| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 43 | | tagDensity | 0.279 | | leniency | 0.558 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 81.80% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1374 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "softly" | | 1 | "slightly" | | 2 | "slowly" |
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| 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) | |
| 41.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1374 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "fractured" | | 1 | "electric" | | 2 | "fleeting" | | 3 | "throb" | | 4 | "weight" | | 5 | "database" | | 6 | "calculating" | | 7 | "flickered" | | 8 | "etched" | | 9 | "gloom" | | 10 | "echoing" | | 11 | "silence" | | 12 | "rhythmic" | | 13 | "pulsed" | | 14 | "dancing" | | 15 | "crystalline" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | 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 | 1 | | markdownWords | 1 | | totalWords | 1370 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.10% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 1031 | | uniqueNames | 7 | | maxNameDensity | 1.36 | | worstName | "Herrera" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Quinn | 11 | | Tomás | 1 | | Herrera | 14 | | Morris | 4 | | Sevillan | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Tomás" | | 2 | "Herrera" | | 3 | "Morris" | | 4 | "Market" |
| | places | | | globalScore | 0.821 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 3 | | per1kWords | 2.19 | | wordCount | 1370 | | matches | | 0 | "Not into a maintenance room, but onto a steep, narrow set of stairs descending into absolute" | | 1 | "not damp or decay, but something older" | | 2 | "not the noise of a street market, but a low, resonant hum of voices, clinking glass" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 26.35 | | std | 18.94 | | cv | 0.719 | | sampleLengths | | 0 | 73 | | 1 | 16 | | 2 | 57 | | 3 | 78 | | 4 | 12 | | 5 | 56 | | 6 | 38 | | 7 | 11 | | 8 | 35 | | 9 | 60 | | 10 | 16 | | 11 | 43 | | 12 | 11 | | 13 | 34 | | 14 | 18 | | 15 | 17 | | 16 | 22 | | 17 | 6 | | 18 | 38 | | 19 | 38 | | 20 | 12 | | 21 | 22 | | 22 | 1 | | 23 | 28 | | 24 | 4 | | 25 | 46 | | 26 | 9 | | 27 | 28 | | 28 | 11 | | 29 | 38 | | 30 | 48 | | 31 | 30 | | 32 | 8 | | 33 | 13 | | 34 | 3 | | 35 | 11 | | 36 | 52 | | 37 | 18 | | 38 | 5 | | 39 | 11 | | 40 | 6 | | 41 | 43 | | 42 | 22 | | 43 | 49 | | 44 | 26 | | 45 | 2 | | 46 | 14 | | 47 | 44 | | 48 | 40 | | 49 | 14 |
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| 91.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 105 | | matches | | 0 | "was gone" | | 1 | "was etched" | | 2 | "were carved" | | 3 | "was strained" |
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| 84.39% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 173 | | matches | | 0 | "was fleeing" | | 1 | "was hunting" | | 2 | "was herding" |
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| 57.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 134 | | ratio | 0.03 | | matches | | 0 | "She didn’t run; she advanced." | | 1 | "Ahead, the suspect—Tomás Herrera, according to the file—ducked past a group of laughing tourists, his dark jacket a fleeting shadow against the brighter chaos." | | 2 | "A smell rolled up to meet them—not damp or decay, but something older." | | 3 | "The tunnel widened, and the sound of a crowd reached them—not the noise of a street market, but a low, resonant hum of voices, clinking glass, and the occasional sharp chime of something crystalline." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 773 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.028460543337645538 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.01164294954721863 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 10.22 | | std | 6.48 | | cv | 0.634 | | sampleLengths | | 0 | 23 | | 1 | 12 | | 2 | 9 | | 3 | 5 | | 4 | 24 | | 5 | 10 | | 6 | 3 | | 7 | 3 | | 8 | 14 | | 9 | 12 | | 10 | 13 | | 11 | 18 | | 12 | 9 | | 13 | 11 | | 14 | 3 | | 15 | 17 | | 16 | 2 | | 17 | 1 | | 18 | 15 | | 19 | 5 | | 20 | 15 | | 21 | 5 | | 22 | 7 | | 23 | 15 | | 24 | 11 | | 25 | 16 | | 26 | 14 | | 27 | 11 | | 28 | 9 | | 29 | 9 | | 30 | 3 | | 31 | 1 | | 32 | 5 | | 33 | 10 | | 34 | 1 | | 35 | 2 | | 36 | 12 | | 37 | 12 | | 38 | 5 | | 39 | 4 | | 40 | 17 | | 41 | 4 | | 42 | 12 | | 43 | 5 | | 44 | 22 | | 45 | 11 | | 46 | 5 | | 47 | 14 | | 48 | 29 | | 49 | 9 |
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| 53.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.3582089552238806 | | totalSentences | 134 | | uniqueOpeners | 48 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 34.14% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 46 | | totalSentences | 99 | | matches | | 0 | "Her breath came in sharp," | | 1 | "She didn’t run; she advanced." | | 2 | "He was quick, but she" | | 3 | "He cut left, down a" | | 4 | "She followed, her hand resting" | | 5 | "He wove through the pedestrian" | | 6 | "They’d found him two days" | | 7 | "He’d resigned a week later." | | 8 | "She shook the memory off." | | 9 | "It was a weight, not" | | 10 | "She slipped through, the noise" | | 11 | "His warm brown eyes widened" | | 12 | "He knew who she was." | | 13 | "Her voice was flat, cutting" | | 14 | "He increased his pace, turning" | | 15 | "Her movements were precise, economical." | | 16 | "She was herding him." | | 17 | "He spun, his breath fogging." | | 18 | "he said, his Sevillan accent" | | 19 | "He stayed pressed against the" |
| | ratio | 0.465 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 92 | | totalSentences | 99 | | matches | | 0 | "The rain turned the pavement" | | 1 | "Harlow Quinn’s boots pounded the" | | 2 | "Her breath came in sharp," | | 3 | "She didn’t run; she advanced." | | 4 | "He was quick, but she" | | 5 | "Herrera was fleeing." | | 6 | "Quinn was hunting." | | 7 | "He cut left, down a" | | 8 | "She followed, her hand resting" | | 9 | "The alley spat them out" | | 10 | "He wove through the pedestrian" | | 11 | "A different suspect, a different" | | 12 | "The same rain." | | 13 | "Morris had gone down an" | | 14 | "They’d found him two days" | | 15 | "He’d resigned a week later." | | 16 | "Quinn never got an answer," | | 17 | "She shook the memory off." | | 18 | "It was a weight, not" | | 19 | "Herrera rounded a corner ahead," |
| | ratio | 0.929 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 1 | | matches | | 0 | "Quinn never got an answer, only a cold case file that made her temples throb." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 2 | | matches | | 0 | "he said, his Sevillan accent thickening his vowels" | | 1 | "she asked, her voice echoing slightly" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | 0 | "Herrera whispered (whisper)" |
| | dialogueSentences | 43 | | tagDensity | 0.116 | | leniency | 0.233 | | rawRatio | 0.2 | | effectiveRatio | 0.047 | |