| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "She crouched again [again]" |
| | dialogueSentences | 33 | | tagDensity | 0.364 | | leniency | 0.727 | | rawRatio | 0.083 | | effectiveRatio | 0.061 | |
| 95.56% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1126 | | totalAiIsmAdverbs | 1 | | 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) | |
| 86.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1126 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "pristine" | | 1 | "measured" | | 2 | "warmth" |
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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 | 62 | | matches | (empty) | |
| 96.77% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 62 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1132 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 795 | | uniqueNames | 3 | | maxNameDensity | 0.88 | | worstName | "Pratchett" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Pratchett" | | discoveredNames | | | persons | | 0 | "Quinn" | | 1 | "Pratchett" | | 2 | "Morris" |
| | places | (empty) | | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.883 | | wordCount | 1132 | | matches | | 0 | "Not pristine, but clean in the way" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 31.44 | | std | 27.41 | | cv | 0.872 | | sampleLengths | | 0 | 48 | | 1 | 63 | | 2 | 13 | | 3 | 13 | | 4 | 22 | | 5 | 71 | | 6 | 10 | | 7 | 76 | | 8 | 4 | | 9 | 6 | | 10 | 39 | | 11 | 18 | | 12 | 12 | | 13 | 65 | | 14 | 5 | | 15 | 2 | | 16 | 10 | | 17 | 72 | | 18 | 36 | | 19 | 5 | | 20 | 1 | | 21 | 69 | | 22 | 12 | | 23 | 75 | | 24 | 71 | | 25 | 3 | | 26 | 11 | | 27 | 88 | | 28 | 8 | | 29 | 36 | | 30 | 8 | | 31 | 45 | | 32 | 21 | | 33 | 55 | | 34 | 2 | | 35 | 37 |
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| 88.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 62 | | matches | | 0 | "been examined" | | 1 | "been left" | | 2 | "been made" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 136 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 83 | | ratio | 0.06 | | matches | | 0 | "Not pristine, but clean in the way that mattered — no dust ground into the welts, no grime caking the soles." | | 1 | "For his head to have struck the near rail on the left, he'd have had to fall, strike, and then roll a full half-turn afterward — onto a broken arm, without a single smear in the dust to show for it." | | 2 | "The skin had a grey, drawn quality, the way meat looked when it had been left out — but the body wasn't cold the way it should have been at this hour, in this damp." | | 3 | "They'd been made before death — the edges had tried to heal." | | 4 | "Faint, scuffed, but deliberate — drawn around the body in something darker than dust, a substance that had soaked into the grit and dried to a rust-brown crust." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 793 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.018915510718789406 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005044136191677175 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 13.64 | | std | 9.77 | | cv | 0.716 | | sampleLengths | | 0 | 29 | | 1 | 1 | | 2 | 1 | | 3 | 17 | | 4 | 15 | | 5 | 25 | | 6 | 23 | | 7 | 13 | | 8 | 13 | | 9 | 19 | | 10 | 3 | | 11 | 31 | | 12 | 40 | | 13 | 3 | | 14 | 7 | | 15 | 3 | | 16 | 21 | | 17 | 21 | | 18 | 10 | | 19 | 21 | | 20 | 4 | | 21 | 6 | | 22 | 12 | | 23 | 27 | | 24 | 8 | | 25 | 10 | | 26 | 5 | | 27 | 7 | | 28 | 32 | | 29 | 17 | | 30 | 7 | | 31 | 9 | | 32 | 5 | | 33 | 2 | | 34 | 2 | | 35 | 8 | | 36 | 7 | | 37 | 10 | | 38 | 14 | | 39 | 41 | | 40 | 13 | | 41 | 3 | | 42 | 10 | | 43 | 10 | | 44 | 5 | | 45 | 1 | | 46 | 17 | | 47 | 16 | | 48 | 2 | | 49 | 21 |
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| 79.52% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5060240963855421 | | totalSentences | 83 | | uniqueOpeners | 42 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 74.55% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 55 | | matches | | 0 | "He flipped a page" | | 1 | "She was looking at the" | | 2 | "They were clean." | | 3 | "She turned and looked back" | | 4 | "Her own footprints stood out" | | 5 | "She stepped down onto the" | | 6 | "She tilted her head, studying" | | 7 | "He lay arranged, composed, like" | | 8 | "She leaned closer to the" | | 9 | "She lifted the dead man's" | | 10 | "It held a faint, unpleasant" | | 11 | "She set the hand down" | | 12 | "They'd been made before death" | | 13 | "She'd seen marks like that" | | 14 | "Her jaw tightened." | | 15 | "He fetched it, the cable" | | 16 | "She thought of the marks" | | 17 | "She pulled out her phone," | | 18 | "She crouched again, studying the" | | 19 | "She stood, and the lamp" |
| | ratio | 0.364 | |
| 69.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 43 | | totalSentences | 55 | | matches | | 0 | "The call had come in" | | 1 | "The abandoned platform stretched into" | | 2 | "Someone had strung work lamps" | | 3 | "A man lay sprawled across" | | 4 | "DC Pratchett looked up from" | | 5 | "Quinn crouched at the platform's" | | 6 | "He flipped a page" | | 7 | "Quinn didn't answer." | | 8 | "She was looking at the" | | 9 | "They were clean." | | 10 | "A man who'd walked down" | | 11 | "She turned and looked back" | | 12 | "Her own footprints stood out" | | 13 | "Pratchett tucked the notebook into" | | 14 | "Quinn rose, knees protesting" | | 15 | "Pratchett peered down, then shrugged." | | 16 | "She stepped down onto the" | | 17 | "The wound was there, a" | | 18 | "She tilted her head, studying" | | 19 | "Quinn measured the distance with" |
| | ratio | 0.782 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 55 | | matches | (empty) | | ratio | 0 | |
| 73.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 3 | | matches | | 0 | "A man who'd walked down two hundred feet of filth-choked stairwell should have left a trail of it across the platform." | | 1 | "It held a faint, unpleasant warmth, as though it had been somewhere else until recently and only now begun to cool." | | 2 | "Faint, scuffed, but deliberate — drawn around the body in something darker than dust, a substance that had soaked into the grit and dried to a rust-brown crust." |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "She stood, and the lamp threw her shadow long across the tiles" |
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| 89.39% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Quinn rose knees protesting (protest)" | | 1 | "Pratchett breathed (breathe)" |
| | dialogueSentences | 33 | | tagDensity | 0.091 | | leniency | 0.182 | | rawRatio | 0.667 | | effectiveRatio | 0.121 | |