0 | "She’d been tracking him for two hours, following breadcrumbs through Soho’s winding streets after spotting him at The Raven’s Nest with a man who’d gone missing…" | | 1 | "The descent was steep, concrete steps slick with something that might have been rainwater or something less clean." | | 2 | "But detectives don’t retreat from mysteries, especially not ones that smell like her partner’s unsolved case file." | | 3 | "The cavernous space stretched before them, lit by lanterns that cast dancing shadows across stalls selling what looked like curiosities from another world." | | 4 | "And at the back, beneath a sign that read *Information Specialist*, stood the missing man—Marcus Chen, his eyes now a shade too pale, his movements too precise." | | 5 | "Only the three of them stood in that impossible space, surrounded by the silence that followed something important being broken." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 5 | | matches | | 0 | "she demanded, her accent cutting through the rain like a blade" | | 1 | "Tomás whispered, as if the walls might overhear" | | 2 | "she asked, not turning around" | | 3 | "she asked, her voice steady despite the ice forming in her stomach" | | 4 | "she asked, her voice barely above a whisper" |
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| 70.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "she demanded (demand)" | | 1 | "Tomás whispered (whisper)" |
| | dialogueSentences | 25 | | tagDensity | 0.28 | | leniency | 0.56 | | rawRatio | 0.286 | | effectiveRatio | 0.16 | |