| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 59 | | tagDensity | 0.186 | | leniency | 0.373 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 93.97% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 829 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 69.84% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 829 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "traced" | | 1 | "measured" | | 2 | "perfect" | | 3 | "silk" | | 4 | "echoing" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 44 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 44 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 829 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 40.51% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 411 | | uniqueNames | 9 | | maxNameDensity | 2.19 | | worstName | "Marcus" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Marcus" | | discoveredNames | | Aurora | 5 | | Raven | 1 | | Nest | 1 | | Marcus | 9 | | Elliott | 2 | | Bank | 1 | | Investment | 1 | | Director | 1 | | Silas | 4 |
| | persons | | 0 | "Aurora" | | 1 | "Raven" | | 2 | "Marcus" | | 3 | "Elliott" | | 4 | "Silas" |
| | places | (empty) | | globalScore | 0.405 | | windowScore | 0.5 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 30 | | glossingSentenceCount | 2 | | matches | | 0 | "tasted like copper on her tongue" | | 1 | "as if wanting to say more" |
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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 | 829 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 92 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 13.82 | | std | 11.15 | | cv | 0.807 | | sampleLengths | | 0 | 51 | | 1 | 25 | | 2 | 4 | | 3 | 18 | | 4 | 53 | | 5 | 5 | | 6 | 3 | | 7 | 12 | | 8 | 9 | | 9 | 19 | | 10 | 3 | | 11 | 31 | | 12 | 6 | | 13 | 25 | | 14 | 11 | | 15 | 12 | | 16 | 10 | | 17 | 14 | | 18 | 20 | | 19 | 19 | | 20 | 12 | | 21 | 6 | | 22 | 18 | | 23 | 12 | | 24 | 3 | | 25 | 6 | | 26 | 38 | | 27 | 6 | | 28 | 1 | | 29 | 12 | | 30 | 14 | | 31 | 5 | | 32 | 16 | | 33 | 1 | | 34 | 10 | | 35 | 10 | | 36 | 7 | | 37 | 25 | | 38 | 8 | | 39 | 19 | | 40 | 4 | | 41 | 9 | | 42 | 6 | | 43 | 10 | | 44 | 17 | | 45 | 3 | | 46 | 9 | | 47 | 4 | | 48 | 21 | | 49 | 4 |
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| 97.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 44 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 77 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 92 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 411 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 11 | | adverbRatio | 0.0267639902676399 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.009732360097323601 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 9.01 | | std | 6.04 | | cv | 0.67 | | sampleLengths | | 0 | 15 | | 1 | 7 | | 2 | 29 | | 3 | 25 | | 4 | 4 | | 5 | 11 | | 6 | 7 | | 7 | 19 | | 8 | 5 | | 9 | 12 | | 10 | 17 | | 11 | 5 | | 12 | 3 | | 13 | 8 | | 14 | 3 | | 15 | 1 | | 16 | 7 | | 17 | 2 | | 18 | 16 | | 19 | 3 | | 20 | 3 | | 21 | 17 | | 22 | 5 | | 23 | 9 | | 24 | 6 | | 25 | 25 | | 26 | 11 | | 27 | 12 | | 28 | 10 | | 29 | 14 | | 30 | 4 | | 31 | 16 | | 32 | 13 | | 33 | 6 | | 34 | 12 | | 35 | 6 | | 36 | 8 | | 37 | 10 | | 38 | 12 | | 39 | 3 | | 40 | 6 | | 41 | 11 | | 42 | 27 | | 43 | 6 | | 44 | 1 | | 45 | 12 | | 46 | 14 | | 47 | 2 | | 48 | 3 | | 49 | 16 |
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| 84.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.532608695652174 | | totalSentences | 92 | | uniqueOpeners | 49 | |
| 85.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 39 | | matches | | 0 | "Finally, he placed it on" |
| | ratio | 0.026 | |
| 76.41% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 39 | | matches | | 0 | "She blinked, certain her mind" | | 1 | "He studied her face" | | 2 | "His hair, once a rebellious" | | 3 | "His eyes widened." | | 4 | "She forced her hands to" | | 5 | "He crossed the room, each" | | 6 | "He laughed, but it wasn't" | | 7 | "His perfect smile cracked, just" | | 8 | "He caught himself, straightened his" | | 9 | "He stood, reached for his" | | 10 | "He smoothed his jacket" | | 11 | "He turned to leave, then" | | 12 | "He walked away, each step" | | 13 | "she said, tearing the card" |
| | ratio | 0.359 | |
| 11.28% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 35 | | totalSentences | 39 | | matches | | 0 | "The glass slipped from Aurora's" | | 1 | "She blinked, certain her mind" | | 2 | "Silas appeared with a dustpan," | | 3 | "He studied her face" | | 4 | "Aurora's fingers traced the crescent" | | 5 | "Marcus hadn't noticed her yet." | | 6 | "His hair, once a rebellious" | | 7 | "The ratty band shirts replaced" | | 8 | "Marcus turned, amber liquid sloshing" | | 9 | "His eyes widened." | | 10 | "She forced her hands to" | | 11 | "He crossed the room, each" | | 12 | "He laughed, but it wasn't" | | 13 | "This laugh was polished, practiced." | | 14 | "Silas materialized with a fresh" | | 15 | "Marcus swirled his drink." | | 16 | "Aurora studied the man across" | | 17 | "His perfect smile cracked, just" | | 18 | "The words hung between them" | | 19 | "Marcus loosened his silk tie," |
| | ratio | 0.897 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 39 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 14 | | technicalSentenceCount | 2 | | matches | | 0 | "But there he stood, Marcus Elliott, her best friend from sixth form, ordering a pint at the bar as if the past seven years hadn't carved themselves between them…" | | 1 | "Marcus loosened his silk tie, and for a moment, she caught a glimpse of the boy who'd helped her spray paint anti-war slogans on their headmaster's car." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 59 | | tagDensity | 0.068 | | leniency | 0.136 | | rawRatio | 0 | | effectiveRatio | 0 | |