| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.421 | | leniency | 0.842 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.51% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 690 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "barely above a whisper" | | count | 1 |
|
| | highlights | | 0 | "slowly" | | 1 | "barely above a whisper" |
| |
| 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) | |
| 20.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 690 | | totalAiIsms | 11 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | word | "down her spine" | | count | 1 |
| | 10 | |
| | highlights | | 0 | "echoing" | | 1 | "sinister" | | 2 | "etched" | | 3 | "furrowing" | | 4 | "whisper" | | 5 | "eyebrow" | | 6 | "silence" | | 7 | "racing" | | 8 | "chill" | | 9 | "down her spine" | | 10 | "scanning" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 40 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 40 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 51 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 686 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 480 | | uniqueNames | 13 | | maxNameDensity | 2.29 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Detective | 2 | | Harlow | 1 | | Quinn | 11 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Andrew | 1 | | Harris | 4 | | Blake | 2 | | Eva | 6 | | Morris | 2 | | Kowalski | 1 |
| | persons | | 0 | "Detective" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Market" | | 4 | "Andrew" | | 5 | "Harris" | | 6 | "Blake" | | 7 | "Eva" | | 8 | "Morris" | | 9 | "Kowalski" |
| | places | (empty) | | globalScore | 0.354 | | windowScore | 0.167 | |
| 80.56% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 36 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a small, crescent moon" |
| |
| 54.23% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.458 | | wordCount | 686 | | matches | | 0 | "not yet, but she was getting closer" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 51 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 17 | | mean | 40.35 | | std | 23.29 | | cv | 0.577 | | sampleLengths | | 0 | 71 | | 1 | 60 | | 2 | 63 | | 3 | 91 | | 4 | 28 | | 5 | 44 | | 6 | 62 | | 7 | 34 | | 8 | 13 | | 9 | 16 | | 10 | 14 | | 11 | 7 | | 12 | 41 | | 13 | 50 | | 14 | 20 | | 15 | 21 | | 16 | 51 |
| |
| 96.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 40 | | matches | | |
| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 80 | | matches | | 0 | "was packing" | | 1 | "was getting" | | 2 | "was going" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 51 | | ratio | 0 | | matches | (empty) | |
| 92.77% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 483 | | adjectiveStacks | 1 | | stackExamples | | 0 | "damp, moss-covered steps." |
| | adverbCount | 20 | | adverbRatio | 0.041407867494824016 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.016563146997929608 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 51 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 51 | | mean | 13.45 | | std | 7.29 | | cv | 0.542 | | sampleLengths | | 0 | 25 | | 1 | 21 | | 2 | 25 | | 3 | 28 | | 4 | 17 | | 5 | 15 | | 6 | 21 | | 7 | 16 | | 8 | 5 | | 9 | 21 | | 10 | 10 | | 11 | 11 | | 12 | 16 | | 13 | 21 | | 14 | 33 | | 15 | 8 | | 16 | 20 | | 17 | 9 | | 18 | 35 | | 19 | 15 | | 20 | 6 | | 21 | 6 | | 22 | 21 | | 23 | 9 | | 24 | 5 | | 25 | 16 | | 26 | 10 | | 27 | 8 | | 28 | 4 | | 29 | 9 | | 30 | 6 | | 31 | 10 | | 32 | 3 | | 33 | 11 | | 34 | 7 | | 35 | 9 | | 36 | 21 | | 37 | 11 | | 38 | 9 | | 39 | 14 | | 40 | 7 | | 41 | 12 | | 42 | 8 | | 43 | 7 | | 44 | 13 | | 45 | 6 | | 46 | 15 | | 47 | 11 | | 48 | 10 | | 49 | 13 |
| |
| 65.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.45098039215686275 | | totalSentences | 51 | | uniqueOpeners | 23 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 50.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 40 | | matches | | 0 | "Her salt-and-pepper hair, cropped short," | | 1 | "Her colleague, Detective Andrew Harris," | | 2 | "He looked up as she" | | 3 | "he acknowledged, straightening up" | | 4 | "She examined the body, her" | | 5 | "she murmured, more to herself" | | 6 | "She pointed to a small," | | 7 | "She turned to a nearby" | | 8 | "she called out, striding over" | | 9 | "She knew Eva was involved" | | 10 | "She couldn't prove it, not" | | 11 | "She crouched down again, examining" | | 12 | "It looked like a small," | | 13 | "She'd seen that mark before," | | 14 | "She felt a chill run" | | 15 | "She walked back to the" | | 16 | "She needed to find out" |
| | ratio | 0.425 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 40 | | matches | | 0 | "The stench of decay and" | | 1 | "Her salt-and-pepper hair, cropped short," | | 2 | "The Veil Market, a hidden" | | 3 | "The market was a labyrinth" | | 4 | "Tonight, it was a crime" | | 5 | "Her colleague, Detective Andrew Harris," | | 6 | "He looked up as she" | | 7 | "he acknowledged, straightening up" | | 8 | "Quinn crouched down, her military" | | 9 | "She examined the body, her" | | 10 | "she murmured, more to herself" | | 11 | "She pointed to a small," | | 12 | "Harris squinted at the compass," | | 13 | "Quinn stood up, her gaze" | | 14 | "She turned to a nearby" | | 15 | "she called out, striding over" | | 16 | "The figure froze, then slowly" | | 17 | "A young woman with curly" | | 18 | "Quinn said, recognition flickering in" | | 19 | "Eva tucked a strand of" |
| | ratio | 0.9 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 40 | | matches | | 0 | "Because she was sure, whatever" |
| | ratio | 0.025 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 1 | | matches | | 0 | "She knew Eva was involved in something dangerous, something that had cost her partner, DS Morris, his life three years ago." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 4 | | matches | | 0 | "she murmured, more to herself than to Harris" | | 1 | "Quinn said, recognition flickering in her eyes" | | 2 | "Eva acknowledged, her voice barely above a whisper" | | 3 | "Harris called out, his voice hesitant" |
| |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 5 | | fancyTags | | 0 | "he acknowledged (acknowledge)" | | 1 | "she murmured (murmur)" | | 2 | "she called out (call out)" | | 3 | "Eva acknowledged (acknowledge)" | | 4 | "Harris called out (call out)" |
| | dialogueSentences | 19 | | tagDensity | 0.316 | | leniency | 0.632 | | rawRatio | 0.833 | | effectiveRatio | 0.526 | |