| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 4 | | adverbTags | | 0 | "Chen said suddenly [suddenly]" | | 1 | "Quinn said quietly [quietly]" | | 2 | "Chen protested mildly [mildly]" | | 3 | "Quinn continued quietly [quietly]" |
| | dialogueSentences | 32 | | tagDensity | 0.563 | | leniency | 1 | | rawRatio | 0.222 | | effectiveRatio | 0.222 | |
| 65.22% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1150 | | totalAiIsmAdverbs | 8 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | adverb | "deliberately" | | count | 1 |
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| | highlights | | 0 | "carefully" | | 1 | "slightly" | | 2 | "suddenly" | | 3 | "completely" | | 4 | "gently" | | 5 | "deliberately" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 69.57% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1150 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "standard" | | 1 | "weight" | | 2 | "rhythmic" | | 3 | "echoed" | | 4 | "methodical" | | 5 | "familiar" |
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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 | 102 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 102 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1136 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 26.24% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 808 | | uniqueNames | 11 | | maxNameDensity | 2.48 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Quinn | 20 | | Metropolitan | 1 | | Police | 1 | | Detective | 2 | | Sergeant | 1 | | Chen | 9 | | August | 1 | | Medieval | 1 | | London | 2 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Police" | | 2 | "Detective" | | 3 | "Sergeant" | | 4 | "Chen" | | 5 | "Morris" |
| | places | | | globalScore | 0.262 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | 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 | 1136 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 25.82 | | std | 16.82 | | cv | 0.652 | | sampleLengths | | 0 | 42 | | 1 | 42 | | 2 | 3 | | 3 | 49 | | 4 | 5 | | 5 | 37 | | 6 | 56 | | 7 | 5 | | 8 | 15 | | 9 | 28 | | 10 | 8 | | 11 | 42 | | 12 | 6 | | 13 | 7 | | 14 | 46 | | 15 | 29 | | 16 | 13 | | 17 | 7 | | 18 | 31 | | 19 | 33 | | 20 | 22 | | 21 | 9 | | 22 | 44 | | 23 | 8 | | 24 | 31 | | 25 | 8 | | 26 | 39 | | 27 | 4 | | 28 | 33 | | 29 | 41 | | 30 | 19 | | 31 | 4 | | 32 | 46 | | 33 | 24 | | 34 | 38 | | 35 | 11 | | 36 | 51 | | 37 | 46 | | 38 | 4 | | 39 | 42 | | 40 | 8 | | 41 | 55 | | 42 | 20 | | 43 | 25 |
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| 84.62% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 102 | | matches | | 0 | "been reported" | | 1 | "been closed" | | 2 | "was supposed" | | 3 | "were torn" | | 4 | "been pulled" | | 5 | "been sanitized" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 145 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 115 | | ratio | 0.07 | | matches | | 0 | "The Metropolitan Police had cordoned off the platform with tape—bright yellow against the decay." | | 1 | "Her torch beam caught something—the surrounding tissue showed no bruising, no defensive marks on the hands." | | 2 | "Someone had oiled these hinges recently—the movement was smooth despite the corrosion." | | 3 | "Inside breast pocket—empty." | | 4 | "A sound drifted from the open shutter—water dripping deeper in the tunnel." | | 5 | "Not defensive wounds—the damage was too uniform, too methodical." | | 6 | "\"Closed to the public in 1941 during the Blitz. Reopened briefly in '46 for a few months, then sealed permanently. Too unstable. The tunnels beneath it—\" Chen consulted his notebook, \"—supposedly lead to medieval catacombs. London's old burial grounds.\"" | | 7 | "Fibres caught on the rust—dark, organic matter." |
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| 93.68% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 720 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.04722222222222222 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.016666666666666666 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 9.88 | | std | 7.23 | | cv | 0.732 | | sampleLengths | | 0 | 10 | | 1 | 18 | | 2 | 14 | | 3 | 11 | | 4 | 2 | | 5 | 7 | | 6 | 7 | | 7 | 4 | | 8 | 11 | | 9 | 3 | | 10 | 10 | | 11 | 6 | | 12 | 2 | | 13 | 16 | | 14 | 6 | | 15 | 9 | | 16 | 5 | | 17 | 10 | | 18 | 11 | | 19 | 16 | | 20 | 14 | | 21 | 2 | | 22 | 16 | | 23 | 10 | | 24 | 14 | | 25 | 5 | | 26 | 15 | | 27 | 19 | | 28 | 9 | | 29 | 2 | | 30 | 6 | | 31 | 4 | | 32 | 10 | | 33 | 8 | | 34 | 12 | | 35 | 8 | | 36 | 6 | | 37 | 7 | | 38 | 11 | | 39 | 9 | | 40 | 7 | | 41 | 5 | | 42 | 5 | | 43 | 9 | | 44 | 5 | | 45 | 5 | | 46 | 3 | | 47 | 16 | | 48 | 7 | | 49 | 6 |
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| 84.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5304347826086957 | | totalSentences | 115 | | uniqueOpeners | 61 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 88 | | matches | | 0 | "Of course it wasn't." | | 1 | "Then she turned back to" | | 2 | "Instead, she marked the shutter" |
| | ratio | 0.034 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 88 | | matches | | 0 | "She crouched beside the corpse," | | 1 | "Her torch beam caught something—the" | | 2 | "It hung partially open, rust" | | 3 | "She swept her torch into" | | 4 | "His shoes bore fresh polish." | | 5 | "His fingernails were clean, manicured." | | 6 | "She patted the jacket carefully." | | 7 | "She photographed the discoloration with" | | 8 | "It was darker, almost mineral." | | 9 | "She stood and photographed his" | | 10 | "She bagged them." | | 11 | "she said, not turning from" | | 12 | "She glanced back at the" | | 13 | "She'd lost her partner, Morris," | | 14 | "She didn't look back as" | | 15 | "She reached ground level and" |
| | ratio | 0.182 | |
| 67.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 88 | | matches | | 0 | "The abandoned Tube station smelled" | | 1 | "Detective Quinn descended the concrete" | | 2 | "The Metropolitan Police had cordoned" | | 3 | "The body lay on the" | | 4 | "Standard mugging, the initial report" | | 5 | "A vagrant killed for whatever" | | 6 | "Quinn's jaw tightened." | | 7 | "She crouched beside the corpse," | | 8 | "The blade entry points were" | | 9 | "Her torch beam caught something—the" | | 10 | "This man had stood still." | | 11 | "a voice called from across" | | 12 | "Detective Sergeant Chen emerged from" | | 13 | "Quinn rose, her worn leather" | | 14 | "The body had been reported" | | 15 | "The station had been closed" | | 16 | "Quinn gestured to the iron" | | 17 | "It hung partially open, rust" | | 18 | "The responding officers had seen" | | 19 | "Quinn pulled the shutter wider," |
| | ratio | 0.784 | |
| 56.82% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 88 | | matches | | 0 | "Even drunk, even surprised, the" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Quinn descended the concrete stairs with her torch cutting through darkness that hadn't seen daylight in decades." |
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| 97.22% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, not turning from the symbols" |
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| 56.25% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 3 | | fancyTags | | 0 | "Quinn muttered (mutter)" | | 1 | "Chen protested mildly (protest)" | | 2 | "Quinn continued quietly (continue)" |
| | dialogueSentences | 32 | | tagDensity | 0.344 | | leniency | 0.688 | | rawRatio | 0.273 | | effectiveRatio | 0.188 | |