| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 23 | | tagDensity | 0.304 | | leniency | 0.609 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.76% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1082 | | 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) | |
| 53.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1082 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "streaming" | | 1 | "quickened" | | 2 | "calculated" | | 3 | "flickered" | | 4 | "echoed" | | 5 | "oppressive" | | 6 | "silence" | | 7 | "weight" | | 8 | "dancing" | | 9 | "warmth" |
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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 | 88 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 88 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1073 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.73% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 880 | | uniqueNames | 15 | | maxNameDensity | 2.05 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Harlow | 1 | | Quinn | 18 | | Camden | 2 | | Raven | 1 | | Nest | 1 | | Static | 2 | | Town | 1 | | Tube | 2 | | Morris | 5 | | Underground | 2 | | Saint | 1 | | Christopher | 1 | | Spanish | 1 | | Tomás | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Static" | | 3 | "Morris" | | 4 | "Underground" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "Camden" | | 2 | "Raven" | | 3 | "Town" |
| | globalScore | 0.477 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 70 | | 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 | 1073 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 28.24 | | std | 19.34 | | cv | 0.685 | | sampleLengths | | 0 | 42 | | 1 | 58 | | 2 | 48 | | 3 | 65 | | 4 | 46 | | 5 | 27 | | 6 | 8 | | 7 | 19 | | 8 | 3 | | 9 | 55 | | 10 | 11 | | 11 | 41 | | 12 | 6 | | 13 | 59 | | 14 | 5 | | 15 | 50 | | 16 | 42 | | 17 | 49 | | 18 | 4 | | 19 | 31 | | 20 | 8 | | 21 | 60 | | 22 | 14 | | 23 | 6 | | 24 | 39 | | 25 | 12 | | 26 | 15 | | 27 | 19 | | 28 | 11 | | 29 | 19 | | 30 | 3 | | 31 | 44 | | 32 | 16 | | 33 | 33 | | 34 | 44 | | 35 | 3 | | 36 | 26 | | 37 | 32 |
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| 97.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 88 | | matches | | 0 | "been replaced" | | 1 | "been led" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 157 | | matches | | |
| 60.44% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 104 | | ratio | 0.029 | | matches | | 0 | "The suspect—one of the group her department had been tracking—moved with purpose through the downpour, unaware of her presence." | | 1 | "Then she spotted it—wet footprints leading toward a service door marked \"Authorized Personnel Only.\"" | | 2 | "The air smelled of herbs, smoke, and something else—something that made her skin crawl." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 888 | | adjectiveStacks | 1 | | stackExamples | | 0 | "revealing graffiti-covered tiles" |
| | adverbCount | 16 | | adverbRatio | 0.018018018018018018 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.00563063063063063 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 10.32 | | std | 5.24 | | cv | 0.508 | | sampleLengths | | 0 | 15 | | 1 | 27 | | 2 | 12 | | 3 | 19 | | 4 | 13 | | 5 | 2 | | 6 | 12 | | 7 | 15 | | 8 | 12 | | 9 | 10 | | 10 | 11 | | 11 | 8 | | 12 | 13 | | 13 | 16 | | 14 | 14 | | 15 | 14 | | 16 | 11 | | 17 | 6 | | 18 | 8 | | 19 | 13 | | 20 | 8 | | 21 | 16 | | 22 | 11 | | 23 | 3 | | 24 | 5 | | 25 | 8 | | 26 | 11 | | 27 | 3 | | 28 | 10 | | 29 | 11 | | 30 | 16 | | 31 | 18 | | 32 | 11 | | 33 | 6 | | 34 | 9 | | 35 | 14 | | 36 | 12 | | 37 | 6 | | 38 | 8 | | 39 | 12 | | 40 | 6 | | 41 | 15 | | 42 | 18 | | 43 | 5 | | 44 | 8 | | 45 | 15 | | 46 | 12 | | 47 | 15 | | 48 | 4 | | 49 | 16 |
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| 74.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.46153846153846156 | | totalSentences | 104 | | uniqueOpeners | 48 | |
| 38.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 86 | | matches | | 0 | "Then she spotted it—wet footprints" |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 86 | | matches | | 0 | "She pushed off the wall" | | 1 | "Her boots splashed through puddles," | | 2 | "She'd been watching the bar" | | 3 | "They crossed the road without" | | 4 | "She spoke into her radio," | | 5 | "She watched the figure disappear" | | 6 | "Her sharp jaw set with" | | 7 | "She pulled out her torch" | | 8 | "Her torch beam cut through" | | 9 | "She vaulted the barriers and" | | 10 | "She eased the door open" | | 11 | "She'd studied every map of" | | 12 | "He wore a Saint Christopher" | | 13 | "His accent carried traces of" | | 14 | "He gestured at the crowd" | | 15 | "They made eye contact across" | | 16 | "She'd been led here." |
| | ratio | 0.198 | |
| 41.40% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 86 | | matches | | 0 | "Detective Harlow Quinn pressed herself" | | 1 | "The suspect—one of the group" | | 2 | "Quinn's worn leather watch caught" | | 3 | "The streets had emptied, leaving" | | 4 | "She pushed off the wall" | | 5 | "Her boots splashed through puddles," | | 6 | "The suspect turned left at" | | 7 | "Quinn quickened her pace, military" | | 8 | "The chase had started outside" | | 9 | "She'd been watching the bar" | | 10 | "The green neon sign had" | | 11 | "The same instincts that had" | | 12 | "A taxi rushed past, throwing" | | 13 | "Quinn sidestepped it without breaking" | | 14 | "The suspect was pulling away," | | 15 | "They crossed the road without" | | 16 | "Quinn waited for it to" | | 17 | "She spoke into her radio," | | 18 | "Static crackled back." | | 19 | "She watched the figure disappear" |
| | ratio | 0.837 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 86 | | matches | (empty) | | ratio | 0 | |
| 80.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 4 | | matches | | 0 | "Quinn swung her torch, catching a flash of motion disappearing down the escalator to the platforms." | | 1 | "Lanterns hung from chains, casting dancing shadows on stone walls that looked far older than the Tube system." | | 2 | "He wore a Saint Christopher medallion that caught the lamplight." | | 3 | "Quinn saw objects that defied explanation, heard languages she didn't recognize." |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "She spoke, voice steady despite her elevated breathing" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 23 | | tagDensity | 0.043 | | leniency | 0.087 | | rawRatio | 1 | | effectiveRatio | 0.087 | |