| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 87.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1547 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | |
| 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) | |
| 70.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1547 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "electric" | | 1 | "vibrated" | | 2 | "tracing" | | 3 | "scanning" | | 4 | "scanned" | | 5 | "gloom" | | 6 | "echoed" | | 7 | "unspoken" | | 8 | "silence" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
| | 1 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" | | 2 | "hung in the air" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 285 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 285 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 285 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1539 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 102 | | wordCount | 1539 | | uniqueNames | 18 | | maxNameDensity | 1.75 | | worstName | "Aurora" | | maxWindowNameDensity | 4 | | worstWindowName | "Aurora" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Carter | 1 | | Blackwood | 1 | | London | 2 | | Aurora | 27 | | Eva | 20 | | Silas | 9 | | Cardiff | 4 | | Evan | 2 | | July | 1 | | Chinese | 1 | | Soho | 1 | | Empress | 1 | | Rory | 2 | | You | 22 | | Like | 3 | | Yu-Fei | 3 |
| | persons | | 0 | "Carter" | | 1 | "Blackwood" | | 2 | "Aurora" | | 3 | "Eva" | | 4 | "Silas" | | 5 | "Evan" | | 6 | "Rory" | | 7 | "You" | | 8 | "Yu-Fei" |
| | places | | 0 | "Raven" | | 1 | "London" | | 2 | "Cardiff" | | 3 | "July" | | 4 | "Soho" |
| | globalScore | 0.623 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 94 | | 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 | 1539 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 285 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 150 | | mean | 10.26 | | std | 12.3 | | cv | 1.199 | | sampleLengths | | 0 | 90 | | 1 | 53 | | 2 | 20 | | 3 | 50 | | 4 | 8 | | 5 | 28 | | 6 | 1 | | 7 | 11 | | 8 | 44 | | 9 | 9 | | 10 | 2 | | 11 | 2 | | 12 | 4 | | 13 | 4 | | 14 | 15 | | 15 | 3 | | 16 | 8 | | 17 | 2 | | 18 | 2 | | 19 | 3 | | 20 | 32 | | 21 | 8 | | 22 | 15 | | 23 | 8 | | 24 | 4 | | 25 | 1 | | 26 | 8 | | 27 | 10 | | 28 | 4 | | 29 | 48 | | 30 | 12 | | 31 | 4 | | 32 | 12 | | 33 | 4 | | 34 | 7 | | 35 | 2 | | 36 | 19 | | 37 | 8 | | 38 | 7 | | 39 | 3 | | 40 | 2 | | 41 | 8 | | 42 | 4 | | 43 | 15 | | 44 | 13 | | 45 | 2 | | 46 | 3 | | 47 | 18 | | 48 | 13 | | 49 | 14 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 285 | | matches | | 0 | "were manicured" | | 1 | "being asked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 322 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 285 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1548 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.021963824289405683 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.005813953488372093 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 285 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 285 | | mean | 5.4 | | std | 3.78 | | cv | 0.701 | | sampleLengths | | 0 | 18 | | 1 | 17 | | 2 | 19 | | 3 | 20 | | 4 | 16 | | 5 | 14 | | 6 | 21 | | 7 | 18 | | 8 | 3 | | 9 | 17 | | 10 | 4 | | 11 | 12 | | 12 | 10 | | 13 | 9 | | 14 | 15 | | 15 | 3 | | 16 | 5 | | 17 | 3 | | 18 | 18 | | 19 | 7 | | 20 | 1 | | 21 | 4 | | 22 | 7 | | 23 | 7 | | 24 | 4 | | 25 | 9 | | 26 | 6 | | 27 | 18 | | 28 | 2 | | 29 | 7 | | 30 | 2 | | 31 | 2 | | 32 | 4 | | 33 | 3 | | 34 | 1 | | 35 | 6 | | 36 | 4 | | 37 | 5 | | 38 | 3 | | 39 | 5 | | 40 | 3 | | 41 | 2 | | 42 | 2 | | 43 | 3 | | 44 | 9 | | 45 | 13 | | 46 | 3 | | 47 | 7 | | 48 | 4 | | 49 | 4 |
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| 37.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 36 | | diversityRatio | 0.256140350877193 | | totalSentences | 285 | | uniqueOpeners | 73 | |
| 13.77% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 242 | | matches | | | ratio | 0.004 | |
| 18.35% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 122 | | totalSentences | 242 | | matches | | 0 | "He wiped a spot on" | | 1 | "She shook a wet umbrella," | | 2 | "Her coat was expensive wool," | | 3 | "She scanned the room, eyes" | | 4 | "Her heels clicked on the" | | 5 | "She stopped two stools away" | | 6 | "She took a sip of" | | 7 | "She placed her hands on" | | 8 | "Her nails were manicured, pale" | | 9 | "He looked at Eva, then" | | 10 | "You look different." | | 11 | "She looked older." | | 12 | "I looked for you." | | 13 | "It peeled away in a" | | 14 | "You looked in Cardiff." | | 15 | "I moved to London." | | 16 | "I called your parents." | | 17 | "They said you didn't want" | | 18 | "They told the truth." | | 19 | "He slid a napkin under" |
| | ratio | 0.504 | |
| 13.72% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 216 | | totalSentences | 242 | | matches | | 0 | "The green neon sign above" | | 1 | "Maps covered the walls, yellowed" | | 2 | "Aurora Carter sat at the" | | 3 | "The crescent-shaped scar on her" | | 4 | "Silas Blackwood moved behind the" | | 5 | "He wiped a spot on" | | 6 | "The silver signet ring on" | | 7 | "The door opened." | | 8 | "A gust of cold London" | | 9 | "A woman stepped inside." | | 10 | "She shook a wet umbrella," | | 11 | "Her coat was expensive wool," | | 12 | "She scanned the room, eyes" | | 13 | "Aurora looked up." | | 14 | "The bright blue eyes narrowed." | | 15 | "Eva walked over." | | 16 | "Her heels clicked on the" | | 17 | "She stopped two stools away" | | 18 | "Aurora did not smile." | | 19 | "She took a sip of" |
| | ratio | 0.893 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 242 | | matches | | 0 | "Before you left." | | 1 | "Now I'm here." | | 2 | "To check the route." | | 3 | "If I rush, I get" | | 4 | "If you rush, you crash." |
| | ratio | 0.021 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 1 | | matches | | 0 | "The green neon sign above the door buzzed with a low, electric hum that vibrated in the teeth." |
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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 | |