| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.393 | | leniency | 0.786 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 696 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 64.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 696 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "echoing" | | 1 | "dancing" | | 2 | "processed" | | 3 | "tracing" | | 4 | "determined" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 38 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 38 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 55 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 2 | | totalWords | 694 | | ratio | 0.003 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 364 | | uniqueNames | 9 | | maxNameDensity | 3.02 | | worstName | "Eva" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Ford | 1 | | Focus | 1 | | Camden | 1 | | Town | 1 | | Underground | 1 | | Eva | 11 | | Kowalski | 1 | | Quinn | 11 | | Gothic | 1 |
| | persons | | 0 | "Eva" | | 1 | "Kowalski" | | 2 | "Quinn" |
| | places | | 0 | "Focus" | | 1 | "Camden" | | 2 | "Town" | | 3 | "Underground" |
| | globalScore | 0 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 30 | | 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 | 694 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 55 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 38.56 | | std | 25.28 | | cv | 0.656 | | sampleLengths | | 0 | 88 | | 1 | 19 | | 2 | 40 | | 3 | 33 | | 4 | 37 | | 5 | 8 | | 6 | 15 | | 7 | 43 | | 8 | 54 | | 9 | 18 | | 10 | 66 | | 11 | 12 | | 12 | 59 | | 13 | 7 | | 14 | 66 | | 15 | 11 | | 16 | 34 | | 17 | 84 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 38 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 64 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 55 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 366 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, leather-bound journal," |
| | adverbCount | 7 | | adverbRatio | 0.01912568306010929 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.00819672131147541 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 55 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 55 | | mean | 12.62 | | std | 9.57 | | cv | 0.759 | | sampleLengths | | 0 | 17 | | 1 | 16 | | 2 | 11 | | 3 | 12 | | 4 | 10 | | 5 | 22 | | 6 | 6 | | 7 | 13 | | 8 | 13 | | 9 | 9 | | 10 | 6 | | 11 | 12 | | 12 | 8 | | 13 | 25 | | 14 | 11 | | 15 | 23 | | 16 | 3 | | 17 | 5 | | 18 | 3 | | 19 | 2 | | 20 | 13 | | 21 | 4 | | 22 | 24 | | 23 | 13 | | 24 | 2 | | 25 | 3 | | 26 | 11 | | 27 | 22 | | 28 | 18 | | 29 | 6 | | 30 | 12 | | 31 | 6 | | 32 | 41 | | 33 | 19 | | 34 | 6 | | 35 | 6 | | 36 | 7 | | 37 | 25 | | 38 | 8 | | 39 | 19 | | 40 | 3 | | 41 | 4 | | 42 | 8 | | 43 | 40 | | 44 | 18 | | 45 | 5 | | 46 | 6 | | 47 | 9 | | 48 | 18 | | 49 | 7 |
| |
| 86.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5272727272727272 | | totalSentences | 55 | | uniqueOpeners | 29 | |
| 90.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 37 | | matches | | 0 | "Then she looked at Quinn's" |
| | ratio | 0.027 | |
| 79.46% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 37 | | matches | | 0 | "She stepped out, her polished" | | 1 | "She stood, her sharp jawline" | | 2 | "She unzipped the worn leather" | | 3 | "she said, holding up a" | | 4 | "She walked to the platform" | | 5 | "She pulled out her own" | | 6 | "She pointed to a faint," | | 7 | "She held up a small," | | 8 | "It was the same color" | | 9 | "She looked at Eva, her" | | 10 | "She paused at the mouth" | | 11 | "She looked at the overturned" | | 12 | "she began, her voice steady" |
| | ratio | 0.351 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 37 | | matches | | 0 | "DetectiveQuinn parked her unmarked Ford" | | 1 | "The air hung thick with" | | 2 | "She stepped out, her polished" | | 3 | "The flickering emergency lights cast" | | 4 | "A single, overturned metal chair" | | 5 | "Detective Eva Kowalski stood beside" | | 6 | "the constable said, shifting uncomfortably" | | 7 | "Quinn walked past the constable," | | 8 | "Eva followed, her satchel thumping" | | 9 | "Quinn murmured, her voice low" | | 10 | "Eva adjusted her glasses, her" | | 11 | "Quinn crouched beside the chair," | | 12 | "She stood, her sharp jawline" | | 13 | "the constable replied" | | 14 | "Eva hesitated, then nodded." | | 15 | "She unzipped the worn leather" | | 16 | "she said, holding up a" | | 17 | "Quinn's eyes narrowed." | | 18 | "She walked to the platform" | | 19 | "She pulled out her own" |
| | ratio | 0.973 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 37 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 11 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 3 | | matches | | 0 | "Quinn murmured, her voice low" | | 1 | "She looked, her gaze intense" | | 2 | "she began, her voice steady but low," |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn murmured (murmur)" |
| | dialogueSentences | 28 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.25 | | effectiveRatio | 0.071 | |