| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.13 | | leniency | 0.261 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 91.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1201 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 91.67% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1201 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 75 | | matches | (empty) | |
| 85.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 75 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1201 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 47.80% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 636 | | uniqueNames | 8 | | maxNameDensity | 2.04 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Caldwell" | | discoveredNames | | Quinn | 13 | | Caldwell | 9 | | Kowalski | 1 | | Eva | 10 | | Morris | 1 | | Tube | 1 | | Jonathan | 1 | | Ashworth | 2 |
| | persons | | 0 | "Quinn" | | 1 | "Caldwell" | | 2 | "Kowalski" | | 3 | "Eva" | | 4 | "Morris" | | 5 | "Jonathan" |
| | places | | | globalScore | 0.478 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 53 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like he was sleeping" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1201 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 21.07 | | std | 15.89 | | cv | 0.754 | | sampleLengths | | 0 | 41 | | 1 | 3 | | 2 | 23 | | 3 | 3 | | 4 | 20 | | 5 | 35 | | 6 | 30 | | 7 | 4 | | 8 | 24 | | 9 | 28 | | 10 | 5 | | 11 | 18 | | 12 | 46 | | 13 | 2 | | 14 | 33 | | 15 | 28 | | 16 | 4 | | 17 | 15 | | 18 | 35 | | 19 | 3 | | 20 | 49 | | 21 | 16 | | 22 | 2 | | 23 | 30 | | 24 | 44 | | 25 | 3 | | 26 | 6 | | 27 | 3 | | 28 | 44 | | 29 | 4 | | 30 | 5 | | 31 | 45 | | 32 | 11 | | 33 | 38 | | 34 | 50 | | 35 | 17 | | 36 | 5 | | 37 | 28 | | 38 | 34 | | 39 | 14 | | 40 | 15 | | 41 | 22 | | 42 | 7 | | 43 | 18 | | 44 | 36 | | 45 | 5 | | 46 | 34 | | 47 | 9 | | 48 | 65 | | 49 | 8 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 75 | | matches | (empty) | |
| 82.01% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 113 | | matches | | 0 | "was trying" | | 1 | "was sleeping" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 115 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 639 | | adjectiveStacks | 1 | | stackExamples | | 0 | "worn leather-bound book" |
| | adverbCount | 18 | | adverbRatio | 0.028169014084507043 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.010954616588419406 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 10.44 | | std | 8.84 | | cv | 0.846 | | sampleLengths | | 0 | 9 | | 1 | 13 | | 2 | 3 | | 3 | 3 | | 4 | 13 | | 5 | 3 | | 6 | 17 | | 7 | 6 | | 8 | 3 | | 9 | 20 | | 10 | 12 | | 11 | 4 | | 12 | 19 | | 13 | 18 | | 14 | 12 | | 15 | 4 | | 16 | 4 | | 17 | 20 | | 18 | 7 | | 19 | 16 | | 20 | 5 | | 21 | 5 | | 22 | 10 | | 23 | 8 | | 24 | 7 | | 25 | 17 | | 26 | 6 | | 27 | 14 | | 28 | 1 | | 29 | 1 | | 30 | 2 | | 31 | 11 | | 32 | 22 | | 33 | 8 | | 34 | 9 | | 35 | 3 | | 36 | 8 | | 37 | 4 | | 38 | 15 | | 39 | 7 | | 40 | 2 | | 41 | 3 | | 42 | 8 | | 43 | 15 | | 44 | 3 | | 45 | 10 | | 46 | 26 | | 47 | 3 | | 48 | 10 | | 49 | 16 |
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| 79.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4956521739130435 | | totalSentences | 115 | | uniqueOpeners | 57 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 69 | | matches | | 0 | "Her watch caught the light" | | 1 | "Her red curls caught the" | | 2 | "His face held an expression" | | 3 | "She reached down and examined" | | 4 | "Her torch caught something in" | | 5 | "He glanced over." | | 6 | "She'd seen corpses in the" | | 7 | "They landed wrong, limbs at" | | 8 | "She pulled out her own" | | 9 | "It pointed directly at Jonathan" | | 10 | "She leaned close, squinting through" | | 11 | "She crouched again beside Ashworth" | | 12 | "Her fingers hovered over the" | | 13 | "She buttoned the coat back" | | 14 | "She tucked her hair behind" |
| | ratio | 0.217 | |
| 32.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 69 | | matches | | 0 | "The stairs down smelled of" | | 1 | "Harlow Quinn clicked her torch" | | 2 | "Victorian, maybe older." | | 3 | "A faded sign hung crooked" | | 4 | "Sergeant Caldwell stood at the" | | 5 | "Something he was trying to" | | 6 | "Quinn descended the last few" | | 7 | "The air grew colder." | | 8 | "Her watch caught the light" | | 9 | "Eva Kowalski was already there," | | 10 | "Her red curls caught the" | | 11 | "Caldwell shifted his weight." | | 12 | "Quinn stopped a few feet" | | 13 | "The younger woman had her" | | 14 | "Eva tucked a curl behind" | | 15 | "The body lay supine on" | | 16 | "His face held an expression" | | 17 | "Caldwell flipped a page on" | | 18 | "Quinn crouched at the edge" | | 19 | "The rails beneath the body" |
| | ratio | 0.855 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 1 | | matches | | 0 | "Male, mid-thirties by the look of him, dressed in clothes that cost more than Quinn's monthly rent." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |