| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 24 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 98 | | tagDensity | 0.245 | | leniency | 0.49 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 89.29% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2335 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slowly" | | 1 | "sharply" | | 2 | "really" | | 3 | "very" |
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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) | |
| 80.73% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2335 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "etched" | | 1 | "weight" | | 2 | "velvet" | | 3 | "grave" | | 4 | "whisper" | | 5 | "gloom" | | 6 | "pulse" | | 7 | "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 | 199 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 199 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 271 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2335 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 79 | | wordCount | 1605 | | uniqueNames | 13 | | maxNameDensity | 1.93 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 31 | | Shaftesbury | 1 | | Avenue | 1 | | Detective | 2 | | Inspector | 1 | | Lyle | 1 | | Benton | 25 | | Cracked | 1 | | Underground | 1 | | Veil | 1 | | Market | 1 | | Harlow | 10 | | One | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Inspector" | | 2 | "Lyle" | | 3 | "Benton" | | 4 | "Market" | | 5 | "Harlow" | | 6 | "One" |
| | places | | | globalScore | 0.534 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 109 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared behind her, one hand already on his torch" |
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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 | 2335 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 271 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 133 | | mean | 17.56 | | std | 16.63 | | cv | 0.947 | | sampleLengths | | 0 | 8 | | 1 | 52 | | 2 | 4 | | 3 | 73 | | 4 | 26 | | 5 | 2 | | 6 | 5 | | 7 | 43 | | 8 | 20 | | 9 | 21 | | 10 | 45 | | 11 | 65 | | 12 | 19 | | 13 | 5 | | 14 | 33 | | 15 | 7 | | 16 | 34 | | 17 | 26 | | 18 | 5 | | 19 | 3 | | 20 | 18 | | 21 | 9 | | 22 | 26 | | 23 | 5 | | 24 | 32 | | 25 | 25 | | 26 | 11 | | 27 | 2 | | 28 | 15 | | 29 | 9 | | 30 | 29 | | 31 | 6 | | 32 | 3 | | 33 | 53 | | 34 | 5 | | 35 | 36 | | 36 | 14 | | 37 | 5 | | 38 | 33 | | 39 | 9 | | 40 | 9 | | 41 | 4 | | 42 | 6 | | 43 | 45 | | 44 | 6 | | 45 | 38 | | 46 | 6 | | 47 | 22 | | 48 | 6 | | 49 | 6 |
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| 98.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 199 | | matches | | 0 | "been carved" | | 1 | "been disturbed" | | 2 | "been bolted" | | 3 | "been yanked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 258 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 271 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1611 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 42 | | adverbRatio | 0.0260707635009311 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.006828057107386716 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 271 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 271 | | mean | 8.62 | | std | 7.01 | | cv | 0.813 | | sampleLengths | | 0 | 8 | | 1 | 18 | | 2 | 19 | | 3 | 8 | | 4 | 4 | | 5 | 3 | | 6 | 4 | | 7 | 17 | | 8 | 23 | | 9 | 8 | | 10 | 10 | | 11 | 15 | | 12 | 26 | | 13 | 2 | | 14 | 5 | | 15 | 19 | | 16 | 17 | | 17 | 7 | | 18 | 20 | | 19 | 14 | | 20 | 7 | | 21 | 22 | | 22 | 23 | | 23 | 8 | | 24 | 20 | | 25 | 13 | | 26 | 4 | | 27 | 3 | | 28 | 17 | | 29 | 19 | | 30 | 5 | | 31 | 7 | | 32 | 6 | | 33 | 8 | | 34 | 5 | | 35 | 7 | | 36 | 7 | | 37 | 4 | | 38 | 30 | | 39 | 7 | | 40 | 13 | | 41 | 2 | | 42 | 3 | | 43 | 1 | | 44 | 5 | | 45 | 3 | | 46 | 14 | | 47 | 4 | | 48 | 5 | | 49 | 4 |
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| 51.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.33579335793357934 | | totalSentences | 271 | | uniqueOpeners | 91 | |
| 60.24% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 166 | | matches | | 0 | "Instead, the blood had pooled" | | 1 | "Then the marks changed." | | 2 | "Then the chamber answered with" |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 166 | | matches | | 0 | "He had a constable’s hat" | | 1 | "She ducked beneath the tape" | | 2 | "He had the look of" | | 3 | "He lifted his chin towards" | | 4 | "His throat had been cut" | | 5 | "She looked at the compass." | | 6 | "It sat hard against the" | | 7 | "She pointed to the wrists" | | 8 | "He closed his notebook." | | 9 | "It had been carved with" | | 10 | "She watched the light catch" | | 11 | "She stood and dusted chalk" | | 12 | "She moved to the altar" | | 13 | "She pulled the curtain aside." | | 14 | "He shone the beam across" | | 15 | "It shifted inward with a" | | 16 | "She took the torch from" | | 17 | "He angled the light." | | 18 | "They went on." | | 19 | "They watched the tunnel entrance" |
| | ratio | 0.175 | |
| 62.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 132 | | totalSentences | 166 | | matches | | 0 | "Harlow Quinn flattened a gloved" | | 1 | "He had a constable’s hat" | | 2 | "Blood darkened the cracked tiles" | | 3 | "She ducked beneath the tape" | | 4 | "The place sat under a" | | 5 | "Someone had once painted saints" | | 6 | "Time and damp had worried" | | 7 | "The dead man lay on" | | 8 | "Detective Inspector Lyle Benton stood" | | 9 | "He had the look of" | | 10 | "He lifted his chin towards" | | 11 | "Quinn stepped closer, her shoes" | | 12 | "Benton nodded towards the brass" | | 13 | "Quinn crouched beside the body" | | 14 | "The man wore a navy" | | 15 | "His throat had been cut" | | 16 | "A ritual cut." | | 17 | "She looked at the compass." | | 18 | "Brass, small, worn smooth at" | | 19 | "Verdigris clung to the casing" |
| | ratio | 0.795 | |
| 30.12% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 166 | | matches | | 0 | "Now the only clean thing" |
| | ratio | 0.006 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 1 | | matches | | 0 | "Then the chamber answered with a low, deep thrum from the brickwork itself, as if a giant fist had struck the tunnel wall from the other side." |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 24 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn stepped, her shoes whispering over grit and old wax" | | 1 | "She let, the table, the compass in Quinn’s hand" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 3 | | fancyTags | | 0 | "Benton murmured (murmur)" | | 1 | "Quinn pressed (press)" | | 2 | "Benton muttered (mutter)" |
| | dialogueSentences | 98 | | tagDensity | 0.122 | | leniency | 0.245 | | rawRatio | 0.25 | | effectiveRatio | 0.061 | |