| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.889 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 823 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 45.32% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 823 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "familiar" | | 1 | "comforting" | | 2 | "silence" | | 3 | "weight" | | 4 | "flickered" | | 5 | "pulsed" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "clenched jaw/fists" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "clenched her fist" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 79 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 79 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 79 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 10 | | totalWords | 821 | | ratio | 0.012 | | matches | | 0 | "Silas." | | 1 | "Why now?" | | 2 | "used" | | 3 | "I am her." | | 4 | "Time to go." |
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| 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 | 24 | | wordCount | 681 | | uniqueNames | 15 | | maxNameDensity | 0.59 | | worstName | "Silas" | | maxWindowNameDensity | 1 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Carter | 2 | | Rory | 2 | | Yu-Fei | 1 | | Cheung | 1 | | Golden | 1 | | Empress | 1 | | Silas | 4 | | Blackwood | 2 | | Spymaster | 1 | | London | 1 | | Evan | 2 | | Aurora | 3 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Rory" | | 4 | "Yu-Fei" | | 5 | "Cheung" | | 6 | "Silas" | | 7 | "Blackwood" | | 8 | "Evan" | | 9 | "Aurora" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 59.09% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 2 | | matches | | 0 | "tasted like ash on her tongue" | | 1 | "felt like a weight, a chain" |
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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 | 821 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 79 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 1 | | mean | 0 | | std | 0 | | cv | 0 | | sampleLengths | | |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 79 | | matches | | |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 111 | | matches | | 0 | "was running" | | 1 | "was running" | | 2 | "was running" | | 3 | "was running, was catching" | | 4 | "was waiting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 79 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 683 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.01903367496339678 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005856515373352855 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 79 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 79 | | mean | 10.39 | | std | 6.61 | | cv | 0.636 | | sampleLengths | | 0 | 17 | | 1 | 15 | | 2 | 15 | | 3 | 12 | | 4 | 19 | | 5 | 9 | | 6 | 15 | | 7 | 18 | | 8 | 21 | | 9 | 19 | | 10 | 14 | | 11 | 2 | | 12 | 1 | | 13 | 5 | | 14 | 9 | | 15 | 16 | | 16 | 5 | | 17 | 9 | | 18 | 6 | | 19 | 2 | | 20 | 3 | | 21 | 14 | | 22 | 12 | | 23 | 4 | | 24 | 12 | | 25 | 7 | | 26 | 12 | | 27 | 8 | | 28 | 3 | | 29 | 4 | | 30 | 6 | | 31 | 8 | | 32 | 7 | | 33 | 7 | | 34 | 16 | | 35 | 11 | | 36 | 11 | | 37 | 11 | | 38 | 13 | | 39 | 15 | | 40 | 9 | | 41 | 9 | | 42 | 14 | | 43 | 18 | | 44 | 23 | | 45 | 8 | | 46 | 24 | | 47 | 42 | | 48 | 25 | | 49 | 13 |
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| 39.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.34177215189873417 | | totalSentences | 79 | | uniqueOpeners | 27 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 76 | | matches | | 0 | "Her breath fogged the window" | | 1 | "She’d taken the job at" | | 2 | "She pushed the thought aside," | | 3 | "She’d lived above Silas Blackwood’s" | | 4 | "She turned, her gaze catching" | | 5 | "He didn’t look at her." | | 6 | "He ordered a scotch, neat," | | 7 | "She recognized the posture, the" | | 8 | "Her breath hitched." | | 9 | "She’d refused, choosing the delivery" | | 10 | "She couldn’t let him see" | | 11 | "She took a step closer," | | 12 | "Her voice was a low" | | 13 | "He didn’t turn." | | 14 | "He didn’t acknowledge her." | | 15 | "He just kept watching the" | | 16 | "His voice was gravel, deep" | | 17 | "She nodded, the silence stretching" | | 18 | "She meant it as a" | | 19 | "He finally turned, his hazel" |
| | ratio | 0.605 | |
| 6.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 76 | | matches | | 0 | "The Raven’s Nest’s green neon" | | 1 | "Aurora Carter, alias Rory, leaned" | | 2 | "Her breath fogged the window" | | 3 | "A delivery van idled outside," | | 4 | "She’d taken the job at" | | 5 | "Evan’s memory still tasted like" | | 6 | "She pushed the thought aside," | | 7 | "The bar’s air hung thick" | | 8 | "She’d lived above Silas Blackwood’s" | | 9 | "Tonight, the usual low murmur" | | 10 | "She turned, her gaze catching" | | 11 | "A silver signet ring glinted" | | 12 | "The limp was subtle, a" | | 13 | "He didn’t look at her." | | 14 | "He ordered a scotch, neat," | | 15 | "She recognized the posture, the" | | 16 | "Her breath hitched." | | 17 | "*Silas.* She’d fled London when" | | 18 | "She’d refused, choosing the delivery" | | 19 | "She couldn’t let him see" |
| | ratio | 0.908 | |
| 65.79% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 76 | | matches | | | ratio | 0.013 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 1 | | matches | | 0 | "*Time to go.* She pushed through the door, the rain soaking her coat, the city’s roar swallowing her." |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 2 | | matches | | 0 | "He held, the silver band catching the bar’s dim light" | | 1 | "He leaned, his voice dropping" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |