| 88.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 2 | | adverbTags | | 0 | "He paused then [then]" | | 1 | "he said finally [finally]" |
| | dialogueSentences | 36 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.125 | | effectiveRatio | 0.111 | |
| 83.94% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 934 | | totalAiIsmAdverbs | 3 | | found | | 0 | | adverb | "barely above a whisper" | | count | 3 |
|
| | highlights | | 0 | "barely above a whisper" |
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| 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) | |
| 8.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 934 | | totalAiIsms | 17 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | word | "down her spine" | | count | 1 |
| | 8 | | | 9 | | | 10 | |
| | highlights | | 0 | "symphony" | | 1 | "beacon" | | 2 | "familiar" | | 3 | "scanning" | | 4 | "glinting" | | 5 | "eyebrow" | | 6 | "chill" | | 7 | "down her spine" | | 8 | "whisper" | | 9 | "weight" | | 10 | "unspoken" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "heart pounded in chest" | | count | 1 |
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| | highlights | | 0 | "heart pounded in her chest" |
| |
| 86.54% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 3 | | narrationSentences | 65 | | matches | | 0 | "felt a chill" | | 1 | "felt the sting" | | 2 | "felt a lump" |
| |
| 10.99% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 6 | | narrationSentences | 65 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 85 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 933 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 34.73% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 694 | | uniqueNames | 3 | | maxNameDensity | 2.31 | | worstName | "Silas" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Silas" | | discoveredNames | | | persons | | | places | | | globalScore | 0.347 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 933 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 32.17 | | std | 18.18 | | cv | 0.565 | | sampleLengths | | 0 | 73 | | 1 | 64 | | 2 | 13 | | 3 | 28 | | 4 | 20 | | 5 | 16 | | 6 | 27 | | 7 | 21 | | 8 | 14 | | 9 | 10 | | 10 | 24 | | 11 | 27 | | 12 | 30 | | 13 | 70 | | 14 | 30 | | 15 | 50 | | 16 | 17 | | 17 | 23 | | 18 | 38 | | 19 | 24 | | 20 | 29 | | 21 | 44 | | 22 | 15 | | 23 | 44 | | 24 | 39 | | 25 | 13 | | 26 | 38 | | 27 | 19 | | 28 | 73 |
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| 99.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 65 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 141 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 85 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 695 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.02158273381294964 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.008633093525179856 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 85 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 85 | | mean | 10.98 | | std | 5.37 | | cv | 0.49 | | sampleLengths | | 0 | 23 | | 1 | 20 | | 2 | 21 | | 3 | 9 | | 4 | 15 | | 5 | 17 | | 6 | 16 | | 7 | 16 | | 8 | 8 | | 9 | 5 | | 10 | 12 | | 11 | 16 | | 12 | 13 | | 13 | 4 | | 14 | 3 | | 15 | 4 | | 16 | 12 | | 17 | 11 | | 18 | 16 | | 19 | 12 | | 20 | 8 | | 21 | 1 | | 22 | 9 | | 23 | 3 | | 24 | 2 | | 25 | 8 | | 26 | 2 | | 27 | 7 | | 28 | 17 | | 29 | 10 | | 30 | 11 | | 31 | 6 | | 32 | 14 | | 33 | 4 | | 34 | 12 | | 35 | 8 | | 36 | 10 | | 37 | 14 | | 38 | 14 | | 39 | 12 | | 40 | 12 | | 41 | 12 | | 42 | 6 | | 43 | 12 | | 44 | 14 | | 45 | 14 | | 46 | 22 | | 47 | 10 | | 48 | 7 | | 49 | 10 |
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| 42.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.27058823529411763 | | totalSentences | 85 | | uniqueOpeners | 23 | |
| 55.56% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 60 | | matches | | 0 | "Then, the door creaked open," |
| | ratio | 0.017 | |
| 33.33% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 60 | | matches | | 0 | "He limped towards the bar," | | 1 | "She hadn't seen him in" | | 2 | "he said, his voice a" | | 3 | "She bristled at the old" | | 4 | "she replied, her voice steady" | | 5 | "He leaned against the bar," | | 6 | "He paused, then asked" | | 7 | "She poured him a drink," | | 8 | "she said, sliding the glass" | | 9 | "He took a sip, his" | | 10 | "She knew all too well" | | 11 | "He swirled the whiskey in" | | 12 | "he said finally" | | 13 | "She'd left Cardiff to escape" | | 14 | "She took a deep breath," | | 15 | "she said, her voice barely" | | 16 | "She'd run from her past," | | 17 | "she said, her voice firm" | | 18 | "She'd tried to leave it" | | 19 | "she said, her voice barely" |
| | ratio | 0.467 | |
| 18.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 53 | | totalSentences | 60 | | matches | | 0 | "The bar was a symphony" | | 1 | "The neon sign outside cast" | | 2 | "Rory, wiping down the counter" | | 3 | "Silas stood in the doorway," | | 4 | "He limped towards the bar," | | 5 | "Rory's heart pounded in her" | | 6 | "She hadn't seen him in" | | 7 | "he said, his voice a" | | 8 | "She bristled at the old" | | 9 | "she replied, her voice steady" | | 10 | "He leaned against the bar," | | 11 | "Rory raised an eyebrow." | | 12 | "Silas glanced at his hand," | | 13 | "He paused, then asked" | | 14 | "She poured him a drink," | | 15 | "she said, sliding the glass" | | 16 | "He took a sip, his" | | 17 | "Rory leaned against the bar," | | 18 | "Silas chuckled, a sound like" | | 19 | "Rory nodded, her gaze flicking" |
| | ratio | 0.883 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 60 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 2 | | matches | | 0 | "Rory, wiping down the counter with a cloth that had seen better days, felt the familiar rhythm of the evening's lull." | | 1 | "Rory felt the tears pricking at the corners of her eyes, the weight of her past threatening to crush her." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 8 | | matches | | 0 | "he said, his voice a low rumble" | | 1 | "she replied, her voice steady despite the storm within her" | | 2 | "she said, her voice barely above a whisper" | | 3 | "she said, her voice firm" | | 4 | "she said, her voice barely above a whisper" | | 5 | "she said, her voice breaking" | | 6 | "he said, his voice soft" | | 7 | "she said, her voice barely above a whisper" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 36 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0.067 | | effectiveRatio | 0.056 | |