| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 95.45% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1099 | | 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) | |
| 59.05% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1099 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "weight" | | 1 | "familiar" | | 2 | "glint" | | 3 | "gloom" | | 4 | "silence" | | 5 | "pulsed" | | 6 | "rhythmic" | | 7 | "scanned" | | 8 | "depths" |
| |
| 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 | 191 | | matches | (empty) | |
| 75.54% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 8 | | hedgeCount | 1 | | narrationSentences | 191 | | filterMatches | | 0 | "watch" | | 1 | "know" | | 2 | "know see" | | 3 | "see" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 191 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 20 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1099 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 81.76% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 1099 | | uniqueNames | 12 | | maxNameDensity | 1.36 | | worstName | "Tomás" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Tomás" | | discoveredNames | | Camden | 2 | | Quinn | 1 | | Christopher | 1 | | Harlow | 13 | | Detective | 1 | | Tomás | 15 | | Morris | 4 | | Veil | 1 | | Market | 1 | | Nest | 1 | | You | 8 | | Don | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Christopher" | | 2 | "Harlow" | | 3 | "Tomás" | | 4 | "Morris" | | 5 | "Market" | | 6 | "You" |
| | places | (empty) | | globalScore | 0.818 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | 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 | 1099 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 191 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 77 | | mean | 14.27 | | std | 13.63 | | cv | 0.955 | | sampleLengths | | 0 | 70 | | 1 | 18 | | 2 | 31 | | 3 | 20 | | 4 | 1 | | 5 | 12 | | 6 | 32 | | 7 | 20 | | 8 | 13 | | 9 | 3 | | 10 | 5 | | 11 | 20 | | 12 | 9 | | 13 | 18 | | 14 | 10 | | 15 | 41 | | 16 | 4 | | 17 | 24 | | 18 | 7 | | 19 | 8 | | 20 | 14 | | 21 | 11 | | 22 | 20 | | 23 | 6 | | 24 | 5 | | 25 | 27 | | 26 | 8 | | 27 | 43 | | 28 | 9 | | 29 | 10 | | 30 | 34 | | 31 | 7 | | 32 | 6 | | 33 | 7 | | 34 | 4 | | 35 | 6 | | 36 | 8 | | 37 | 59 | | 38 | 3 | | 39 | 8 | | 40 | 15 | | 41 | 7 | | 42 | 4 | | 43 | 2 | | 44 | 6 | | 45 | 27 | | 46 | 27 | | 47 | 4 | | 48 | 4 | | 49 | 19 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 191 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 197 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 191 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1101 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.019981834695731154 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004541326067211626 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 191 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 191 | | mean | 5.75 | | std | 3.13 | | cv | 0.544 | | sampleLengths | | 0 | 14 | | 1 | 8 | | 2 | 14 | | 3 | 13 | | 4 | 4 | | 5 | 17 | | 6 | 4 | | 7 | 3 | | 8 | 10 | | 9 | 1 | | 10 | 6 | | 11 | 5 | | 12 | 20 | | 13 | 10 | | 14 | 10 | | 15 | 1 | | 16 | 5 | | 17 | 7 | | 18 | 7 | | 19 | 4 | | 20 | 13 | | 21 | 8 | | 22 | 6 | | 23 | 8 | | 24 | 3 | | 25 | 3 | | 26 | 5 | | 27 | 8 | | 28 | 3 | | 29 | 5 | | 30 | 11 | | 31 | 9 | | 32 | 5 | | 33 | 4 | | 34 | 8 | | 35 | 10 | | 36 | 3 | | 37 | 7 | | 38 | 5 | | 39 | 8 | | 40 | 3 | | 41 | 7 | | 42 | 18 | | 43 | 4 | | 44 | 3 | | 45 | 5 | | 46 | 8 | | 47 | 1 | | 48 | 7 | | 49 | 2 |
| |
| 40.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.25654450261780104 | | totalSentences | 191 | | uniqueOpeners | 49 | |
| 57.47% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 174 | | matches | | 0 | "Dark curly hair." | | 1 | "Then give them to me." | | 2 | "Then she looked into the" |
| | ratio | 0.017 | |
| 45.29% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 76 | | totalSentences | 174 | | matches | | 0 | "Her soaked wool coat dragged" | | 1 | "She ignored the burn." | | 2 | "Her boots slapped against puddles." | | 3 | "Her voice cut through the" | | 4 | "He didn't turn around." | | 5 | "He lifted a wrist, revealing" | | 6 | "You are dead last, Harlow" | | 7 | "She saw the mark on" | | 8 | "He spun on his heel." | | 9 | "You lost, Detective." | | 10 | "Your partner is dead, Tomás." | | 11 | "You know the rules." | | 12 | "I know a crime when" | | 13 | "I see the clique paying" | | 14 | "I save lives." | | 15 | "She had followed the dossier" | | 16 | "He didn't answer." | | 17 | "He reached into the pocket." | | 18 | "His fingers closed around something" | | 19 | "She saw it." |
| | ratio | 0.437 | |
| 37.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 147 | | totalSentences | 174 | | matches | | 0 | "Rain fell like nails driven" | | 1 | "Harlow Quinn drove her body" | | 2 | "Her soaked wool coat dragged" | | 3 | "Rainwater ran down the sharp" | | 4 | "She ignored the burn." | | 5 | "The leather strap of her" | | 6 | "A shape darted ahead." | | 7 | "Olive skin fading into the" | | 8 | "Harlow broke into a run" | | 9 | "Her boots slapped against puddles." | | 10 | "The suspect turned into a" | | 11 | "Harlow rounded the corner, breath" | | 12 | "The word came out ragged." | | 13 | "Her voice cut through the" | | 14 | "Tomás stood beneath a dripping" | | 15 | "He didn't turn around." | | 16 | "He lifted a wrist, revealing" | | 17 | "A saint Christopher medallion swung" | | 18 | "You are dead last, Harlow" | | 19 | "She saw the mark on" |
| | ratio | 0.845 | |
| 28.74% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 174 | | matches | | | ratio | 0.006 | |
| 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 | |