| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "She stepped aside [aside]" |
| | dialogueSentences | 20 | | tagDensity | 0.45 | | leniency | 0.9 | | rawRatio | 0.111 | | effectiveRatio | 0.1 | |
| 76.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1071 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | | 2 | | adverb | "reluctantly" | | count | 1 |
| | 3 | | | 4 | |
| | highlights | | 0 | "quickly" | | 1 | "slowly" | | 2 | "reluctantly" | | 3 | "cautiously" | | 4 | "very" |
| |
| 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) | |
| 25.30% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1071 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "treacherous" | | 1 | "flickered" | | 2 | "scanned" | | 3 | "clandestine" | | 4 | "weight" | | 5 | "echoed" | | 6 | "footsteps" | | 7 | "beacon" | | 8 | "racing" | | 9 | "sinister" | | 10 | "echoing" | | 11 | "raced" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "heart pounded in chest" | | count | 1 |
| | 1 | | label | "blood ran cold" | | count | 1 |
|
| | highlights | | 0 | "heart pounded in her chest" | | 1 | "blood ran cold" |
| |
| 98.40% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 3 | | narrationSentences | 94 | | matches | | 0 | "e with fear" | | 1 | "e with terror" | | 2 | "e with fear" |
| |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 106 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 22 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1069 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 979 | | uniqueNames | 8 | | maxNameDensity | 2.45 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 24 | | Soho | 1 | | Morris | 1 | | Raven | 1 | | Nest | 1 | | Veil | 3 | | Market | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Raven" |
| | places | | | globalScore | 0.274 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | 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 | 1069 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 106 | | matches | (empty) | |
| 96.01% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 34.48 | | std | 16.76 | | cv | 0.486 | | sampleLengths | | 0 | 69 | | 1 | 58 | | 2 | 25 | | 3 | 51 | | 4 | 55 | | 5 | 45 | | 6 | 31 | | 7 | 40 | | 8 | 21 | | 9 | 7 | | 10 | 12 | | 11 | 17 | | 12 | 13 | | 13 | 46 | | 14 | 65 | | 15 | 46 | | 16 | 49 | | 17 | 36 | | 18 | 49 | | 19 | 51 | | 20 | 31 | | 21 | 36 | | 22 | 11 | | 23 | 24 | | 24 | 15 | | 25 | 28 | | 26 | 18 | | 27 | 12 | | 28 | 31 | | 29 | 37 | | 30 | 40 |
| |
| 94.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 94 | | matches | | 0 | "were lined" | | 1 | "was used" | | 2 | "were traded" | | 3 | "were lined" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 174 | | matches | | 0 | "was slipping" | | 1 | "was watching" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 106 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 256 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.03515625 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0078125 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 106 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 106 | | mean | 10.08 | | std | 4.67 | | cv | 0.463 | | sampleLengths | | 0 | 22 | | 1 | 17 | | 2 | 11 | | 3 | 19 | | 4 | 9 | | 5 | 18 | | 6 | 8 | | 7 | 10 | | 8 | 13 | | 9 | 9 | | 10 | 9 | | 11 | 4 | | 12 | 3 | | 13 | 9 | | 14 | 10 | | 15 | 10 | | 16 | 9 | | 17 | 13 | | 18 | 3 | | 19 | 2 | | 20 | 7 | | 21 | 12 | | 22 | 20 | | 23 | 11 | | 24 | 15 | | 25 | 18 | | 26 | 12 | | 27 | 6 | | 28 | 17 | | 29 | 8 | | 30 | 10 | | 31 | 17 | | 32 | 13 | | 33 | 4 | | 34 | 9 | | 35 | 8 | | 36 | 3 | | 37 | 4 | | 38 | 8 | | 39 | 4 | | 40 | 11 | | 41 | 6 | | 42 | 8 | | 43 | 5 | | 44 | 11 | | 45 | 8 | | 46 | 18 | | 47 | 9 | | 48 | 16 | | 49 | 11 |
| |
| 42.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.2830188679245283 | | totalSentences | 106 | | uniqueOpeners | 30 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 91 | | matches | (empty) | | ratio | 0 | |
| 83.74% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 91 | | matches | | 0 | "she shouted, her voice swallowed" | | 1 | "She landed with a splash," | | 2 | "She needed him alive." | | 3 | "She needed answers." | | 4 | "She paused at the top," | | 5 | "She took a deep breath" | | 6 | "She reached the bottom and" | | 7 | "She moved quickly, weaving through" | | 8 | "She reached the bookshelf and" | | 9 | "She pushed the door open" | | 10 | "His face was pale, eyes" | | 11 | "She turned back to the" | | 12 | "She cursed and followed, dropping" | | 13 | "She landed in a narrow" | | 14 | "It had to be." | | 15 | "She had no token, but" | | 16 | "He was slipping through the" | | 17 | "She reached the stall just" | | 18 | "she shouted, but it was" | | 19 | "She had no backup, no" |
| | ratio | 0.341 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 87 | | totalSentences | 91 | | matches | | 0 | "Detective Harlow Quinn's boots pounded" | | 1 | "The suspect darted through the" | | 2 | "Rain lashed down, turning the" | | 3 | "Quinn's worn leather watch, a" | | 4 | "she shouted, her voice swallowed" | | 5 | "The figure glanced back, eyes" | | 6 | "Quinn followed, her military precision" | | 7 | "She landed with a splash," | | 8 | "The suspect was already at" | | 9 | "Quinn's hand went to her" | | 10 | "The suspect was young, barely" | | 11 | "She needed him alive." | | 12 | "She needed answers." | | 13 | "The gate creaked open, and" | | 14 | "Quinn sprinted after him, her" | | 15 | "The gate led to a" | | 16 | "She paused at the top," | | 17 | "The faint glow of a" | | 18 | "Quinn's jaw tightened." | | 19 | "A known haunt for the" |
| | ratio | 0.956 | |
| 54.95% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 91 | | matches | | 0 | "Before he could respond, a" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 1 | | matches | | 0 | "An underground market that moved locations every full moon, accessible only with a bone token." |
| |
| 13.89% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn demanded, her voice steady" | | 1 | "she said, her voice a low purr" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 4 | | fancyTags | | 0 | "she shouted (shout)" | | 1 | "he stammered (stammer)" | | 2 | "Quinn demanded (demand)" | | 3 | "she shouted (shout)" |
| | dialogueSentences | 20 | | tagDensity | 0.35 | | leniency | 0.7 | | rawRatio | 0.571 | | effectiveRatio | 0.4 | |