| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.32 | | leniency | 0.64 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1057 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 90.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1057 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 11 | | totalWords | 1052 | | ratio | 0.01 | | matches | | 0 | "suspicious death, strange circumstances" | | 1 | "we don't understand what we're looking at" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 18 | | wordCount | 825 | | uniqueNames | 8 | | maxNameDensity | 0.73 | | worstName | "Farrow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Farrow" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Town | 1 | | Farrow | 6 | | Two | 1 | | Quinn | 4 | | Victorian | 1 | | Looked | 3 |
| | persons | | 0 | "Farrow" | | 1 | "Quinn" | | 2 | "Looked" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 47.96% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 2 | | matches | | 0 | "Not quite anything she had a category for" | | 1 | "looked like he'd been placed here by some" |
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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 | 1052 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 26.3 | | std | 25.44 | | cv | 0.967 | | sampleLengths | | 0 | 6 | | 1 | 83 | | 2 | 12 | | 3 | 73 | | 4 | 52 | | 5 | 3 | | 6 | 3 | | 7 | 80 | | 8 | 2 | | 9 | 7 | | 10 | 58 | | 11 | 3 | | 12 | 19 | | 13 | 8 | | 14 | 56 | | 15 | 27 | | 16 | 6 | | 17 | 36 | | 18 | 6 | | 19 | 30 | | 20 | 58 | | 21 | 9 | | 22 | 38 | | 23 | 38 | | 24 | 10 | | 25 | 1 | | 26 | 10 | | 27 | 43 | | 28 | 7 | | 29 | 70 | | 30 | 7 | | 31 | 30 | | 32 | 76 | | 33 | 7 | | 34 | 25 | | 35 | 5 | | 36 | 35 | | 37 | 6 | | 38 | 1 | | 39 | 6 |
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| 88.35% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 83 | | matches | | 0 | "was crouched" | | 1 | "was engraved" | | 2 | "been placed" | | 3 | "been unpicked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 137 | | matches | | 0 | "was thinking" | | 1 | "was pointing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 100 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 829 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.033775633293124246 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.008443908323281062 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 10.52 | | std | 8.95 | | cv | 0.851 | | sampleLengths | | 0 | 6 | | 1 | 18 | | 2 | 42 | | 3 | 23 | | 4 | 12 | | 5 | 22 | | 6 | 19 | | 7 | 2 | | 8 | 9 | | 9 | 2 | | 10 | 2 | | 11 | 4 | | 12 | 1 | | 13 | 12 | | 14 | 9 | | 15 | 16 | | 16 | 21 | | 17 | 6 | | 18 | 3 | | 19 | 3 | | 20 | 18 | | 21 | 8 | | 22 | 2 | | 23 | 3 | | 24 | 13 | | 25 | 17 | | 26 | 19 | | 27 | 2 | | 28 | 7 | | 29 | 34 | | 30 | 24 | | 31 | 3 | | 32 | 19 | | 33 | 5 | | 34 | 3 | | 35 | 10 | | 36 | 2 | | 37 | 3 | | 38 | 10 | | 39 | 9 | | 40 | 22 | | 41 | 2 | | 42 | 4 | | 43 | 11 | | 44 | 1 | | 45 | 8 | | 46 | 1 | | 47 | 6 | | 48 | 14 | | 49 | 22 |
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| 84.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.53 | | totalSentences | 100 | | uniqueOpeners | 53 | |
| 48.31% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 69 | | matches | | 0 | "Aside from the fact that" |
| | ratio | 0.014 | |
| 92.46% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 69 | | matches | | 0 | "She'd stepped off the Tube" | | 1 | "She still didn't, and she'd" | | 2 | "He lay on his back" | | 3 | "He was crouched near the" | | 4 | "She stepped under the tape" | | 5 | "Her torch swept the space" | | 6 | "His eyes were closed, which" | | 7 | "She looked at the floor" | | 8 | "She moved her torch along" | | 9 | "She crossed the platform in" | | 10 | "She didn't touch it." | | 11 | "She leaned in close and" | | 12 | "She'd have felt it." | | 13 | "He came over." | | 14 | "She studied the symbols on" | | 15 | "She looked back down the" | | 16 | "She thought about the single" | | 17 | "She appreciated that about him." | | 18 | "He was teachable." | | 19 | "He led her back to" |
| | ratio | 0.319 | |
| 97.68% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 50 | | totalSentences | 69 | | matches | | 0 | "The dead man wore no" | | 1 | "Quinn noticed it before she" | | 2 | "She'd stepped off the Tube" | | 3 | "The uniform at the top" | | 4 | "She still didn't, and she'd" | | 5 | "The victim was a man" | | 6 | "He lay on his back" | | 7 | "The soles were clean." | | 8 | "A man who'd walked no" | | 9 | "DS Farrow's voice bounced off" | | 10 | "He was crouched near the" | | 11 | "She stepped under the tape" | | 12 | "Her torch swept the space" | | 13 | "The old advertising boards on" | | 14 | "Nobody had disturbed so much" | | 15 | "Farrow checked his notes" | | 16 | "Quinn stood over the body" | | 17 | "A small scar through his" | | 18 | "His eyes were closed, which" | | 19 | "People didn't typically die with" |
| | ratio | 0.725 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 69 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 8 | | matches | | 0 | "Quinn noticed it before she noticed the body, which said something either about her instincts or her morning." | | 1 | "She'd stepped off the Tube at Camden Town with a coffee going cold in her hand, followed the flutter of police tape down two side streets, and descended into th…" | | 2 | "The uniform at the top had said *suspicious death, strange circumstances*, which was police code for *we don't understand what we're looking at*." | | 3 | "Thirty-two years old and already developing the particular posture of a man who spent too much time looking at terrible things." | | 4 | "The face was engraved, not printed, with fine symbols arranged around the cardinal points in a pattern that wasn't any cartographic convention she recognised." | | 5 | "This man looked like he'd been placed here by someone who'd taken their time, or like he'd lain down himself." | | 6 | "But the lining still held the faint rectangular impression of something that had sat there a long time." | | 7 | "In the dark, the needle glowed faint green, and it was pointing directly at the wall behind the old advertising boards, the wall that, according to any architec…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 25 | | tagDensity | 0.08 | | leniency | 0.16 | | rawRatio | 0 | | effectiveRatio | 0 | |