| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 13 | | adverbTagCount | 1 | | adverbTags | | 0 | "His accent curled around [around]" |
| | dialogueSentences | 30 | | tagDensity | 0.433 | | leniency | 0.867 | | rawRatio | 0.077 | | effectiveRatio | 0.067 | |
| 88.45% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 866 | | 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) | |
| 53.81% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 866 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "flickered" | | 1 | "warmth" | | 2 | "traced" | | 3 | "echoed" | | 4 | "flicker" | | 5 | "gleaming" |
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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 | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 74 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | 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 | 5 | | markdownWords | 13 | | totalWords | 860 | | ratio | 0.015 | | matches | | 0 | "Focus" | | 1 | "The Raven’s Nest" | | 2 | "The Iliad" | | 3 | "“Some shadows don’t forgive the light.”" | | 4 | "Move." |
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| 62.50% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 1 | | matches | | 0 | "*Focus*, she told herself, the words useless against the ache in her chest." |
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| 85.90% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 702 | | uniqueNames | 11 | | maxNameDensity | 1.28 | | worstName | "Tomás" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Herrera | 1 | | Soho | 1 | | Morris | 5 | | Raven | 2 | | Nest | 2 | | London | 1 | | Saint | 1 | | Christopher | 1 | | Market | 2 | | Tomás | 9 | | Quinn | 7 |
| | persons | | 0 | "Herrera" | | 1 | "Morris" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Market" | | 5 | "Tomás" | | 6 | "Quinn" |
| | places | | | globalScore | 0.859 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 54 | | 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 | 860 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 90 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 22.63 | | std | 21.25 | | cv | 0.939 | | sampleLengths | | 0 | 108 | | 1 | 6 | | 2 | 63 | | 3 | 39 | | 4 | 8 | | 5 | 20 | | 6 | 37 | | 7 | 24 | | 8 | 13 | | 9 | 49 | | 10 | 5 | | 11 | 36 | | 12 | 28 | | 13 | 34 | | 14 | 4 | | 15 | 44 | | 16 | 1 | | 17 | 54 | | 18 | 19 | | 19 | 21 | | 20 | 4 | | 21 | 12 | | 22 | 9 | | 23 | 29 | | 24 | 21 | | 25 | 5 | | 26 | 34 | | 27 | 10 | | 28 | 13 | | 29 | 6 | | 30 | 2 | | 31 | 33 | | 32 | 7 | | 33 | 10 | | 34 | 34 | | 35 | 8 | | 36 | 3 | | 37 | 7 |
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| 95.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 74 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 120 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 90 | | ratio | 0.078 | | matches | | 0 | "She nearly lost him at the corner, her gloved hand gripping the worn leather watch on her wrist—a habit from her old partner, DS Morris." | | 1 | "Maps hung crookedly—London’s forgotten districts, marked with red ink that might have been blood." | | 2 | "“Always the prophet, aren’t you?” He slid a coin across the bar—tarnished, ancient." | | 3 | "“Depends. Do you want to see what’s really out there, Detective?” He nodded toward a bookshelf behind the bar—*The Raven’s Nest*—where a single volume of *The Iliad* sat slightly askew." | | 4 | "The basement reeked of damp stone and something else—sulfur, maybe, or old blood." | | 5 | "“Does it? Or does it just begin?” He gestured to the crowd—faceless silhouettes trading in secrets, in souls." | | 6 | "She looked down to see a bone shard embedded in her boot—Morris’s watch, its chain wrapped around it like a serpent." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 713 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.025245441795231416 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005610098176718092 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 9.56 | | std | 6.29 | | cv | 0.658 | | sampleLengths | | 0 | 9 | | 1 | 16 | | 2 | 29 | | 3 | 25 | | 4 | 13 | | 5 | 2 | | 6 | 14 | | 7 | 6 | | 8 | 16 | | 9 | 3 | | 10 | 14 | | 11 | 16 | | 12 | 14 | | 13 | 10 | | 14 | 12 | | 15 | 17 | | 16 | 7 | | 17 | 1 | | 18 | 3 | | 19 | 15 | | 20 | 2 | | 21 | 8 | | 22 | 9 | | 23 | 7 | | 24 | 13 | | 25 | 5 | | 26 | 13 | | 27 | 6 | | 28 | 9 | | 29 | 4 | | 30 | 3 | | 31 | 7 | | 32 | 11 | | 33 | 28 | | 34 | 5 | | 35 | 6 | | 36 | 30 | | 37 | 7 | | 38 | 8 | | 39 | 13 | | 40 | 10 | | 41 | 7 | | 42 | 5 | | 43 | 12 | | 44 | 4 | | 45 | 13 | | 46 | 7 | | 47 | 13 | | 48 | 11 | | 49 | 1 |
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| 59.26% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3888888888888889 | | totalSentences | 90 | | uniqueOpeners | 35 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 67 | | matches | | 0 | "She nearly lost him at" | | 1 | "He ducked into The Raven’s" | | 2 | "She stepped inside, the warmth" | | 3 | "He didn’t flinch." | | 4 | "His accent curled around the" | | 5 | "She studied his face" | | 6 | "He slid a coin across" | | 7 | "She’d heard the stories about" | | 8 | "He nodded toward a bookshelf" | | 9 | "Her hand drifted to her" | | 10 | "She pushed past him." | | 11 | "She pressed it into the" | | 12 | "He gestured to the crowd—faceless" | | 13 | "She saw Morris’s face in" | | 14 | "His badge, half-buried in ash." | | 15 | "He plucked a syringe from" | | 16 | "His smile sharpened" | | 17 | "She looked down to see" |
| | ratio | 0.269 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 67 | | matches | | 0 | "The rain hammered the pavement" | | 1 | "Quinn’s boots slapped against the" | | 2 | "Tomás Herrera was a blur" | | 3 | "She nearly lost him at" | | 4 | "He ducked into The Raven’s" | | 5 | "The green neon sign flickered" | | 6 | "Quinn didn’t hesitate." | | 7 | "She stepped inside, the warmth" | | 8 | "The walls were lined with" | | 9 | "Maps hung crookedly—London’s forgotten districts," | | 10 | "Tomás was already at the" | | 11 | "A scar traced his forearm," | | 12 | "The bartender, a grizzled man" | | 13 | "Quinn slid onto the stool" | | 14 | "He didn’t flinch." | | 15 | "His accent curled around the" | | 16 | "She studied his face" | | 17 | "The Saint Christopher medallion peeking" | | 18 | "A prayer for safety, or" | | 19 | "Tomás laughed, low and bitter." |
| | ratio | 0.94 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 3 | | matches | | 0 | "Tomás Herrera was a blur of olive skin and dark curls, weaving through the Soho streets with the fluid grace of someone who’d spent years dodging ambulances and…" | | 1 | "Maps hung crookedly—London’s forgotten districts, marked with red ink that might have been blood." | | 2 | "Stalls stretched into darkness, selling vials of glowing liquid, masks with too many eyes, and contracts written in languages that made her teeth ache." |
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| 86.54% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 13 | | uselessAdditionCount | 1 | | matches | | |
| 83.33% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she growled (growl)" | | 1 | "he gasped (gasp)" |
| | dialogueSentences | 30 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0.667 | | effectiveRatio | 0.133 | |