| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 90.62% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1599 | | totalAiIsmAdverbs | 3 | | 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) | |
| 28.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1599 | | totalAiIsms | 23 | | found | | | highlights | | 0 | "weight" | | 1 | "silence" | | 2 | "warmth" | | 3 | "traced" | | 4 | "fractured" | | 5 | "pulse" | | 6 | "tension" | | 7 | "vibrated" | | 8 | "perfect" | | 9 | "unspoken" | | 10 | "resolved" |
| |
| 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 | 339 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 0 | | narrationSentences | 339 | | filterMatches | | 0 | "look" | | 1 | "think" | | 2 | "know" | | 3 | "see" |
| | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 339 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 15 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1599 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 80 | | wordCount | 1599 | | uniqueNames | 12 | | maxNameDensity | 1 | | worstName | "You" | | maxWindowNameDensity | 3 | | worstWindowName | "You" | | discoveredNames | | Lucien | 10 | | Rory | 11 | | You | 16 | | Pulled | 8 | | Ptolemy | 3 | | Fingers | 6 | | Felt | 6 | | Found | 4 | | Heat | 5 | | Teeth | 4 | | Breath | 4 | | Sucked | 3 |
| | persons | | 0 | "Lucien" | | 1 | "Rory" | | 2 | "You" | | 3 | "Pulled" | | 4 | "Ptolemy" | | 5 | "Fingers" | | 6 | "Heat" | | 7 | "Teeth" | | 8 | "Breath" |
| | places | (empty) | | globalScore | 1 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 117 | | glossingSentenceCount | 1 | | matches | | 0 | "something like ozone and burnt sugar" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1599 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 339 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 75 | | mean | 21.32 | | std | 23.11 | | cv | 1.084 | | sampleLengths | | 0 | 63 | | 1 | 20 | | 2 | 4 | | 3 | 32 | | 4 | 5 | | 5 | 44 | | 6 | 26 | | 7 | 28 | | 8 | 7 | | 9 | 12 | | 10 | 22 | | 11 | 32 | | 12 | 6 | | 13 | 4 | | 14 | 4 | | 15 | 41 | | 16 | 8 | | 17 | 5 | | 18 | 9 | | 19 | 41 | | 20 | 3 | | 21 | 5 | | 22 | 6 | | 23 | 48 | | 24 | 5 | | 25 | 6 | | 26 | 6 | | 27 | 22 | | 28 | 7 | | 29 | 18 | | 30 | 8 | | 31 | 45 | | 32 | 3 | | 33 | 2 | | 34 | 4 | | 35 | 19 | | 36 | 9 | | 37 | 44 | | 38 | 40 | | 39 | 48 | | 40 | 12 | | 41 | 5 | | 42 | 15 | | 43 | 5 | | 44 | 35 | | 45 | 2 | | 46 | 3 | | 47 | 42 | | 48 | 10 | | 49 | 5 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 339 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 348 | | matches | | 0 | "were looking" | | 1 | "were not looking" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 339 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1602 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 46 | | adverbRatio | 0.02871410736579276 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0018726591760299626 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 339 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 339 | | mean | 4.72 | | std | 2.54 | | cv | 0.537 | | sampleLengths | | 0 | 10 | | 1 | 6 | | 2 | 14 | | 3 | 10 | | 4 | 11 | | 5 | 7 | | 6 | 5 | | 7 | 4 | | 8 | 10 | | 9 | 6 | | 10 | 4 | | 11 | 2 | | 12 | 9 | | 13 | 6 | | 14 | 7 | | 15 | 8 | | 16 | 5 | | 17 | 9 | | 18 | 8 | | 19 | 13 | | 20 | 9 | | 21 | 5 | | 22 | 8 | | 23 | 9 | | 24 | 9 | | 25 | 3 | | 26 | 3 | | 27 | 6 | | 28 | 9 | | 29 | 7 | | 30 | 7 | | 31 | 4 | | 32 | 8 | | 33 | 5 | | 34 | 2 | | 35 | 9 | | 36 | 6 | | 37 | 3 | | 38 | 4 | | 39 | 9 | | 40 | 6 | | 41 | 5 | | 42 | 5 | | 43 | 3 | | 44 | 3 | | 45 | 4 | | 46 | 4 | | 47 | 3 | | 48 | 4 | | 49 | 9 |
| |
| 47.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 26 | | diversityRatio | 0.3333333333333333 | | totalSentences | 339 | | uniqueOpeners | 113 | |
| 12.25% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 272 | | matches | | 0 | "Bright blue met heterochromatic dark." |
| | ratio | 0.004 | |
| 61.18% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 108 | | totalSentences | 272 | | matches | | 0 | "He held the ivory-handled cane" | | 1 | "He did not move." | | 2 | "She kept her weight on" | | 3 | "You look like hell." | | 4 | "His gaze tracked past her" | | 5 | "I needed to see you." | | 6 | "She remembered the exact shade" | | 7 | "You show up at one" | | 8 | "You look like you rolled" | | 9 | "He stepped forward." | | 10 | "She did not close the" | | 11 | "He caught the scent of" | | 12 | "His cane tapped once against" | | 13 | "I found what we were" | | 14 | "We are not a we." | | 15 | "You made that clear when" | | 16 | "He shifted his weight." | | 17 | "He reached into his coat" | | 18 | "They had leverage." | | 19 | "I bought time." |
| | ratio | 0.397 | |
| 90.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 201 | | totalSentences | 272 | | matches | | 0 | "The third deadbolt slid back" | | 1 | "Rory stepped into the narrow" | | 2 | "Lucien stood on the landing," | | 3 | "Water beads gathered at the" | | 4 | "He held the ivory-handled cane" | | 5 | "The other swallowed the light." | | 6 | "He did not move." | | 7 | "She kept her weight on" | | 8 | "The chain lock hung between" | | 9 | "You look like hell." | | 10 | "His gaze tracked past her" | | 11 | "Stacks of paper climbed the" | | 12 | "A tabby cat stretched along" | | 13 | "I needed to see you." | | 14 | "Rory’s fingers tightened on the" | | 15 | "The crescent scar on her" | | 16 | "She remembered the exact shade" | | 17 | "You show up at one" | | 18 | "You look like you rolled" | | 19 | "He stepped forward." |
| | ratio | 0.739 | |
| 55.15% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 272 | | matches | | 0 | "Because you lost your leverage." | | 1 | "Because I could not stay" | | 2 | "Because I knew I would" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 13 | | 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 | |