| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.26% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2109 | | totalAiIsmAdverbs | 2 | | 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) | |
| 62.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2109 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "pounding" | | 2 | "vibrated" | | 3 | "flickered" | | 4 | "scanned" | | 5 | "could feel" | | 6 | "eyebrow" | | 7 | "silence" | | 8 | "throbbed" | | 9 | "loomed" | | 10 | "footsteps" | | 11 | "echoed" | | 12 | "weight" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "knuckles turned white" | | count | 1 |
| | 2 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "knuckles turned white" | | 2 | "hung in the air" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 452 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 6 | | hedgeCount | 0 | | narrationSentences | 452 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 452 | | 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 | 2109 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 120 | | wordCount | 2109 | | uniqueNames | 8 | | maxNameDensity | 2.13 | | worstName | "Rory" | | maxWindowNameDensity | 5.5 | | worstWindowName | "You" | | discoveredNames | | Moreau | 1 | | London | 2 | | Yu-Fei | 1 | | Lucien | 40 | | Marseille | 1 | | Rory | 45 | | You | 27 | | Let | 3 |
| | persons | | 0 | "Moreau" | | 1 | "Lucien" | | 2 | "Rory" | | 3 | "You" | | 4 | "Let" |
| | places | | | globalScore | 0.433 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 122 | | glossingSentenceCount | 1 | | matches | | 0 | "smelled like rain and old magic" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2109 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 452 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 210 | | mean | 10.04 | | std | 10.43 | | cv | 1.038 | | sampleLengths | | 0 | 72 | | 1 | 48 | | 2 | 8 | | 3 | 34 | | 4 | 3 | | 5 | 27 | | 6 | 3 | | 7 | 51 | | 8 | 5 | | 9 | 35 | | 10 | 4 | | 11 | 7 | | 12 | 23 | | 13 | 2 | | 14 | 12 | | 15 | 3 | | 16 | 11 | | 17 | 1 | | 18 | 2 | | 19 | 6 | | 20 | 3 | | 21 | 30 | | 22 | 11 | | 23 | 2 | | 24 | 4 | | 25 | 27 | | 26 | 9 | | 27 | 15 | | 28 | 12 | | 29 | 12 | | 30 | 15 | | 31 | 14 | | 32 | 11 | | 33 | 3 | | 34 | 3 | | 35 | 28 | | 36 | 21 | | 37 | 6 | | 38 | 7 | | 39 | 14 | | 40 | 23 | | 41 | 9 | | 42 | 6 | | 43 | 13 | | 44 | 2 | | 45 | 4 | | 46 | 30 | | 47 | 7 | | 48 | 2 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 452 | | matches | | 0 | "was gone" | | 1 | "was gone" | | 2 | "were gone" | | 3 | "was drawn" | | 4 | "was hidden" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 469 | | matches | | 0 | "was drowning" | | 1 | "was gathering" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 452 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2110 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 32 | | adverbRatio | 0.015165876777251185 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.002369668246445498 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 452 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 452 | | mean | 4.67 | | std | 2.32 | | cv | 0.497 | | sampleLengths | | 0 | 8 | | 1 | 21 | | 2 | 9 | | 3 | 17 | | 4 | 7 | | 5 | 10 | | 6 | 5 | | 7 | 14 | | 8 | 2 | | 9 | 12 | | 10 | 9 | | 11 | 6 | | 12 | 4 | | 13 | 4 | | 14 | 8 | | 15 | 7 | | 16 | 10 | | 17 | 6 | | 18 | 3 | | 19 | 3 | | 20 | 4 | | 21 | 12 | | 22 | 11 | | 23 | 3 | | 24 | 9 | | 25 | 7 | | 26 | 8 | | 27 | 8 | | 28 | 10 | | 29 | 9 | | 30 | 5 | | 31 | 5 | | 32 | 6 | | 33 | 10 | | 34 | 5 | | 35 | 4 | | 36 | 3 | | 37 | 2 | | 38 | 4 | | 39 | 4 | | 40 | 3 | | 41 | 2 | | 42 | 5 | | 43 | 6 | | 44 | 6 | | 45 | 4 | | 46 | 2 | | 47 | 3 | | 48 | 5 | | 49 | 4 |
| |
| 32.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 81 | | diversityRatio | 0.13495575221238937 | | totalSentences | 452 | | uniqueOpeners | 61 | |
| 59.07% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 395 | | matches | | 0 | "Then why are you here?" | | 1 | "Then why are you coming?" | | 2 | "Then let them." | | 3 | "Then prove it." | | 4 | "Then why am I here?" | | 5 | "Just a little." | | 6 | "Just a little." |
| | ratio | 0.018 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 222 | | totalSentences | 395 | | matches | | 0 | "It cut through the low" | | 1 | "She rubbed the sleep from" | | 2 | "She pulled her robe tight" | | 3 | "She peered through the peephole." | | 4 | "He wore a charcoal suit" | | 5 | "He leaned on his ivory" | | 6 | "She opened the door." | | 7 | "He shook the rain from" | | 8 | "He closed the door behind" | | 9 | "You look terrible." | | 10 | "Her left wrist ached, the" | | 11 | "She kept her hands hidden" | | 12 | "He set the cane against" | | 13 | "He stopped in the center" | | 14 | "His eyes scanned the clutter" | | 15 | "I didn't come for pleasantries." | | 16 | "She watched his hands." | | 17 | "They were steady." | | 18 | "I need a favor." | | 19 | "You don't do favors." |
| | ratio | 0.562 | |
| 0.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 363 | | totalSentences | 395 | | matches | | 0 | "The knock came at three" | | 1 | "It cut through the low" | | 2 | "Aurora sat up, the sheets" | | 3 | "She rubbed the sleep from" | | 4 | "The cold air bit at" | | 5 | "She pulled her robe tight" | | 6 | "She peered through the peephole." | | 7 | "The hallway light flickered, casting" | | 8 | "He wore a charcoal suit" | | 9 | "Rain slicked his platinum hair" | | 10 | "He leaned on his ivory" | | 11 | "Aurora unlocked the deadbolt." | | 12 | "She opened the door." | | 13 | "Lucien stepped inside without waiting" | | 14 | "He shook the rain from" | | 15 | "The scent of ozone and" | | 16 | "He closed the door behind" | | 17 | "The lock clicked." | | 18 | "You look terrible." | | 19 | "Rory crossed her arms." |
| | ratio | 0.919 | |
| 63.29% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 5 | | totalSentences | 395 | | matches | | 0 | "Now they are close." | | 1 | "Because you have the key." | | 2 | "If we go, we do" | | 3 | "To the warehouse on the" | | 4 | "Because I need to know" |
| | ratio | 0.013 | |
| 95.24% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 15 | | technicalSentenceCount | 1 | | matches | | 0 | "It cut through the low hum of the bar below, a rhythmic pounding that vibrated through the floorboards of the flat." |
| |
| 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 | |