| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 4 | | adverbTags | | 0 | "she corrected automatically [automatically]" | | 1 | "he said finally [finally]" | | 2 | "She glanced around [around]" | | 3 | "he said carefully [carefully]" |
| | dialogueSentences | 40 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.2 | | effectiveRatio | 0.2 | |
| 68.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 954 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "slightly" | | 1 | "slowly" | | 2 | "carefully" | | 3 | "really" |
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| 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) | |
| 47.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 954 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "comfortable" | | 1 | "familiar" | | 2 | "traced" | | 3 | "silence" | | 4 | "weight" | | 5 | "crystal" | | 6 | "could feel" | | 7 | "tension" | | 8 | "pulse" |
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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 | 60 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 60 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 79 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 959 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 75.58% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 739 | | uniqueNames | 8 | | maxNameDensity | 1.49 | | worstName | "Aurora" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Thursday | 1 | | Manhattan | 1 | | Aurora | 11 | | London | 2 | | Silas | 7 | | Martin | 5 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Silas" | | 4 | "Martin" |
| | places | | | globalScore | 0.756 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | 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 | 959 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 79 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 26.64 | | std | 16.09 | | cv | 0.604 | | sampleLengths | | 0 | 71 | | 1 | 60 | | 2 | 18 | | 3 | 28 | | 4 | 51 | | 5 | 1 | | 6 | 17 | | 7 | 4 | | 8 | 51 | | 9 | 20 | | 10 | 17 | | 11 | 21 | | 12 | 29 | | 13 | 7 | | 14 | 27 | | 15 | 31 | | 16 | 37 | | 17 | 24 | | 18 | 14 | | 19 | 39 | | 20 | 8 | | 21 | 42 | | 22 | 25 | | 23 | 12 | | 24 | 12 | | 25 | 14 | | 26 | 24 | | 27 | 33 | | 28 | 18 | | 29 | 34 | | 30 | 11 | | 31 | 43 | | 32 | 10 | | 33 | 36 | | 34 | 22 | | 35 | 48 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 60 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 133 | | matches | (empty) | |
| 34.36% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 79 | | ratio | 0.038 | | matches | | 0 | "Through the bar's window, she could see the usual Thursday crowd - sparse enough to breathe, busy enough to blur into comfortable anonymity." | | 1 | "\"Please.\" Aurora's fingers traced the worn edge of the bartop, her eyes drawn to a new photograph on the wall - some foggy London street from decades past." | | 2 | "Nobody had called her that in years - nobody except-" |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 743 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 29 | | adverbRatio | 0.039030955585464336 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.013458950201884253 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 79 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 79 | | mean | 12.14 | | std | 7.63 | | cv | 0.629 | | sampleLengths | | 0 | 16 | | 1 | 19 | | 2 | 13 | | 3 | 23 | | 4 | 21 | | 5 | 16 | | 6 | 23 | | 7 | 18 | | 8 | 28 | | 9 | 11 | | 10 | 21 | | 11 | 19 | | 12 | 1 | | 13 | 7 | | 14 | 10 | | 15 | 4 | | 16 | 9 | | 17 | 14 | | 18 | 28 | | 19 | 13 | | 20 | 7 | | 21 | 6 | | 22 | 11 | | 23 | 6 | | 24 | 15 | | 25 | 14 | | 26 | 10 | | 27 | 5 | | 28 | 4 | | 29 | 3 | | 30 | 14 | | 31 | 2 | | 32 | 10 | | 33 | 1 | | 34 | 12 | | 35 | 19 | | 36 | 17 | | 37 | 12 | | 38 | 8 | | 39 | 9 | | 40 | 15 | | 41 | 10 | | 42 | 4 | | 43 | 4 | | 44 | 35 | | 45 | 8 | | 46 | 11 | | 47 | 22 | | 48 | 9 | | 49 | 3 |
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| 81.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.5063291139240507 | | totalSentences | 79 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 41.43% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 56 | | matches | | 0 | "She pushed through the heavy" | | 1 | "He acknowledged her with a" | | 2 | "he asked as she settled" | | 3 | "His dark hair had started" | | 4 | "he repeated, testing it out" | | 5 | "He gestured to the empty" | | 6 | "She nodded, not trusting her" | | 7 | "she corrected automatically" | | 8 | "He ordered a scotch, neat," | | 9 | "She'd heard through mutual friends," | | 10 | "he said finally, after the" | | 11 | "He trailed off, leaving the" | | 12 | "She took a slow sip" | | 13 | "He cut her off, his" | | 14 | "He swirled his scotch, watching" | | 15 | "she said, watching his reaction" | | 16 | "His eyebrows rose slightly, but" | | 17 | "She glanced around the bar," | | 18 | "He checked it, his face" | | 19 | "He stood, pulling out his" |
| | ratio | 0.446 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 56 | | matches | | 0 | "The green neon sign of" | | 1 | "Aurora hesitated at the bottom" | | 2 | "The evening delivery shift had" | | 3 | "She pushed through the heavy" | | 4 | "Silas stood behind the bar," | | 5 | "He acknowledged her with a" | | 6 | "he asked as she settled" | | 7 | "Aurora's fingers traced the worn" | | 8 | "The glass appeared before her," | | 9 | "Silas limped slightly as he" | | 10 | "Aurora let her mind drift," | | 11 | "The name hit her like" | | 12 | "Nobody had called her that" | | 13 | "Aurora turned slowly, her heart" | | 14 | "Martin stood there, his once-sharp" | | 15 | "His dark hair had started" | | 16 | "The name felt strange on" | | 17 | "he repeated, testing it out" | | 18 | "He gestured to the empty" | | 19 | "She nodded, not trusting her" |
| | ratio | 0.946 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 56 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 1 | | matches | | 0 | "The idealistic boy who'd wanted to fight for environmental causes had always been warring with the one who craved security, success, recognition." |
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| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 2 | | matches | | 0 | "He cut, his voice tight" | | 1 | "Silas called, his voice carrying quiet authority" |
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| 75.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 3 | | fancyTags | | 0 | "he repeated (repeat)" | | 1 | "she corrected automatically (correct)" | | 2 | "Aurora corrected (correct)" |
| | dialogueSentences | 40 | | tagDensity | 0.225 | | leniency | 0.45 | | rawRatio | 0.333 | | effectiveRatio | 0.15 | |