| 75.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 16 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0.167 | | effectiveRatio | 0.125 | |
| 76.36% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 846 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "suddenly" | | 1 | "truly" | | 2 | "very" |
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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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 846 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "pounding" | | 1 | "scanning" | | 2 | "gloom" | | 3 | "glinting" | | 4 | "etched" | | 5 | "unraveling" | | 6 | "familiar" | | 7 | "footsteps" | | 8 | "echoed" | | 9 | "otherworldly" | | 10 | "stark" | | 11 | "cacophony" | | 12 | "scanned" | | 13 | "unwavering" | | 14 | "could feel" |
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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 | 1 | | narrationSentences | 62 | | matches | | |
| 96.77% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 62 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 74 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 24 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 842 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 64.41% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 701 | | uniqueNames | 11 | | maxNameDensity | 1.71 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 12 | | Soho | 1 | | Tomás | 1 | | Herrera | 6 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Veil | 2 | | Market | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" |
| | places | | | globalScore | 0.644 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 58 | | 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 | 842 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 38.27 | | std | 21.14 | | cv | 0.552 | | sampleLengths | | 0 | 70 | | 1 | 50 | | 2 | 64 | | 3 | 52 | | 4 | 28 | | 5 | 6 | | 6 | 20 | | 7 | 43 | | 8 | 11 | | 9 | 28 | | 10 | 58 | | 11 | 71 | | 12 | 62 | | 13 | 47 | | 14 | 20 | | 15 | 23 | | 16 | 19 | | 17 | 12 | | 18 | 40 | | 19 | 11 | | 20 | 37 | | 21 | 70 |
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| 93.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 62 | | matches | | 0 | "was carved" | | 1 | "was caught" |
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| 87.01% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 118 | | matches | | 0 | "was stepping" | | 1 | "was brewing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 74 | | ratio | 0.014 | | matches | | 0 | "There— Herrera's olive skin stood out against the gloom, his Saint Christopher medallion glinting under a flickering streetlight." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 650 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.023076923076923078 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.010769230769230769 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 74 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 74 | | mean | 11.38 | | std | 4.66 | | cv | 0.41 | | sampleLengths | | 0 | 20 | | 1 | 19 | | 2 | 15 | | 3 | 16 | | 4 | 12 | | 5 | 18 | | 6 | 20 | | 7 | 10 | | 8 | 13 | | 9 | 24 | | 10 | 17 | | 11 | 12 | | 12 | 16 | | 13 | 11 | | 14 | 10 | | 15 | 3 | | 16 | 4 | | 17 | 10 | | 18 | 14 | | 19 | 6 | | 20 | 11 | | 21 | 9 | | 22 | 13 | | 23 | 13 | | 24 | 8 | | 25 | 9 | | 26 | 6 | | 27 | 5 | | 28 | 8 | | 29 | 20 | | 30 | 5 | | 31 | 8 | | 32 | 10 | | 33 | 17 | | 34 | 18 | | 35 | 7 | | 36 | 8 | | 37 | 10 | | 38 | 15 | | 39 | 10 | | 40 | 12 | | 41 | 9 | | 42 | 18 | | 43 | 16 | | 44 | 7 | | 45 | 11 | | 46 | 10 | | 47 | 8 | | 48 | 10 | | 49 | 20 |
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| 46.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.33783783783783783 | | totalSentences | 74 | | uniqueOpeners | 25 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 61 | | matches | | 0 | "Suddenly, he stopped, turned." | | 1 | "Then he turned and disappeared" | | 2 | "Suddenly, a hand clamped down" |
| | ratio | 0.049 | |
| 42.95% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 61 | | matches | | 0 | "She rounded the corner, her" | | 1 | "He glanced back, panic etched" | | 2 | "It was a rabbit hole," | | 3 | "She drew her gun, the" | | 4 | "She caught sight of Herrera's" | | 5 | "He was close." | | 6 | "His eyes, warm brown in" | | 7 | "he began, but Quinn was" | | 8 | "He held up his hands," | | 9 | "He looked at her, resignation" | | 10 | "She approached the door, her" | | 11 | "It was a heavy wooden" | | 12 | "She'd seen it before, in" | | 13 | "She placed her hand on" | | 14 | "It was warm, pulsing with" | | 15 | "She closed her eyes, took" | | 16 | "She was in way over" | | 17 | "She stepped inside, her heart" | | 18 | "She spotted him at a" | | 19 | "She started towards him, her" |
| | ratio | 0.443 | |
| 17.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 54 | | totalSentences | 61 | | matches | | 0 | "The rain pounded London's streets" | | 1 | "Detective Harlow Quinn, her salt-and-pepper" | | 2 | "The man, Tomás Herrera, darted" | | 3 | "Quinn's breath fogged in the" | | 4 | "She rounded the corner, her" | | 5 | "There— Herrera's olive skin stood" | | 6 | "He glanced back, panic etched" | | 7 | "Quinn hesitated, her worn leather" | | 8 | "The station was a known" | | 9 | "It was a rabbit hole," | | 10 | "She drew her gun, the" | | 11 | "The air grew colder, damp" | | 12 | "The station was a labyrinth" | | 13 | "Quinn's footsteps echoed ominously, her" | | 14 | "She caught sight of Herrera's" | | 15 | "He was close." | | 16 | "His eyes, warm brown in" | | 17 | "he began, but Quinn was" | | 18 | "He held up his hands," | | 19 | "A low rumble echoed through" |
| | ratio | 0.885 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 61 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 35 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 4 | | matches | | 0 | "Then he turned, the door slamming shut behind him" | | 1 | "he said, his voice a low rumble" | | 2 | "He leaned in, his voice dropping to a low growl" | | 3 | "He turned, his cloak billowing behind him" |
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| 87.50% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 16 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0.5 | | effectiveRatio | 0.125 | |