| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.314 | | leniency | 0.627 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1308 | | 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) | |
| 88.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1308 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "etched" | | 1 | "trembled" | | 2 | "weight" |
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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 | 87 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 121 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1308 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 14 | | wordCount | 918 | | uniqueNames | 4 | | maxNameDensity | 0.87 | | worstName | "Ferrier" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Ferrier" | | discoveredNames | | Ferrier | 8 | | Quinn | 4 | | Careful | 1 | | Hoxton | 1 |
| | persons | | | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1308 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 121 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 22.55 | | std | 21.11 | | cv | 0.936 | | sampleLengths | | 0 | 14 | | 1 | 38 | | 2 | 1 | | 3 | 66 | | 4 | 6 | | 5 | 1 | | 6 | 46 | | 7 | 45 | | 8 | 10 | | 9 | 17 | | 10 | 66 | | 11 | 6 | | 12 | 1 | | 13 | 1 | | 14 | 66 | | 15 | 16 | | 16 | 3 | | 17 | 5 | | 18 | 25 | | 19 | 35 | | 20 | 7 | | 21 | 27 | | 22 | 58 | | 23 | 3 | | 24 | 2 | | 25 | 31 | | 26 | 6 | | 27 | 59 | | 28 | 9 | | 29 | 2 | | 30 | 19 | | 31 | 56 | | 32 | 21 | | 33 | 10 | | 34 | 11 | | 35 | 38 | | 36 | 54 | | 37 | 7 | | 38 | 38 | | 39 | 44 | | 40 | 4 | | 41 | 35 | | 42 | 4 | | 43 | 2 | | 44 | 7 | | 45 | 2 | | 46 | 13 | | 47 | 63 | | 48 | 40 | | 49 | 9 |
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| 97.20% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 87 | | matches | | 0 | "been opened" | | 1 | "been buttoned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 146 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 121 | | ratio | 0.008 | | matches | | 0 | "She'd give him that much; he did look." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 921 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.03691639522258415 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.0054288816503800215 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 121 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 121 | | mean | 10.81 | | std | 9.57 | | cv | 0.886 | | sampleLengths | | 0 | 14 | | 1 | 17 | | 2 | 11 | | 3 | 4 | | 4 | 6 | | 5 | 1 | | 6 | 21 | | 7 | 7 | | 8 | 16 | | 9 | 22 | | 10 | 6 | | 11 | 1 | | 12 | 9 | | 13 | 26 | | 14 | 3 | | 15 | 3 | | 16 | 5 | | 17 | 19 | | 18 | 26 | | 19 | 7 | | 20 | 3 | | 21 | 17 | | 22 | 11 | | 23 | 6 | | 24 | 21 | | 25 | 28 | | 26 | 6 | | 27 | 1 | | 28 | 1 | | 29 | 26 | | 30 | 6 | | 31 | 2 | | 32 | 2 | | 33 | 17 | | 34 | 13 | | 35 | 5 | | 36 | 11 | | 37 | 3 | | 38 | 5 | | 39 | 25 | | 40 | 19 | | 41 | 6 | | 42 | 2 | | 43 | 8 | | 44 | 7 | | 45 | 27 | | 46 | 3 | | 47 | 15 | | 48 | 40 | | 49 | 3 |
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| 79.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5041322314049587 | | totalSentences | 121 | | uniqueOpeners | 61 | |
| 95.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 70 | | matches | | 0 | "Then she prised open his" | | 1 | "Then carried on." |
| | ratio | 0.029 | |
| 94.29% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 70 | | matches | | 0 | "He'd brought coffee to a" | | 1 | "She stood and stepped back" | | 2 | "He swung his torch about" | | 3 | "It threw every ridge into" | | 4 | "He straightened up and held" | | 5 | "She'd give him that much;" | | 6 | "She meant it" | | 7 | "It was a decent theory," | | 8 | "She went back to the" | | 9 | "His beam wobbled" | | 10 | "She joined him." | | 11 | "It had crept up the" | | 12 | "He rubbed his jaw" | | 13 | "She was, a little, and" | | 14 | "She turned back and worked" | | 15 | "It took effort." | | 16 | "It turned slow and steady," | | 17 | "He jerked a thumb" | | 18 | "She held the thing flat" | | 19 | "She looked at the dead" |
| | ratio | 0.314 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 70 | | matches | | 0 | "The corpse had no shoes," | | 1 | "Quinn crouched at the platform" | | 2 | "He'd brought coffee to a" | | 3 | "She stood and stepped back" | | 4 | "The dead man lay on" | | 5 | "Charity shop overcoat." | | 6 | "Socks white as communion wafers." | | 7 | "Quinn pointed the beam at" | | 8 | "He swung his torch about" | | 9 | "The old station drank the" | | 10 | "Cream tiles, ox-blood trim, the" | | 11 | "Quinn crossed to the tiled" | | 12 | "It threw every ridge into" | | 13 | "A bare foot with too" | | 14 | "He straightened up and held" | | 15 | "She'd give him that much;" | | 16 | "The tone people used when" | | 17 | "She meant it" | | 18 | "It was a decent theory," | | 19 | "She went back to the" |
| | ratio | 0.657 | |
| 71.43% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 70 | | matches | | 0 | "To his credit, he didn't" |
| | ratio | 0.014 | |
| 93.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 2 | | matches | | 0 | "Cream tiles, ox-blood trim, the ghost of an advertisement for a cigarette brand that had killed its last customer decades ago." | | 1 | "A single stroke, right to left, deep enough that she could see the pale gleam of something that should never see torchlight." |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "She stood, and the frost cracked beneath her, and the sound went down the tunnel and came back too late, as if the tunnel had thought about it first" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.098 | | leniency | 0.196 | | rawRatio | 0 | | effectiveRatio | 0 | |