| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 78.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1159 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "softly" | | 1 | "quickly" | | 2 | "really" |
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| 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) | |
| 22.35% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1159 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "chill" | | 1 | "weight" | | 2 | "silence" | | 3 | "sanctuary" | | 4 | "reminder" | | 5 | "predator" | | 6 | "scanning" | | 7 | "tension" | | 8 | "testament" | | 9 | "tracing" | | 10 | "unspoken" | | 11 | "familiar" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "eyes widened" | | 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 | 104 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 104 | | 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 | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1159 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 8 | | matches | | 0 | "Rory, he said." | | 1 | "Julian, she replied." | | 2 | "I heard you were in London, he said." | | 3 | "Delivery driver, Aurora said." | | 4 | "I made a name, Julian said." | | 5 | "I should have called, Julian said finally." | | 6 | "Well, she said softly." | | 7 | "To the present, Aurora corrected." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 1159 | | uniqueNames | 17 | | maxNameDensity | 0.95 | | worstName | "Julian" | | maxWindowNameDensity | 2 | | worstWindowName | "Julian" | | discoveredNames | | Soho | 2 | | Golden | 1 | | Empress | 1 | | Raven | 2 | | Nest | 2 | | Silas | 5 | | London | 3 | | Cardiff | 2 | | Julian | 11 | | Pre-Law | 1 | | University | 1 | | Bar | 2 | | Aurora | 8 | | City | 1 | | Evan | 1 | | Prague | 2 | | You | 3 |
| | persons | | 0 | "Raven" | | 1 | "Silas" | | 2 | "Julian" | | 3 | "University" | | 4 | "Aurora" | | 5 | "Evan" | | 6 | "You" |
| | places | | 0 | "Soho" | | 1 | "Golden" | | 2 | "London" | | 3 | "Cardiff" | | 4 | "Bar" | | 5 | "City" | | 6 | "Prague" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a man who had won the battle" |
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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 | 1159 | | 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 | 33 | | mean | 35.12 | | std | 30.96 | | cv | 0.881 | | sampleLengths | | 0 | 89 | | 1 | 92 | | 2 | 97 | | 3 | 4 | | 4 | 88 | | 5 | 59 | | 6 | 30 | | 7 | 14 | | 8 | 18 | | 9 | 14 | | 10 | 94 | | 11 | 37 | | 12 | 31 | | 13 | 16 | | 14 | 17 | | 15 | 23 | | 16 | 39 | | 17 | 72 | | 18 | 4 | | 19 | 2 | | 20 | 71 | | 21 | 10 | | 22 | 6 | | 23 | 5 | | 24 | 60 | | 25 | 14 | | 26 | 28 | | 27 | 4 | | 28 | 60 | | 29 | 8 | | 30 | 18 | | 31 | 5 | | 32 | 30 |
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| 88.39% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 104 | | matches | | 0 | "was tucked" | | 1 | "was gone" | | 2 | "was pinned" | | 3 | "was written" | | 4 | "was written" |
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| 98.48% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 197 | | matches | | 0 | "was sitting" | | 1 | "was wearing" | | 2 | "was thinning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 104 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1170 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 31 | | adverbRatio | 0.026495726495726495 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.01282051282051282 | |
| 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 | 11.14 | | std | 8.14 | | cv | 0.73 | | sampleLengths | | 0 | 22 | | 1 | 28 | | 2 | 10 | | 3 | 17 | | 4 | 12 | | 5 | 15 | | 6 | 36 | | 7 | 25 | | 8 | 1 | | 9 | 1 | | 10 | 1 | | 11 | 13 | | 12 | 12 | | 13 | 16 | | 14 | 21 | | 15 | 16 | | 16 | 32 | | 17 | 4 | | 18 | 18 | | 19 | 20 | | 20 | 3 | | 21 | 20 | | 22 | 20 | | 23 | 7 | | 24 | 11 | | 25 | 7 | | 26 | 21 | | 27 | 4 | | 28 | 16 | | 29 | 9 | | 30 | 4 | | 31 | 8 | | 32 | 9 | | 33 | 11 | | 34 | 3 | | 35 | 3 | | 36 | 7 | | 37 | 8 | | 38 | 3 | | 39 | 11 | | 40 | 22 | | 41 | 28 | | 42 | 9 | | 43 | 35 | | 44 | 12 | | 45 | 25 | | 46 | 17 | | 47 | 8 | | 48 | 5 | | 49 | 1 |
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| 37.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.2980769230769231 | | totalSentences | 104 | | uniqueOpeners | 31 | |
| 68.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 97 | | matches | | 0 | "Instead, she pushed through the" | | 1 | "Then she saw him." |
| | ratio | 0.021 | |
| 5.57% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 52 | | totalSentences | 97 | | matches | | 0 | "She didn't want to go" | | 1 | "It was a sanctuary of" | | 2 | "She moved toward the bar," | | 3 | "She was twenty-five, but the" | | 4 | "Her straight shoulder-length black hair" | | 5 | "He was sitting at a" | | 6 | "He was wearing a suit" | | 7 | "It was Julian." | | 8 | "They had sat in the" | | 9 | "He had been the one" | | 10 | "He had been the one" | | 11 | "He looked older than his" | | 12 | "He had changed significantly." | | 13 | "She could walk away." | | 14 | "She could go upstairs and" | | 15 | "He hadn't called her that" | | 16 | "Her voice was steady, cool-headed," | | 17 | "He was a mountain of" | | 18 | "He paused when he saw" | | 19 | "He knew better than to" |
| | ratio | 0.536 | |
| 27.01% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 97 | | matches | | 0 | "The rain in Soho had" | | 1 | "Aurora pulled her collar up" | | 2 | "She didn't want to go" | | 3 | "The silence of the room" | | 4 | "The air inside was thick" | | 5 | "It was a sanctuary of" | | 6 | "The walls were a collage" | | 7 | "Places where Silas had bled" | | 8 | "She moved toward the bar," | | 9 | "She was twenty-five, but the" | | 10 | "Her straight shoulder-length black hair" | | 11 | "The small crescent-shaped scar on" | | 12 | "He was sitting at a" | | 13 | "He was wearing a suit" | | 14 | "It was Julian." | | 15 | "They had sat in the" | | 16 | "He had been the one" | | 17 | "He had been the one" | | 18 | "Julian looked up as the" | | 19 | "He looked older than his" |
| | ratio | 0.866 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 97 | | matches | | 0 | "To the past." | | 1 | "To the present, Aurora corrected." |
| | ratio | 0.021 | |
| 99.13% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 3 | | matches | | 0 | "It was a sanctuary of shadows, illuminated only by the amber glow of low-hanging lamps and the distinctive green neon sign that buzzed softly above the entrance…" | | 1 | "Her straight shoulder-length black hair was tucked behind her ears, framing a face that had learned to keep its expressions neutral." | | 2 | "The small crescent-shaped scar on her left wrist stood out against her pale skin, a white reminder of a childhood accident that felt trivial compared to the thi…" |
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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 | |