| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 76.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 217 | | totalAiIsmAdverbs | 1 | | 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) | |
| 53.92% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 217 | | 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 | 22 | | matches | (empty) | |
| 77.92% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 22 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 22 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 211 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 55.21% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 11 | | wordCount | 211 | | uniqueNames | 7 | | maxNameDensity | 1.9 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 4 | | Morris | 2 | | Victorian-era | 1 | | Underground | 1 | | Tube | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" |
| | places | (empty) | | globalScore | 0.552 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 15 | | 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 | 211 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 22 | | matches | (empty) | |
| 92.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 8 | | mean | 26.38 | | std | 12.45 | | cv | 0.472 | | sampleLengths | | |
| 89.31% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 22 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 33 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 22 | | ratio | 0.091 | | matches | | 0 | "Her target—a wiry figure in a dark hoodie—darted between parked cars and street lamps, always just three steps ahead." | | 1 | "But something burning inside her—grief, curiosity, a need to understand Morris's mysterious death—pushed her forward." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 217 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.027649769585253458 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.013824884792626729 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 22 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 22 | | mean | 9.59 | | std | 5.17 | | cv | 0.539 | | sampleLengths | | 0 | 16 | | 1 | 19 | | 2 | 7 | | 3 | 13 | | 4 | 13 | | 5 | 15 | | 6 | 14 | | 7 | 12 | | 8 | 6 | | 9 | 10 | | 10 | 17 | | 11 | 4 | | 12 | 4 | | 13 | 3 | | 14 | 6 | | 15 | 7 | | 16 | 14 | | 17 | 15 | | 18 | 5 | | 19 | 5 | | 20 | 4 | | 21 | 2 |
| |
| 83.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.5 | | totalSentences | 22 | | uniqueOpeners | 11 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 21 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 21 | | matches | | 0 | "Her target—a wiry figure in" | | 1 | "She'd been tracking this suspect" | | 2 | "Her lost partner Morris's voice" | | 3 | "Her military-trained legs ate up" | | 4 | "She knew the rumors." | | 5 | "Her hand instinctively brushed her" |
| | ratio | 0.286 | |
| 7.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 19 | | totalSentences | 21 | | matches | | 0 | "The rain pelted Detective Harlow" | | 1 | "Her target—a wiry figure in" | | 2 | "She'd been tracking this suspect" | | 3 | "Something about the recent string" | | 4 | "Her lost partner Morris's voice" | | 5 | "The suspect suddenly veered left," | | 6 | "Quinn's worn leather watch caught" | | 7 | "Her military-trained legs ate up" | | 8 | "A rusted metal door slammed" | | 9 | "Quinn heard the distinct sound" | | 10 | "The alley opened into a" | | 11 | "Camden's abandoned Tube station." | | 12 | "She knew the rumors." | | 13 | "The underground market." | | 14 | "A place where impossible things" | | 15 | "Her hand instinctively brushed her" | | 16 | "The heavy door stood ajar." | | 17 | "An invitation or a trap." | | 18 | "Quinn took a breath." |
| | ratio | 0.905 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 21 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 11 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |