| 85.71% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 2 | | adverbTags | | 0 | "Aurora replied curtly [curtly]" | | 1 | "She spat back [back]" |
| | dialogueSentences | 35 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.143 | | effectiveRatio | 0.114 | |
| 88.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1266 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "coolly" | | 1 | "suddenly" | | 2 | "coldly" |
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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) | |
| 60.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1266 | | totalAiIsms | 10 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | word | "down her spine" | | count | 1 |
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| | highlights | | 0 | "thundered" | | 1 | "pounding" | | 2 | "pulse" | | 3 | "footsteps" | | 4 | "glinting" | | 5 | "flickered" | | 6 | "imposing" | | 7 | "eyebrow" | | 8 | "calculating" | | 9 | "down her spine" |
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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 | 58 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 58 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 78 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 68 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 17 | | totalWords | 1268 | | ratio | 0.013 | | matches | | 0 | "That's when things got out of hand in Pakistan. That's why you're in this mess now." | | 1 | "that" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 76.58% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 681 | | uniqueNames | 10 | | maxNameDensity | 1.47 | | worstName | "Aurora" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 10 | | Eva | 4 | | Chen | 1 | | Golden | 1 | | Empress | 1 | | Sergei | 6 | | Authoritative | 1 | | Evan | 2 | | Sheriff | 1 | | Mrs | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Eva" | | 2 | "Chen" | | 3 | "Sergei" | | 4 | "Evan" | | 5 | "Mrs" |
| | places | (empty) | | globalScore | 0.766 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | 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 | 1268 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 78 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 35 | | mean | 36.23 | | std | 22.32 | | cv | 0.616 | | sampleLengths | | 0 | 56 | | 1 | 44 | | 2 | 57 | | 3 | 43 | | 4 | 64 | | 5 | 16 | | 6 | 10 | | 7 | 7 | | 8 | 19 | | 9 | 7 | | 10 | 31 | | 11 | 23 | | 12 | 49 | | 13 | 6 | | 14 | 22 | | 15 | 85 | | 16 | 34 | | 17 | 68 | | 18 | 69 | | 19 | 13 | | 20 | 5 | | 21 | 68 | | 22 | 11 | | 23 | 23 | | 24 | 17 | | 25 | 33 | | 26 | 45 | | 27 | 5 | | 28 | 25 | | 29 | 53 | | 30 | 45 | | 31 | 47 | | 32 | 51 | | 33 | 66 | | 34 | 51 |
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| 99.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 58 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 115 | | matches | | |
| 69.60% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 78 | | ratio | 0.026 | | matches | | 0 | "She took in her former shock at seeing Eva again - the shock, the recognition, the dread - barely sparing attention to the man Isեthe still on the ground before them, his face twisted in a final mask of terror." | | 1 | "\"I wasn't talking about being a mother anymore using you, Rory. I was talking about taking you out of this game. Out of this life. But you don't no how. Do you even want to know how?\" Better, a question; there have been some other ones." |
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| 95.05% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 679 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.045655375552282766 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.01914580265095729 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 78 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 78 | | mean | 16.26 | | std | 13.17 | | cv | 0.81 | | sampleLengths | | 0 | 11 | | 1 | 21 | | 2 | 24 | | 3 | 23 | | 4 | 21 | | 5 | 11 | | 6 | 9 | | 7 | 21 | | 8 | 16 | | 9 | 3 | | 10 | 40 | | 11 | 24 | | 12 | 16 | | 13 | 10 | | 14 | 14 | | 15 | 6 | | 16 | 10 | | 17 | 3 | | 18 | 7 | | 19 | 4 | | 20 | 3 | | 21 | 9 | | 22 | 10 | | 23 | 7 | | 24 | 6 | | 25 | 25 | | 26 | 23 | | 27 | 12 | | 28 | 20 | | 29 | 17 | | 30 | 1 | | 31 | 5 | | 32 | 19 | | 33 | 3 | | 34 | 10 | | 35 | 9 | | 36 | 55 | | 37 | 11 | | 38 | 34 | | 39 | 68 | | 40 | 2 | | 41 | 3 | | 42 | 5 | | 43 | 33 | | 44 | 16 | | 45 | 10 | | 46 | 13 | | 47 | 5 | | 48 | 46 | | 49 | 22 |
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| 67.95% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.46153846153846156 | | totalSentences | 78 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 53 | | matches | | 0 | "Only an hour ago, Aurora" | | 1 | "Then Sergei had emerged to" |
| | ratio | 0.038 | |
| 84.15% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 53 | | matches | | 0 | "She was in an alley" | | 1 | "She took in her former" | | 2 | "She'd heard the skitter of" | | 3 | "He'd stepped out from the" | | 4 | "he'd said coolly, almost amused" | | 5 | "she whispered now" | | 6 | "His eyes flickered to Aurora" | | 7 | "It was a tone that" | | 8 | "He straightened, looking her up" | | 9 | "Him in the cursed, boutique" | | 10 | "He raised a cocked eyebrow." | | 11 | "He stepped closer" | | 12 | "It was a tone that" | | 13 | "She spat back, hating the" | | 14 | "She held onto her anger," | | 15 | "She knew he was right-" | | 16 | "She would expose the truth," | | 17 | "She owed it to Mrs." |
| | ratio | 0.34 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 53 | | matches | | 0 | "Sweat beaded her skin, mingling" | | 1 | "Aurora's heart thundered against her" | | 2 | "The shoe that had housed" | | 3 | "The voice cut through the" | | 4 | "Aurora looked up, taking in" | | 5 | "She was in an alley" | | 6 | "She took in her former" | | 7 | "She'd heard the skitter of" | | 8 | "A man whose surname she'd" | | 9 | "He'd stepped out from the" | | 10 | "he'd said coolly, almost amused" | | 11 | "A weak chuckle." | | 12 | "she whispered now" | | 13 | "Eva clarified with a sharp" | | 14 | "Aurora swallowed hard, her throat" | | 15 | "Silas said, calmly" | | 16 | "His eyes flickered to Aurora" | | 17 | "Example, confident, he always was." | | 18 | "Aurora replied curtly, snatching a" | | 19 | "Sergei leaned closer, darkly and" |
| | ratio | 0.717 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 53 | | matches | | 0 | "Now, Sergei stood in front" | | 1 | "Even if it meant confronting" |
| | ratio | 0.038 | |
| 28.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 4 | | matches | | 0 | "The shoe that had housed this foot moments ago lay upturned nearby, a crimson pool darkening the sole like wine spilled on a doily." | | 1 | "Three weeks ago, Aurora believed she'd left violence behind, along with her childhood street demons and the man who'd once embodied them both." | | 2 | "Aurora had waited tables at The Sheriff, trying to meet the sketchy bastards Sergei had sent her letters promising to make all her doubts easier, if only she wo…" | | 3 | "Sergei threw his head back and laughed, a cold, calculating sound that sent chills down her spine." |
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| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 6 | | fancyTags | | 0 | "Aurora gasped (gasp)" | | 1 | "he'd (would)" | | 2 | "she whispered (whisper)" | | 3 | "Eva clarified (clarify)" | | 4 | "Eva murmured (murmur)" | | 5 | "She spat back (spit)" |
| | dialogueSentences | 35 | | tagDensity | 0.257 | | leniency | 0.514 | | rawRatio | 0.667 | | effectiveRatio | 0.343 | |