| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.42% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 760 | | 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) | |
| 40.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 760 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "fractured" | | 1 | "pulsed" | | 2 | "gloom" | | 3 | "footsteps" | | 4 | "echoing" | | 5 | "flickered" | | 6 | "pounding" | | 7 | "intricate" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 69 | | matches | | |
| 39.34% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 4 | | narrationSentences | 69 | | filterMatches | | | hedgeMatches | | 0 | "began to" | | 1 | "seemed to" | | 2 | "managed to" | | 3 | "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 | 72 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 23 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 751 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 39.81% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 726 | | uniqueNames | 13 | | maxNameDensity | 2.2 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 16 | | Tomás | 1 | | Herrera | 13 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Morris | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | | globalScore | 0.398 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | glossingSentenceCount | 1 | | matches | | 0 | "liquids that seemed to contain captured lightning" |
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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 | 751 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 72 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 34.14 | | std | 20.32 | | cv | 0.595 | | sampleLengths | | 0 | 70 | | 1 | 63 | | 2 | 39 | | 3 | 47 | | 4 | 50 | | 5 | 48 | | 6 | 42 | | 7 | 43 | | 8 | 8 | | 9 | 8 | | 10 | 9 | | 11 | 58 | | 12 | 11 | | 13 | 7 | | 14 | 36 | | 15 | 52 | | 16 | 41 | | 17 | 13 | | 18 | 15 | | 19 | 52 | | 20 | 34 | | 21 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 69 | | matches | | |
| 49.62% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 133 | | matches | | 0 | "was heading" | | 1 | "was speaking" | | 2 | "were walking" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 72 | | ratio | 0.083 | | matches | | 0 | "Thirty yards ahead, the suspect—Tomás Herrera—glanced back, his olive skin pale under the streetlights." | | 1 | "Herrera fumbled with something in his pocket—a bone token—and pressed it against the door." | | 2 | "The smell of wet pavement and exhaust fumes gave way to something ancient—incense, ozone, and something metallic." | | 3 | "The stairs opened into a vast space—an abandoned Tube station, its platform stretched into shadows." | | 4 | "Quinn had heard whispers of it in her investigations—a supernatural black market that moved like a ghost through the city's underground veins." | | 5 | "Through the opening, she could see trees and stars—impossible things underground." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 735 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint blue light pulsed" |
| | adverbCount | 15 | | adverbRatio | 0.02040816326530612 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004081632653061225 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 72 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 72 | | mean | 10.43 | | std | 4.75 | | cv | 0.456 | | sampleLengths | | 0 | 13 | | 1 | 14 | | 2 | 14 | | 3 | 12 | | 4 | 17 | | 5 | 10 | | 6 | 15 | | 7 | 11 | | 8 | 14 | | 9 | 13 | | 10 | 5 | | 11 | 23 | | 12 | 11 | | 13 | 4 | | 14 | 17 | | 15 | 6 | | 16 | 13 | | 17 | 7 | | 18 | 15 | | 19 | 8 | | 20 | 5 | | 21 | 22 | | 22 | 7 | | 23 | 8 | | 24 | 8 | | 25 | 12 | | 26 | 13 | | 27 | 17 | | 28 | 9 | | 29 | 16 | | 30 | 11 | | 31 | 8 | | 32 | 14 | | 33 | 10 | | 34 | 8 | | 35 | 8 | | 36 | 4 | | 37 | 5 | | 38 | 7 | | 39 | 15 | | 40 | 21 | | 41 | 15 | | 42 | 4 | | 43 | 7 | | 44 | 7 | | 45 | 4 | | 46 | 5 | | 47 | 7 | | 48 | 7 | | 49 | 13 |
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| 68.98% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.4305555555555556 | | totalSentences | 72 | | uniqueOpeners | 31 | |
| 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 | 14 | | totalSentences | 67 | | matches | | 0 | "His Saint Christopher medallion bounced" | | 1 | "She'd been following him for" | | 2 | "Her military training screamed against" | | 3 | "She slipped through the door" | | 4 | "She could hear Herrera's footsteps" | | 5 | "Her eyes adjusted to the" | | 6 | "Her eyes, solid black, tracked" | | 7 | "She spotted Herrera near a" | | 8 | "He was speaking with a" | | 9 | "She stumbled but managed to" | | 10 | "She paused at the archway," | | 11 | "She stood at the threshold," | | 12 | "They wouldn't believe her report" | | 13 | "He raised the vial, as" |
| | ratio | 0.209 | |
| 42.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 67 | | matches | | 0 | "Rain lashed against the pavement," | | 1 | "Detective Harlow Quinn's feet splashed" | | 2 | "His Saint Christopher medallion bounced" | | 3 | "She'd been following him for" | | 4 | "Herrera ducked into a narrow" | | 5 | "Quinn followed, her worn leather" | | 6 | "The alley dead-ended at a" | | 7 | "Herrera fumbled with something in" | | 8 | "A faint blue light pulsed" | | 9 | "Quinn hesitated at the threshold." | | 10 | "Her military training screamed against" | | 11 | "She slipped through the door" | | 12 | "The air changed instantly." | | 13 | "The smell of wet pavement" | | 14 | "Quinn pulled her torch from" | | 15 | "She could hear Herrera's footsteps" | | 16 | "The stairs opened into a" | | 17 | "This was the Veil Market." | | 18 | "Quinn had heard whispers of" | | 19 | "Stalls lined the platform, their" |
| | ratio | 0.836 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 65.64% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 4 | | matches | | 0 | "Quinn had heard whispers of it in her investigations—a supernatural black market that moved like a ghost through the city's underground veins." | | 1 | "Her eyes adjusted to the dim light provided by enchanted lanterns that flickered with blue flames." | | 2 | "She spotted Herrera near a stall selling bottled liquids that seemed to contain captured lightning." | | 3 | "He was speaking with a man whose face was covered in intricate tattoos that moved across his skin like living creatures." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |