| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 59 | | tagDensity | 0.254 | | leniency | 0.508 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1254 | | 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) | |
| 68.10% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1254 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "flickered" | | 1 | "throbbed" | | 2 | "weight" | | 3 | "silence" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 76 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 76 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 120 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1252 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 97.55% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 858 | | uniqueNames | 7 | | maxNameDensity | 1.05 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 9 | | November | 1 | | Raven | 1 | | Nest | 1 | | Bratislava | 1 | | Soho | 1 | | Silas | 8 |
| | persons | | 0 | "Aurora" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Silas" |
| | places | | | globalScore | 0.976 | | windowScore | 1 | |
| 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 | 1252 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 120 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 23.19 | | std | 25.4 | | cv | 1.095 | | sampleLengths | | 0 | 126 | | 1 | 80 | | 2 | 3 | | 3 | 38 | | 4 | 3 | | 5 | 22 | | 6 | 37 | | 7 | 2 | | 8 | 3 | | 9 | 2 | | 10 | 48 | | 11 | 25 | | 12 | 3 | | 13 | 2 | | 14 | 39 | | 15 | 19 | | 16 | 16 | | 17 | 41 | | 18 | 9 | | 19 | 24 | | 20 | 4 | | 21 | 78 | | 22 | 17 | | 23 | 80 | | 24 | 10 | | 25 | 11 | | 26 | 4 | | 27 | 12 | | 28 | 28 | | 29 | 15 | | 30 | 27 | | 31 | 2 | | 32 | 7 | | 33 | 12 | | 34 | 6 | | 35 | 43 | | 36 | 75 | | 37 | 19 | | 38 | 13 | | 39 | 25 | | 40 | 50 | | 41 | 29 | | 42 | 4 | | 43 | 7 | | 44 | 29 | | 45 | 1 | | 46 | 2 | | 47 | 2 | | 48 | 39 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 76 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 138 | | matches | (empty) | |
| 95.24% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 120 | | ratio | 0.017 | | matches | | 0 | "Maps yellowed at the edges covered the walls, pinned beside black-and-white photographs of faces she didn't recognise—operatives, assets, ghosts." | | 1 | "She recognised one—a man with a scar across his cheek, dead now, killed in a safe house in Bratislava three winters past." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 863 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.01738122827346466 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0034762456546929316 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 120 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 120 | | mean | 10.43 | | std | 7.87 | | cv | 0.755 | | sampleLengths | | 0 | 18 | | 1 | 18 | | 2 | 14 | | 3 | 19 | | 4 | 22 | | 5 | 10 | | 6 | 25 | | 7 | 16 | | 8 | 14 | | 9 | 18 | | 10 | 32 | | 11 | 3 | | 12 | 7 | | 13 | 12 | | 14 | 19 | | 15 | 3 | | 16 | 2 | | 17 | 9 | | 18 | 10 | | 19 | 1 | | 20 | 5 | | 21 | 12 | | 22 | 18 | | 23 | 2 | | 24 | 2 | | 25 | 3 | | 26 | 2 | | 27 | 13 | | 28 | 6 | | 29 | 5 | | 30 | 24 | | 31 | 14 | | 32 | 11 | | 33 | 3 | | 34 | 2 | | 35 | 9 | | 36 | 15 | | 37 | 3 | | 38 | 12 | | 39 | 10 | | 40 | 9 | | 41 | 6 | | 42 | 7 | | 43 | 3 | | 44 | 19 | | 45 | 21 | | 46 | 1 | | 47 | 7 | | 48 | 2 | | 49 | 6 |
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| 43.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.2916666666666667 | | totalSentences | 120 | | uniqueOpeners | 35 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 34.08% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 71 | | matches | | 0 | "She recognised one—a man with" | | 1 | "She looked away, her boots" | | 2 | "She kept her left hand" | | 3 | "His auburn hair had gone" | | 4 | "He moved with a hitch" | | 5 | "He looked up." | | 6 | "His hazel eyes fixed on" | | 7 | "Her bright blue eyes held" | | 8 | "He reached for a bottle" | | 9 | "He poured two fingers into" | | 10 | "She stared at the scar" | | 11 | "He leaned on the bar," | | 12 | "She didn't drink." | | 13 | "His voice dropped, losing its" | | 14 | "He exhaled through his nose," | | 15 | "He tapped his knee, the" | | 16 | "She tightened her grip on" | | 17 | "He pushed himself upright, the" | | 18 | "He stepped around the bar," | | 19 | "He was taller than she" |
| | ratio | 0.465 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 71 | | matches | | 0 | "The green neon above the" | | 1 | "A gust of November wind" | | 2 | "The Raven's Nest smelled of" | | 3 | "Maps yellowed at the edges" | | 4 | "She recognised one—a man with" | | 5 | "She looked away, her boots" | | 6 | "She kept her left hand" | | 7 | "Silas stood behind the bar," | | 8 | "His auburn hair had gone" | | 9 | "The beard matched, trimmed neat" | | 10 | "He moved with a hitch" | | 11 | "He looked up." | | 12 | "The glass stopped halfway to" | | 13 | "His hazel eyes fixed on" | | 14 | "Recognition crashed over him, visible" | | 15 | "Her bright blue eyes held" | | 16 | "The scar on her left" | | 17 | "Silas set the glass down." | | 18 | "The bar top was scarred" | | 19 | "He reached for a bottle" |
| | ratio | 0.986 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 71 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 2 | | matches | | 0 | "He reached for a bottle of bourbon, his hand steady despite the limp that pulled at his hip." | | 1 | "The black hair fell straight to her shoulders, framing a face that had lost its softness." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 3 | | matches | | 0 | "He leaned, the knee locking with a slight grimace" | | 1 | "She set, untouched" | | 2 | "She moved, her coat heavy on her shoulders" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |