| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 84.08% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 314 | | 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) | |
| 68.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 314 | | 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 | 18 | | matches | (empty) | |
| 63.49% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 18 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 19 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 314 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 51.96% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 12 | | wordCount | 306 | | uniqueNames | 7 | | maxNameDensity | 1.96 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Harlow | 6 | | Quinn | 1 | | Underground | 1 | | Glock | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Glock" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.52 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 18 | | 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 | 314 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 19 | | matches | (empty) | |
| 98.90% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 9 | | mean | 34.89 | | std | 17.31 | | cv | 0.496 | | sampleLengths | | 0 | 55 | | 1 | 44 | | 2 | 47 | | 3 | 15 | | 4 | 50 | | 5 | 50 | | 6 | 32 | | 7 | 8 | | 8 | 13 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 18 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 51 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 19 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 310 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 1 | | adverbRatio | 0.0032258064516129032 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0032258064516129032 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 19 | | echoCount | 0 | | echoWords | (empty) | |
| 54.44% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 19 | | mean | 16.53 | | std | 4.73 | | cv | 0.286 | | sampleLengths | | 0 | 19 | | 1 | 22 | | 2 | 14 | | 3 | 12 | | 4 | 32 | | 5 | 17 | | 6 | 14 | | 7 | 16 | | 8 | 15 | | 9 | 16 | | 10 | 14 | | 11 | 20 | | 12 | 16 | | 13 | 19 | | 14 | 15 | | 15 | 16 | | 16 | 16 | | 17 | 8 | | 18 | 13 |
| |
| 96.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.5789473684210527 | | totalSentences | 19 | | uniqueOpeners | 11 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 18 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 0 | | totalSentences | 18 | | matches | (empty) | | ratio | 0 | |
| 15.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 16 | | totalSentences | 18 | | matches | | 0 | "Rain slicked the asphalt of" | | 1 | "Detective Harlow Quinn adjusted the" | | 2 | "The runner cut sharply between" | | 3 | "Harlow pushed off the curb," | | 4 | "A worn leather watch on" | | 5 | "Metal railings bit into Harlow's" | | 6 | "The air shifted, heavy with" | | 7 | "A rusted iron gate hung" | | 8 | "Shadows stretched long across tiled" | | 9 | "The runner slipped through a" | | 10 | "Harlow cleared the barrier in" | | 11 | "A crowd milled beneath the" | | 12 | "Figures wrapped in heavy cloaks" | | 13 | "Harlow reached the edge of" | | 14 | "A vendor with reptilian irises" | | 15 | "Harlow kept her gaze fixed" |
| | ratio | 0.889 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 18 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | 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 | |