| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 2 | | adverbTags | | 0 | "Carver moved aside [aside]" | | 1 | "She turned back [back]" |
| | dialogueSentences | 40 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0.167 | | effectiveRatio | 0.1 | |
| 90.55% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1058 | | 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) | |
| 85.82% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1058 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "remarkable" | | 1 | "traced" | | 2 | "tinged" |
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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 | 70 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 70 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 97 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1055 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 0 | | matches | (empty) | |
| 79.58% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 639 | | uniqueNames | 6 | | maxNameDensity | 1.41 | | worstName | "Carver" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Carver" | | discoveredNames | | Quinn | 8 | | Tube | 1 | | Camden | 1 | | High | 1 | | Street | 1 | | Carver | 9 |
| | persons | | | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.796 | | windowScore | 0.833 | |
| 91.86% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1055 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 97 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 21.98 | | std | 23.09 | | cv | 1.05 | | sampleLengths | | 0 | 28 | | 1 | 79 | | 2 | 8 | | 3 | 62 | | 4 | 12 | | 5 | 3 | | 6 | 17 | | 7 | 65 | | 8 | 3 | | 9 | 13 | | 10 | 45 | | 11 | 8 | | 12 | 2 | | 13 | 18 | | 14 | 74 | | 15 | 7 | | 16 | 25 | | 17 | 6 | | 18 | 6 | | 19 | 53 | | 20 | 8 | | 21 | 23 | | 22 | 6 | | 23 | 43 | | 24 | 6 | | 25 | 4 | | 26 | 3 | | 27 | 26 | | 28 | 46 | | 29 | 6 | | 30 | 7 | | 31 | 4 | | 32 | 34 | | 33 | 60 | | 34 | 1 | | 35 | 8 | | 36 | 11 | | 37 | 45 | | 38 | 4 | | 39 | 18 | | 40 | 4 | | 41 | 12 | | 42 | 5 | | 43 | 82 | | 44 | 5 | | 45 | 3 | | 46 | 43 | | 47 | 4 |
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| 95.24% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 70 | | matches | | 0 | "been told" | | 1 | "was darkened" |
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| 11.32% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 106 | | matches | | 0 | "was sitting" | | 1 | "was crouching" | | 2 | "was watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 97 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 642 | | adjectiveStacks | 1 | | stackExamples | | 0 | "broad, sandy-haired man" |
| | adverbCount | 23 | | adverbRatio | 0.03582554517133956 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.00778816199376947 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 97 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 97 | | mean | 10.88 | | std | 9.21 | | cv | 0.847 | | sampleLengths | | 0 | 12 | | 1 | 10 | | 2 | 4 | | 3 | 2 | | 4 | 18 | | 5 | 28 | | 6 | 23 | | 7 | 3 | | 8 | 7 | | 9 | 8 | | 10 | 20 | | 11 | 7 | | 12 | 30 | | 13 | 2 | | 14 | 2 | | 15 | 1 | | 16 | 12 | | 17 | 3 | | 18 | 7 | | 19 | 7 | | 20 | 3 | | 21 | 21 | | 22 | 20 | | 23 | 24 | | 24 | 3 | | 25 | 13 | | 26 | 2 | | 27 | 23 | | 28 | 5 | | 29 | 4 | | 30 | 11 | | 31 | 8 | | 32 | 2 | | 33 | 18 | | 34 | 8 | | 35 | 9 | | 36 | 18 | | 37 | 9 | | 38 | 19 | | 39 | 4 | | 40 | 7 | | 41 | 7 | | 42 | 25 | | 43 | 6 | | 44 | 6 | | 45 | 29 | | 46 | 9 | | 47 | 15 | | 48 | 8 | | 49 | 10 |
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| 83.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5257731958762887 | | totalSentences | 97 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 95.86% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 58 | | matches | | 0 | "She stepped under the cordon" | | 1 | "He was sitting upright against" | | 2 | "She'd stood outside for three" | | 3 | "He was a broad, sandy-haired" | | 4 | "She pulled her phone out" | | 5 | "She pulled out her worn" | | 6 | "He moved to the victim's" | | 7 | "He hadn't walked to the" | | 8 | "She pulled on a glove" | | 9 | "She photographed it with her" | | 10 | "She looked up at him." | | 11 | "she said, She stood and" | | 12 | "she said, over him" | | 13 | "She angled her torch so" | | 14 | "She peeled her glove off" | | 15 | "She turned back to the" | | 16 | "Her phone buzzed." | | 17 | "She answered it." |
| | ratio | 0.31 | |
| 46.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 58 | | matches | | 0 | "The smell hit Quinn before" | | 1 | "The abandoned Tube station yawned" | | 2 | "Quinn noted that." | | 3 | "Men who'd seen bodies before," | | 4 | "She stepped under the cordon" | | 5 | "The victim was a man" | | 6 | "He was sitting upright against" | | 7 | "DS Carver was crouching nearby," | | 8 | "Quinn said, which was not" | | 9 | "She'd stood outside for three" | | 10 | "He was a broad, sandy-haired" | | 11 | "Carver moved aside to let" | | 12 | "The man's hands in his" | | 13 | "The skin there was darkened." | | 14 | "Whatever had burned him had" | | 15 | "Quinn looked at the floor" | | 16 | "Dust and old grit, thick" | | 17 | "She pulled her phone out" | | 18 | "Footprints, several sets, circling the" | | 19 | "Something had rested there." |
| | ratio | 0.828 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 1 | | matches | | 0 | "He was a broad, sandy-haired man with the permanent expression of someone who'd just been told the meeting ran long." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.175 | | leniency | 0.35 | | rawRatio | 0 | | effectiveRatio | 0 | |