| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "He stepped back [back]" |
| | dialogueSentences | 23 | | tagDensity | 0.217 | | leniency | 0.435 | | rawRatio | 0.2 | | effectiveRatio | 0.087 | |
| 81.20% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1064 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "gently" | | 1 | "slightly" | | 2 | "quickly" |
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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) | |
| 57.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1064 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "weight" | | 1 | "glint" | | 2 | "loomed" | | 3 | "flicker" | | 4 | "wavering" | | 5 | "pulse" | | 6 | "charm" | | 7 | "familiar" | | 8 | "flicked" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 106 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 106 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 122 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 6 | | markdownWords | 13 | | totalWords | 1054 | | ratio | 0.012 | | matches | | 0 | "Tomás Herrera." | | 1 | "his" | | 2 | "to" | | 3 | "The Anatomy of Melancholy" | | 4 | "information" | | 5 | "When had that happened?" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 913 | | uniqueNames | 16 | | maxNameDensity | 1.2 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Silas" | | discoveredNames | | Harlow | 1 | | Quinn | 11 | | Saint | 1 | | Christopher | 1 | | Former | 1 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Vane | 2 | | Herrera | 8 | | Anatomy | 1 | | Tube | 1 | | Veil | 2 | | Market | 2 | | Smooth | 1 | | Silas | 8 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Former" | | 5 | "Raven" | | 6 | "Nest" | | 7 | "Vane" | | 8 | "Herrera" | | 9 | "Silas" |
| | places | | | globalScore | 0.898 | | windowScore | 0.5 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 4 | | matches | | 0 | "And now, apparently, a man with somethi" | | 1 | "quite match the rest of the decor" | | 2 | "looked like a human tooth, while a woman" | | 3 | "looked like jars of fireflies" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1054 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 122 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 23.95 | | std | 19.39 | | cv | 0.809 | | sampleLengths | | 0 | 9 | | 1 | 56 | | 2 | 64 | | 3 | 20 | | 4 | 40 | | 5 | 2 | | 6 | 56 | | 7 | 5 | | 8 | 66 | | 9 | 4 | | 10 | 30 | | 11 | 43 | | 12 | 8 | | 13 | 53 | | 14 | 5 | | 15 | 19 | | 16 | 24 | | 17 | 7 | | 18 | 63 | | 19 | 8 | | 20 | 47 | | 21 | 49 | | 22 | 5 | | 23 | 7 | | 24 | 42 | | 25 | 13 | | 26 | 5 | | 27 | 36 | | 28 | 15 | | 29 | 20 | | 30 | 3 | | 31 | 29 | | 32 | 9 | | 33 | 38 | | 34 | 13 | | 35 | 27 | | 36 | 7 | | 37 | 34 | | 38 | 7 | | 39 | 24 | | 40 | 10 | | 41 | 21 | | 42 | 6 | | 43 | 5 |
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| 98.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 106 | | matches | | 0 | "were lined" | | 1 | "was dressed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 160 | | matches | | 0 | "was running" | | 1 | "was heading" |
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| 49.18% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 122 | | ratio | 0.033 | | matches | | 0 | "He wasn’t built for speed—too stocky, too much weight in the shoulders—but fear was a hell of a motivator." | | 1 | "This wasn’t just any dive—it was *his* place." | | 2 | "The walls were lined with maps—some yellowed with age, others crisp and modern—all pinned up like trophies." | | 3 | "She took a step forward—" |
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| 91.39% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 923 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 46 | | adverbRatio | 0.04983748645720477 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.010834236186348862 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 122 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 122 | | mean | 8.64 | | std | 5.68 | | cv | 0.658 | | sampleLengths | | 0 | 9 | | 1 | 6 | | 2 | 4 | | 3 | 23 | | 4 | 23 | | 5 | 11 | | 6 | 19 | | 7 | 19 | | 8 | 4 | | 9 | 2 | | 10 | 9 | | 11 | 7 | | 12 | 13 | | 13 | 5 | | 14 | 8 | | 15 | 12 | | 16 | 15 | | 17 | 2 | | 18 | 14 | | 19 | 16 | | 20 | 8 | | 21 | 2 | | 22 | 11 | | 23 | 5 | | 24 | 5 | | 25 | 14 | | 26 | 17 | | 27 | 17 | | 28 | 3 | | 29 | 15 | | 30 | 4 | | 31 | 2 | | 32 | 9 | | 33 | 11 | | 34 | 8 | | 35 | 5 | | 36 | 12 | | 37 | 1 | | 38 | 1 | | 39 | 11 | | 40 | 13 | | 41 | 8 | | 42 | 7 | | 43 | 11 | | 44 | 16 | | 45 | 19 | | 46 | 5 | | 47 | 1 | | 48 | 3 | | 49 | 1 |
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| 56.83% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.38524590163934425 | | totalSentences | 122 | | uniqueOpeners | 47 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 95 | | matches | | 0 | "Just vaulted the low wall" | | 1 | "Then she saw it." | | 2 | "Instead, he tilted his head" |
| | ratio | 0.032 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 95 | | matches | | 0 | "He wasn’t built for speed—too" | | 1 | "He glanced back, eyes wide," | | 2 | "She pulled her radio from" | | 3 | "She cursed, slamming it back" | | 4 | "He was running *to* something." | | 5 | "She pushed the door open." | | 6 | "She moved toward it, her" | | 7 | "She pulled it without looking," | | 8 | "She pressed herself against the" | | 9 | "It was like stepping into" | | 10 | "He moved quickly, his scarred" | | 11 | "He was heading for the" | | 12 | "She knew what lay beyond" | | 13 | "It sold *information*." | | 14 | "She took a step forward—" | | 15 | "She twisted, grabbing the wrist," | | 16 | "She spun, fists raised, but" | | 17 | "He was dressed in his" | | 18 | "She touched her temple." | | 19 | "Her fingers came away red." |
| | ratio | 0.295 | |
| 86.32% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 95 | | matches | | 0 | "The pavement cracked under her" | | 1 | "Detective Harlow Quinn didn’t slow" | | 2 | "A taxi horn blared, tyres" | | 3 | "He wasn’t built for speed—too" | | 4 | "He glanced back, eyes wide," | | 5 | "*Tomás Herrera.* Former paramedic." | | 6 | "She pulled her radio from" | | 7 | "The radio hissed, then died." | | 8 | "She cursed, slamming it back" | | 9 | "Herrera had just taken a" | | 10 | "The Raven’s Nest loomed ahead," | | 11 | "The bar’s door was ajar," | | 12 | "This wasn’t just any dive—it" | | 13 | "He was running *to* something." | | 14 | "She pushed the door open." | | 15 | "The air inside was thick" | | 16 | "The walls were lined with" | | 17 | "The only sound was the" | | 18 | "The one in the far" | | 19 | "The one that didn’t quite" |
| | ratio | 0.747 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 95 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 1 | | matches | | 0 | "Bundles of dried herbs that smelled of ozone and something older, something wild." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 19.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 3 | | fancyTags | | 0 | "a voice murmured (murmur)" | | 1 | "Silas sighed (sigh)" | | 2 | "He pressed (press)" |
| | dialogueSentences | 23 | | tagDensity | 0.13 | | leniency | 0.261 | | rawRatio | 1 | | effectiveRatio | 0.261 | |