| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1246 | | 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) | |
| 31.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1246 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "weight" | | 1 | "perfect" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "silence" | | 5 | "tracing" | | 6 | "potential" | | 7 | "stark" | | 8 | "rhythmic" | | 9 | "vibrated" | | 10 | "shimmered" | | 11 | "intricate" | | 12 | "framework" | | 13 | "hulking" | | 14 | "gleaming" | | 15 | "unreadable" |
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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 | 97 | | matches | (empty) | |
| 83.95% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 97 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "happened to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1238 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 1184 | | uniqueNames | 14 | | maxNameDensity | 0.68 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 8 | | Quinn | 1 | | Elias | 8 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Soho | 1 | | Morris | 3 | | Kent | 1 | | Victorian | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Met | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Elias" | | 3 | "Morris" |
| | places | | 0 | "Charing" | | 1 | "Cross" | | 2 | "Road" | | 3 | "Soho" | | 4 | "Kent" |
| | globalScore | 1 | | windowScore | 1 | |
| 82.43% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed distant muffled" | | 1 | "skulls that seemed to watch her with empty eye sockets" |
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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 | 1238 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 100 | | matches | | |
| 91.86% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 53.83 | | std | 25.38 | | cv | 0.472 | | sampleLengths | | 0 | 70 | | 1 | 77 | | 2 | 82 | | 3 | 59 | | 4 | 1 | | 5 | 85 | | 6 | 59 | | 7 | 22 | | 8 | 30 | | 9 | 15 | | 10 | 52 | | 11 | 53 | | 12 | 16 | | 13 | 70 | | 14 | 62 | | 15 | 58 | | 16 | 15 | | 17 | 62 | | 18 | 60 | | 19 | 84 | | 20 | 42 | | 21 | 70 | | 22 | 94 |
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| 94.41% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 97 | | matches | | 0 | "was hinged" | | 1 | "been pushed" | | 2 | "was paved" |
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| 83.72% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 172 | | matches | | 0 | "was chasing" | | 1 | "was easing" | | 2 | "was watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 100 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1192 | | adjectiveStacks | 2 | | stackExamples | | 0 | "humid, rain-soaked air" | | 1 | "narrow, brick-lined tunnel," |
| | adverbCount | 25 | | adverbRatio | 0.02097315436241611 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.010906040268456376 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 12.38 | | std | 8.22 | | cv | 0.664 | | sampleLengths | | 0 | 23 | | 1 | 27 | | 2 | 20 | | 3 | 24 | | 4 | 37 | | 5 | 2 | | 6 | 14 | | 7 | 20 | | 8 | 14 | | 9 | 10 | | 10 | 12 | | 11 | 18 | | 12 | 8 | | 13 | 9 | | 14 | 16 | | 15 | 8 | | 16 | 15 | | 17 | 11 | | 18 | 1 | | 19 | 17 | | 20 | 1 | | 21 | 4 | | 22 | 4 | | 23 | 22 | | 24 | 13 | | 25 | 15 | | 26 | 3 | | 27 | 6 | | 28 | 11 | | 29 | 3 | | 30 | 7 | | 31 | 28 | | 32 | 10 | | 33 | 11 | | 34 | 11 | | 35 | 30 | | 36 | 15 | | 37 | 2 | | 38 | 8 | | 39 | 5 | | 40 | 7 | | 41 | 21 | | 42 | 4 | | 43 | 5 | | 44 | 6 | | 45 | 5 | | 46 | 5 | | 47 | 14 | | 48 | 9 | | 49 | 14 |
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| 47.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.37 | | totalSentences | 100 | | uniqueOpeners | 37 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 92 | | matches | | 0 | "Just a sudden, explosive movement" | | 1 | "Too large for a normal" | | 2 | "Instead, she pulled a small," | | 3 | "Further in, the main cavern" |
| | ratio | 0.043 | |
| 85.22% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 92 | | matches | | 0 | "He moved with a desperate" | | 1 | "She ignored the burn in" | | 2 | "It was a perfect place" | | 3 | "He hadn’t just vanished." | | 4 | "He’d found a door." | | 5 | "She moved forward, each step" | | 6 | "She crouched, her fingers tracing" | | 7 | "It was hinged." | | 8 | "It had been pushed open" | | 9 | "Her radio crackled, the sound" | | 10 | "She’d switched it to a" | | 11 | "She peered into the darkness." | | 12 | "She remembered the file on" | | 13 | "He dealt in the impossible." | | 14 | "It meant another loose end," | | 15 | "It meant admitting that the" | | 16 | "She holstered her pistol." | | 17 | "It would be useless in" | | 18 | "She landed in a crouch," | | 19 | "She was in a narrow," |
| | ratio | 0.337 | |
| 52.39% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 92 | | matches | | 0 | "The heel of Harlow Quinn’s" | | 1 | "The man she was chasing," | | 2 | "He moved with a desperate" | | 3 | "She ignored the burn in" | | 4 | "The suspect, a low-level information" | | 5 | "Harlow rounded a corner, the" | | 6 | "The city lights of Soho" | | 7 | "Harlow followed, her hand instinctively" | | 8 | "A nervous habit she’d picked" | | 9 | "The service road stank of" | | 10 | "It was a perfect place" | | 11 | "Harlow slowed her pace, her" | | 12 | "The sound of Elias’s frantic" | | 13 | "Harlow pressed herself against the" | | 14 | "He hadn’t just vanished." | | 15 | "He’d found a door." | | 16 | "She moved forward, each step" | | 17 | "The road ended in a" | | 18 | "A storm drain." | | 19 | "She crouched, her fingers tracing" |
| | ratio | 0.815 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 79.08% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 5 | | matches | | 0 | "The suspect, a low-level information broker named Elias who’d been selling state secrets to entities that weren’t on any official register, had bolted the momen…" | | 1 | "Just a sudden, explosive movement that had sent his table of craft beers flying." | | 2 | "She moved forward, each step a deliberate, careful placement on the ground to avoid any sound that might give away her position." | | 3 | "The kind of case that gnawed at the edges of her rational, ordered world." | | 4 | "Her torch beam swept across walls covered in shelves, each one crammed with jars of glowing liquids, bundles of dried herbs that shimmered faintly, and animal s…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |