| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 30 | | adverbTagCount | 2 | | adverbTags | | 0 | "Miller scoffed finally [finally]" | | 1 | "Eva said softly [softly]" |
| | dialogueSentences | 64 | | tagDensity | 0.469 | | leniency | 0.938 | | rawRatio | 0.067 | | effectiveRatio | 0.063 | |
| 93.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1494 | | 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) | |
| 49.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1494 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "pulse" | | 1 | "ensuring" | | 2 | "gloom" | | 3 | "chill" | | 4 | "etched" | | 5 | "silence" | | 6 | "weight" | | 7 | "processed" | | 8 | "vibrated" | | 9 | "could feel" | | 10 | "unreadable" | | 11 | "sense of" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 113 | | matches | | |
| 67.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 5 | | hedgeCount | 1 | | narrationSentences | 113 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 147 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1494 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 28 | | unquotedAttributions | 0 | | matches | (empty) | |
| 6.58% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 72 | | wordCount | 1011 | | uniqueNames | 12 | | maxNameDensity | 2.87 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Quinn | 29 | | Miller | 10 | | Kowalski | 2 | | Eva | 18 | | North | 1 | | Aurora | 1 | | London | 1 | | Compass | 6 | | Met | 1 | | Brass | 1 |
| | persons | | 0 | "Camden" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Aurora" | | 6 | "Brass" |
| | places | | | globalScore | 0.066 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like scars on the metal" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1494 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 147 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 26.68 | | std | 19.39 | | cv | 0.727 | | sampleLengths | | 0 | 82 | | 1 | 54 | | 2 | 32 | | 3 | 8 | | 4 | 24 | | 5 | 57 | | 6 | 18 | | 7 | 45 | | 8 | 74 | | 9 | 22 | | 10 | 31 | | 11 | 26 | | 12 | 75 | | 13 | 15 | | 14 | 26 | | 15 | 41 | | 16 | 16 | | 17 | 8 | | 18 | 44 | | 19 | 20 | | 20 | 10 | | 21 | 40 | | 22 | 10 | | 23 | 48 | | 24 | 11 | | 25 | 7 | | 26 | 40 | | 27 | 11 | | 28 | 8 | | 29 | 44 | | 30 | 52 | | 31 | 12 | | 32 | 16 | | 33 | 9 | | 34 | 18 | | 35 | 7 | | 36 | 20 | | 37 | 9 | | 38 | 32 | | 39 | 58 | | 40 | 22 | | 41 | 6 | | 42 | 26 | | 43 | 20 | | 44 | 51 | | 45 | 13 | | 46 | 7 | | 47 | 26 | | 48 | 38 | | 49 | 16 |
| |
| 83.53% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 113 | | matches | | 0 | "was dressed" | | 1 | "was etched" | | 2 | "was worn" | | 3 | "been walked" | | 4 | "been placed" | | 5 | "was scratched" | | 6 | "been told" |
| |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 197 | | matches | | 0 | "was tucking" | | 1 | "wasn't pointing" | | 2 | "was vibrating" | | 3 | "were trying" | | 4 | "was pointing" | | 5 | "was protecting" | | 6 | "was staring" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 147 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1014 | | adjectiveStacks | 1 | | stackExamples | | 0 | "heavy, pressing against Quinn's" |
| | adverbCount | 27 | | adverbRatio | 0.026627218934911243 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007889546351084813 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 147 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 147 | | mean | 10.16 | | std | 5.91 | | cv | 0.581 | | sampleLengths | | 0 | 14 | | 1 | 20 | | 2 | 20 | | 3 | 28 | | 4 | 18 | | 5 | 17 | | 6 | 19 | | 7 | 12 | | 8 | 6 | | 9 | 14 | | 10 | 8 | | 11 | 4 | | 12 | 20 | | 13 | 10 | | 14 | 12 | | 15 | 13 | | 16 | 22 | | 17 | 13 | | 18 | 5 | | 19 | 5 | | 20 | 15 | | 21 | 13 | | 22 | 12 | | 23 | 12 | | 24 | 23 | | 25 | 25 | | 26 | 14 | | 27 | 10 | | 28 | 9 | | 29 | 3 | | 30 | 5 | | 31 | 14 | | 32 | 12 | | 33 | 11 | | 34 | 9 | | 35 | 6 | | 36 | 5 | | 37 | 8 | | 38 | 15 | | 39 | 16 | | 40 | 8 | | 41 | 4 | | 42 | 19 | | 43 | 7 | | 44 | 8 | | 45 | 14 | | 46 | 12 | | 47 | 9 | | 48 | 12 | | 49 | 20 |
| |
| 36.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.24489795918367346 | | totalSentences | 147 | | uniqueOpeners | 36 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 94 | | matches | (empty) | | ratio | 0 | |
| 28.51% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 94 | | matches | | 0 | "She checked the time, then" | | 1 | "She adjusted her collar, her" | | 2 | "He looked tired, his eyes" | | 3 | "He was a good man," | | 4 | "It was too quiet for" | | 5 | "She knelt beside the body," | | 6 | "She didn't move her own" | | 7 | "She was dressed in a" | | 8 | "She was tucking a strand" | | 9 | "She held a leather satchel" | | 10 | "Her voice was quiet, cutting" | | 11 | "She kept her eyes on" | | 12 | "She ignored the warning look" | | 13 | "She held out her hand." | | 14 | "It had a patina of" | | 15 | "It wasn't pointing North." | | 16 | "It was vibrating, shivering against" | | 17 | "She checked her watch." | | 18 | "She touched the face of" | | 19 | "She didn't touch anything." |
| | ratio | 0.479 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 90 | | totalSentences | 94 | | matches | | 0 | "The air in the abandoned" | | 1 | "Harlow Quinn paused at the" | | 2 | "She checked the time, then" | | 3 | "The station was dark, lit" | | 4 | "Quinn stepped over the yellow" | | 5 | "She adjusted her collar, her" | | 6 | "DS Miller stood by the" | | 7 | "He looked tired, his eyes" | | 8 | "He was a good man," | | 9 | "Quinn asked, her voice low" | | 10 | "Miller didn't look up." | | 11 | "Quinn stepped closer, her gaze" | | 12 | "The station was quiet, the" | | 13 | "It was too quiet for" | | 14 | "Miller said, tapping his pen" | | 15 | "She knelt beside the body," | | 16 | "She didn't move her own" | | 17 | "The victim was young, a" | | 18 | "Eva Kowalski stood three yards" | | 19 | "She was dressed in a" |
| | ratio | 0.957 | |
| 53.19% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 94 | | matches | | | ratio | 0.011 | |
| 59.80% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 5 | | matches | | 0 | "She was dressed in a trench coat that looked too heavy for the heat, her curly red hair pinned back under a hat." | | 1 | "It was vibrating, shivering against the brass casing as if it were trying to push through the glass face." | | 2 | "Eva Kowalski, Aurora’s friend, the occult researcher who knew more about things that didn't exist than anyone else in London." | | 3 | "She remembered the phone call, the rain, the silence that followed the sirens." | | 4 | "She could feel the supernatural energy humming under the floorboards, a faint hum that made the hair on her arms stand up." |
| |
| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 30 | | uselessAdditionCount | 6 | | matches | | 0 | "Quinn asked, her voice low" | | 1 | "Quinn stepped, her gaze sweeping the area" | | 2 | "Eva corrected, her green eyes flashing behind her round glasses" | | 3 | "Quinn said, her voice flat" | | 4 | "Eva said, her voice firm" | | 5 | "Quinn said, her voice hard" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 27 | | fancyCount | 3 | | fancyTags | | 0 | "Eva corrected (correct)" | | 1 | "Eva murmured (murmur)" | | 2 | "Miller argued (argue)" |
| | dialogueSentences | 64 | | tagDensity | 0.422 | | leniency | 0.844 | | rawRatio | 0.111 | | effectiveRatio | 0.094 | |