| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 97.08% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1711 | | totalAiIsmAdverbs | 1 | | 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) | |
| 67.86% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1711 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "familiar" | | 1 | "scanned" | | 2 | "measured" | | 3 | "weight" | | 4 | "traced" | | 5 | "pulsed" | | 6 | "tension" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 118 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 118 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1711 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 46 | | wordCount | 1711 | | uniqueNames | 17 | | maxNameDensity | 0.7 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Carter | 1 | | Blackwood | 1 | | Cardiff | 3 | | Golden | 1 | | Empress | 1 | | Evan | 2 | | Prague | 2 | | London | 2 | | Eva | 1 | | Wales | 1 | | European | 1 | | Rory | 12 | | Silas | 12 | | You | 3 |
| | persons | | 0 | "Nest" | | 1 | "Carter" | | 2 | "Blackwood" | | 3 | "Evan" | | 4 | "Eva" | | 5 | "Rory" | | 6 | "Silas" | | 7 | "You" |
| | places | | 0 | "Raven" | | 1 | "Soho" | | 2 | "Cardiff" | | 3 | "Golden" | | 4 | "Prague" | | 5 | "London" | | 6 | "Wales" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 113 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like an older district of Prague" |
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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 | 1711 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 118 | | matches | | 0 | "reached that section" | | 1 | "traded that path" |
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| 92.05% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 38.89 | | std | 18.36 | | cv | 0.472 | | sampleLengths | | 0 | 137 | | 1 | 82 | | 2 | 34 | | 3 | 22 | | 4 | 27 | | 5 | 26 | | 6 | 40 | | 7 | 20 | | 8 | 30 | | 9 | 27 | | 10 | 25 | | 11 | 27 | | 12 | 25 | | 13 | 31 | | 14 | 28 | | 15 | 38 | | 16 | 47 | | 17 | 42 | | 18 | 33 | | 19 | 30 | | 20 | 40 | | 21 | 43 | | 22 | 39 | | 23 | 33 | | 24 | 27 | | 25 | 30 | | 26 | 45 | | 27 | 36 | | 28 | 54 | | 29 | 36 | | 30 | 40 | | 31 | 44 | | 32 | 34 | | 33 | 43 | | 34 | 46 | | 35 | 38 | | 36 | 34 | | 37 | 29 | | 38 | 55 | | 39 | 46 | | 40 | 35 | | 41 | 33 | | 42 | 47 | | 43 | 33 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 255 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 118 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1718 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 48 | | adverbRatio | 0.027939464493597205 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.004656577415599534 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 14.5 | | std | 6.02 | | cv | 0.415 | | sampleLengths | | 0 | 23 | | 1 | 16 | | 2 | 23 | | 3 | 23 | | 4 | 22 | | 5 | 30 | | 6 | 11 | | 7 | 19 | | 8 | 22 | | 9 | 20 | | 10 | 10 | | 11 | 4 | | 12 | 18 | | 13 | 12 | | 14 | 9 | | 15 | 13 | | 16 | 14 | | 17 | 13 | | 18 | 7 | | 19 | 19 | | 20 | 14 | | 21 | 26 | | 22 | 9 | | 23 | 11 | | 24 | 17 | | 25 | 13 | | 26 | 10 | | 27 | 17 | | 28 | 9 | | 29 | 16 | | 30 | 5 | | 31 | 13 | | 32 | 9 | | 33 | 12 | | 34 | 13 | | 35 | 12 | | 36 | 10 | | 37 | 9 | | 38 | 8 | | 39 | 20 | | 40 | 10 | | 41 | 14 | | 42 | 14 | | 43 | 16 | | 44 | 25 | | 45 | 6 | | 46 | 7 | | 47 | 19 | | 48 | 16 | | 49 | 11 |
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| 36.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.2457627118644068 | | totalSentences | 118 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 118 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 118 | | matches | | 0 | "Her shoulder-length black hair held" | | 1 | "His right hand turned, and" | | 2 | "He glanced up and met" | | 3 | "His hazel eyes stayed level" | | 4 | "You picked a slow night" | | 5 | "She rested one forearm along" | | 6 | "I walk past the entrance" | | 7 | "He poured a measure for" | | 8 | "You kept the arrangement going" | | 9 | "I did not plan for" | | 10 | "Your father still checks in" | | 11 | "Her fingers moved steady, the" | | 12 | "I stayed with the network" | | 13 | "You always said the work" | | 14 | "I figured the switch happened" | | 15 | "He moved to the end" | | 16 | "I kept the flat because" | | 17 | "Your letters stopped arriving after" | | 18 | "I sent one message through" | | 19 | "She reported you had taken" |
| | ratio | 0.212 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 111 | | totalSentences | 118 | | matches | | 0 | "The green neon sign above" | | 1 | "Aurora Carter pulled the brass" | | 2 | "Rory kept her empty delivery" | | 3 | "Her shoulder-length black hair held" | | 4 | "A tall figure moved behind" | | 5 | "The man reached for bottles" | | 6 | "A neatly trimmed beard marked" | | 7 | "His right hand turned, and" | | 8 | "He glanced up and met" | | 9 | "Rory stopped her approach." | | 10 | "Silas Blackwood straightened the single" | | 11 | "His hazel eyes stayed level" | | 12 | "The door shut with a" | | 13 | "The sound of rain against" | | 14 | "Silas reached for a clean" | | 15 | "The motion drew out the" | | 16 | "You picked a slow night" | | 17 | "The place holds the same" | | 18 | "Rory crossed the remaining distance" | | 19 | "She rested one forearm along" |
| | ratio | 0.941 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 118 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 93 | | technicalSentenceCount | 4 | | matches | | 0 | "Her shoulder-length black hair held the mist from the evening air, and the bright blue of her eyes adjusted to the dim bulbs that hung low over the polished woo…" | | 1 | "The childhood accident on the bike had left the mark near the bone, a shape that stayed visible under any light." | | 2 | "I traded that path for orders stacked in plastic bags and bike lanes that change with construction seasons." | | 3 | "The law path suited your father more than it suited the girl who planned escapes from set schedules." |
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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 | |