| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 68.10% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 627 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "gently" | | 1 | "carefully" | | 2 | "suddenly" | | 3 | "quickly" |
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| 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) | |
| 76.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 627 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "silence" | | 1 | "flickered" | | 2 | "footsteps" |
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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 | 1 | | narrationSentences | 46 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 46 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 54 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 633 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 504 | | uniqueNames | 13 | | maxNameDensity | 0.99 | | worstName | "Eva" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Eva | 5 | | Ptolemy | 1 | | Taylor | 1 | | Watts | 1 | | Aurora | 5 | | Rory | 3 | | Pat | 1 | | Patricia | 1 | | Silas | 1 | | Together | 1 | | Brick | 1 | | Lane | 1 | | Evan | 1 |
| | persons | | 0 | "Eva" | | 1 | "Ptolemy" | | 2 | "Taylor" | | 3 | "Aurora" | | 4 | "Rory" | | 5 | "Pat" | | 6 | "Patricia" | | 7 | "Silas" | | 8 | "Evan" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | 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 | 633 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 54 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 33.32 | | std | 19.84 | | cv | 0.595 | | sampleLengths | | 0 | 33 | | 1 | 6 | | 2 | 13 | | 3 | 54 | | 4 | 25 | | 5 | 41 | | 6 | 34 | | 7 | 5 | | 8 | 31 | | 9 | 59 | | 10 | 19 | | 11 | 29 | | 12 | 89 | | 13 | 51 | | 14 | 27 | | 15 | 46 | | 16 | 27 | | 17 | 13 | | 18 | 31 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 46 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 97 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 506 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.03359683794466403 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.009881422924901186 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 54 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 54 | | mean | 11.72 | | std | 7.17 | | cv | 0.612 | | sampleLengths | | 0 | 10 | | 1 | 23 | | 2 | 6 | | 3 | 13 | | 4 | 9 | | 5 | 32 | | 6 | 11 | | 7 | 2 | | 8 | 9 | | 9 | 16 | | 10 | 16 | | 11 | 25 | | 12 | 4 | | 13 | 16 | | 14 | 14 | | 15 | 5 | | 16 | 15 | | 17 | 8 | | 18 | 8 | | 19 | 4 | | 20 | 7 | | 21 | 9 | | 22 | 12 | | 23 | 3 | | 24 | 8 | | 25 | 16 | | 26 | 6 | | 27 | 13 | | 28 | 7 | | 29 | 12 | | 30 | 10 | | 31 | 8 | | 32 | 26 | | 33 | 10 | | 34 | 13 | | 35 | 15 | | 36 | 17 | | 37 | 24 | | 38 | 7 | | 39 | 1 | | 40 | 12 | | 41 | 7 | | 42 | 19 | | 43 | 8 | | 44 | 36 | | 45 | 10 | | 46 | 12 | | 47 | 5 | | 48 | 10 | | 49 | 10 |
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| 75.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5 | | totalSentences | 54 | | uniqueOpeners | 27 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 43 | | matches | | 0 | "Suddenly, arms hooked around her" | | 1 | "Too quickly as she vomited," |
| | ratio | 0.047 | |
| 24.65% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 43 | | matches | | 0 | "She sputtered in frustration" | | 1 | "Her own roommate's research kept" | | 2 | "I won't get you hurt," | | 3 | "She caught her breath" | | 4 | "Her best friend turned around," | | 5 | "She muffled a laugh, trying" | | 6 | "She threw her bike to" | | 7 | "She flew down the concrete" | | 8 | "She wouldn't cry." | | 9 | "She didn't living with Silas" | | 10 | "Her accounting for rent allowing" | | 11 | "She pinched the bridge of" | | 12 | "Her eyes fell open and" | | 13 | "Her lip bounced up and" | | 14 | "She still couldn't wrap her" | | 15 | "She didn't ask, her instinct" | | 16 | "She heard paws nine-pin down" | | 17 | "Her heart thumped, unable to" | | 18 | "She shook, meeting the heterochromatic" | | 19 | "She looked a tremendous mess." |
| | ratio | 0.488 | |
| 41.40% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 43 | | matches | | 0 | "Aurora flung open the door," | | 1 | "Aurora snapped, keeping her voice" | | 2 | "Eva's hair was wild, vapor" | | 3 | "She sputtered in frustration" | | 4 | "Her own roommate's research kept" | | 5 | "Eva took a deep breath," | | 6 | "Ms.Carter narrowed her eyes." | | 7 | "I won't get you hurt," | | 8 | "She caught her breath" | | 9 | "Her best friend turned around," | | 10 | "She muffled a laugh, trying" | | 11 | "Aurora slammed the door." | | 12 | "She threw her bike to" | | 13 | "She flew down the concrete" | | 14 | "She wouldn't cry." | | 15 | "She didn't living with Silas" | | 16 | "Her accounting for rent allowing" | | 17 | "She pinched the bridge of" | | 18 | "Her eyes fell open and" | | 19 | "Her lip bounced up and" |
| | ratio | 0.837 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 43 | | matches | | 0 | "Even Patricia had better be" |
| | ratio | 0.023 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 1 | | matches | | 0 | "A strong hand she'd recognize anywhere lifted her to a sitting position, the woolly arm coiling around to her future as if every moment since Evan hadn't left t…" |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | 0 | "A yelp left, the rush forcing her off the pavement with an" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 4 | | fancyTags | | 0 | "Aurora snapped (snap)" | | 1 | "She sputtered (sputter)" | | 2 | "She whispered (whisper)" | | 3 | "The bells chimed (chime)" |
| | dialogueSentences | 16 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0.8 | | effectiveRatio | 0.5 | |