| 88.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "sign hung crookedly [crookedly]" |
| | dialogueSentences | 18 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0.25 | | effectiveRatio | 0.111 | |
| 92.13% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1271 | | totalAiIsmAdverbs | 2 | | 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) | |
| 37.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1271 | | totalAiIsms | 16 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | word | "down her spine" | | count | 1 |
| | 9 | | | 10 | | | 11 | | | 12 | | | 13 | |
| | highlights | | 0 | "hulking" | | 1 | "streaming" | | 2 | "weight" | | 3 | "echoed" | | 4 | "footsteps" | | 5 | "gloom" | | 6 | "intricate" | | 7 | "chill" | | 8 | "down her spine" | | 9 | "measured" | | 10 | "velvet" | | 11 | "warmth" | | 12 | "flicked" | | 13 | "clenching" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 2 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "eyes widened" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 108 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 108 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 121 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1261 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1090 | | uniqueNames | 15 | | maxNameDensity | 0.73 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Herrera" | | discoveredNames | | London | 2 | | Harlow | 1 | | Quinn | 8 | | Raven | 1 | | Nest | 1 | | Underground | 1 | | Camden | 1 | | Town | 1 | | Veil | 1 | | Market | 2 | | Morris | 5 | | Saint | 1 | | Christopher | 1 | | Herrera | 7 | | Warm | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Nest" | | 3 | "Market" | | 4 | "Morris" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Herrera" |
| | places | | 0 | "London" | | 1 | "Raven" | | 2 | "Camden" | | 3 | "Town" |
| | globalScore | 1 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.793 | | wordCount | 1261 | | matches | | 0 | "not out of deference, but like water around a stone" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 121 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 30.02 | | std | 20.74 | | cv | 0.691 | | sampleLengths | | 0 | 74 | | 1 | 59 | | 2 | 57 | | 3 | 47 | | 4 | 42 | | 5 | 41 | | 6 | 50 | | 7 | 12 | | 8 | 58 | | 9 | 3 | | 10 | 51 | | 11 | 27 | | 12 | 67 | | 13 | 28 | | 14 | 4 | | 15 | 67 | | 16 | 19 | | 17 | 13 | | 18 | 51 | | 19 | 31 | | 20 | 18 | | 21 | 12 | | 22 | 15 | | 23 | 6 | | 24 | 35 | | 25 | 13 | | 26 | 15 | | 27 | 25 | | 28 | 13 | | 29 | 10 | | 30 | 4 | | 31 | 43 | | 32 | 27 | | 33 | 65 | | 34 | 12 | | 35 | 20 | | 36 | 14 | | 37 | 5 | | 38 | 3 | | 39 | 33 | | 40 | 23 | | 41 | 49 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 108 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 202 | | matches | | |
| 72.02% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 121 | | ratio | 0.025 | | matches | | 0 | "Ahead, a shadow darted between the pools of light—the suspect, a hulking figure in a dark hoodie." | | 1 | "The crowd parted for her not out of deference, but like water around a stone—a subtle, wary shift." | | 2 | "But Herrera was right—this was a threshold." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1100 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.026363636363636363 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007272727272727273 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 121 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 121 | | mean | 10.42 | | std | 6.39 | | cv | 0.613 | | sampleLengths | | 0 | 17 | | 1 | 17 | | 2 | 17 | | 3 | 23 | | 4 | 11 | | 5 | 14 | | 6 | 17 | | 7 | 17 | | 8 | 10 | | 9 | 7 | | 10 | 17 | | 11 | 11 | | 12 | 12 | | 13 | 13 | | 14 | 11 | | 15 | 5 | | 16 | 9 | | 17 | 9 | | 18 | 12 | | 19 | 4 | | 20 | 14 | | 21 | 6 | | 22 | 6 | | 23 | 7 | | 24 | 8 | | 25 | 19 | | 26 | 7 | | 27 | 10 | | 28 | 8 | | 29 | 10 | | 30 | 8 | | 31 | 9 | | 32 | 5 | | 33 | 9 | | 34 | 3 | | 35 | 15 | | 36 | 12 | | 37 | 7 | | 38 | 10 | | 39 | 14 | | 40 | 3 | | 41 | 3 | | 42 | 17 | | 43 | 12 | | 44 | 5 | | 45 | 14 | | 46 | 14 | | 47 | 13 | | 48 | 21 | | 49 | 5 |
| |
| 42.42% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3140495867768595 | | totalSentences | 121 | | uniqueOpeners | 38 | |
| 31.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 107 | | matches | | 0 | "Then she turned, and walked" |
| | ratio | 0.009 | |
| 33.08% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 107 | | matches | | 0 | "He’d emerged from a side-alley" | | 1 | "Her sharp jaw was set," | | 2 | "She ignored the water streaming" | | 3 | "He had something on his" | | 4 | "He cut left into an" | | 5 | "She vaulted over a fallen" | | 6 | "He scrambled up a fire" | | 7 | "She went after him, her" | | 8 | "She landed hard, a jolt" | | 9 | "He was pulling ahead, dropping" | | 10 | "She reached the skylight, a" | | 11 | "It opened into darkness." | | 12 | "Her hand went to her" | | 13 | "She was in an abandoned" | | 14 | "She heard footsteps receding down" | | 15 | "She drew her flashlight, the" | | 16 | "She recognized none of them." | | 17 | "It wasn’t deserted." | | 18 | "She’d heard whispers." | | 19 | "She’d found one in his" |
| | ratio | 0.467 | |
| 25.42% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 93 | | totalSentences | 107 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn’s boots slapped" | | 2 | "He’d emerged from a side-alley" | | 3 | "Her sharp jaw was set," | | 4 | "She ignored the water streaming" | | 5 | "He had something on his" | | 6 | "He cut left into an" | | 7 | "Quinn followed, her worn leather" | | 8 | "She vaulted over a fallen" | | 9 | "He scrambled up a fire" | | 10 | "She went after him, her" | | 11 | "The suspect leaped across a" | | 12 | "Quinn didn’t think, just moved." | | 13 | "She landed hard, a jolt" | | 14 | "He was pulling ahead, dropping" | | 15 | "She reached the skylight, a" | | 16 | "It opened into darkness." | | 17 | "The sound of dripping water" | | 18 | "Her hand went to her" | | 19 | "She was in an abandoned" |
| | ratio | 0.869 | |
| 46.73% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 107 | | matches | | 0 | "To what, she didn’t know." |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 1 | | matches | | 0 | "She vaulted over a fallen bin, her military-trained body moving with a precision that belied her age." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, his voice low, accented" | | 1 | "she said, the name hanging between them" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0 | | effectiveRatio | 0 | |