| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "The needle spun wildly [wildly]" |
| | dialogueSentences | 21 | | tagDensity | 0.381 | | leniency | 0.762 | | rawRatio | 0.125 | | effectiveRatio | 0.095 | |
| 85.57% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 693 | | 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) | |
| 49.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 693 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "pristine" | | 1 | "traced" | | 2 | "footsteps" | | 3 | "echoing" | | 4 | "etched" | | 5 | "intricate" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 33 | | matches | (empty) | |
| 0.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 33 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "started to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 46 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 696 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 44.12% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 19 | | wordCount | 425 | | uniqueNames | 8 | | maxNameDensity | 2.12 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 9 | | Tube | 1 | | Matthews | 3 | | Art | 1 | | Deco | 1 | | Morris | 2 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Matthews" | | 3 | "Art" | | 4 | "Deco" | | 5 | "Morris" |
| | places | | | globalScore | 0.441 | | windowScore | 0.833 | |
| 66.67% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 30 | | glossingSentenceCount | 1 | | matches | | 0 | "patterns that seemed to shift in the harsh crime scene lighting" |
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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 | 696 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 46 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 31.64 | | std | 16.57 | | cv | 0.524 | | sampleLengths | | 0 | 56 | | 1 | 41 | | 2 | 36 | | 3 | 30 | | 4 | 12 | | 5 | 31 | | 6 | 48 | | 7 | 37 | | 8 | 11 | | 9 | 5 | | 10 | 38 | | 11 | 44 | | 12 | 20 | | 13 | 10 | | 14 | 35 | | 15 | 3 | | 16 | 36 | | 17 | 39 | | 18 | 43 | | 19 | 10 | | 20 | 62 | | 21 | 49 |
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| 73.37% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 33 | | matches | | 0 | "been removed" | | 1 | "was covered" | | 2 | "been found" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 70 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 46 | | ratio | 0.087 | | matches | | 0 | "The body had been removed hours ago, but the scene remained pristine - too pristine." | | 1 | "The station's original features remained intact - beautiful Art Deco tiles, brass fixtures green with age, ornate iron pillars." | | 2 | "\"When was the last time you saw a compass with markings like these?\" The needle spun wildly, then settled pointing toward the darkened tunnel - not north." | | 3 | "Quinn pocketed the paper, her hand brushing against her watch - the same watch she'd been wearing three years ago when Morris disappeared." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 423 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 6 | | adverbRatio | 0.014184397163120567 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.009456264775413711 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 46 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 46 | | mean | 15.13 | | std | 8.62 | | cv | 0.57 | | sampleLengths | | 0 | 20 | | 1 | 15 | | 2 | 21 | | 3 | 11 | | 4 | 30 | | 5 | 29 | | 6 | 3 | | 7 | 4 | | 8 | 30 | | 9 | 12 | | 10 | 10 | | 11 | 21 | | 12 | 11 | | 13 | 19 | | 14 | 18 | | 15 | 10 | | 16 | 27 | | 17 | 11 | | 18 | 4 | | 19 | 1 | | 20 | 27 | | 21 | 11 | | 22 | 8 | | 23 | 10 | | 24 | 14 | | 25 | 12 | | 26 | 12 | | 27 | 8 | | 28 | 7 | | 29 | 3 | | 30 | 35 | | 31 | 3 | | 32 | 19 | | 33 | 17 | | 34 | 7 | | 35 | 21 | | 36 | 11 | | 37 | 22 | | 38 | 21 | | 39 | 10 | | 40 | 9 | | 41 | 18 | | 42 | 35 | | 43 | 14 | | 44 | 23 | | 45 | 12 |
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| 87.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.5652173913043478 | | totalSentences | 46 | | uniqueOpeners | 26 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 32 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 7 | | totalSentences | 32 | | matches | | 0 | "Her worn leather watch caught" | | 1 | "She paced the platform, her" | | 2 | "She handed it to a" | | 3 | "His last case had led" | | 4 | "They'd found his body three" | | 5 | "She walked to where the" | | 6 | "Their eyes met briefly before" |
| | ratio | 0.219 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 28 | | totalSentences | 32 | | matches | | 0 | "Detective Harlow Quinn knelt beside" | | 1 | "The body had been removed" | | 2 | "DS Matthews flipped through his" | | 3 | "Quinn traced her finger along" | | 4 | "Her worn leather watch caught" | | 5 | "Quinn's eyes narrowed at the" | | 6 | "She paced the platform, her" | | 7 | "The station's original features remained" | | 8 | "Quinn pulled on a latex" | | 9 | "The casing was covered in" | | 10 | "She handed it to a" | | 11 | "The needle spun wildly, then" | | 12 | "Quinn's mind flashed to her" | | 13 | "His last case had led" | | 14 | "They'd found his body three" | | 15 | "The official report cited a" | | 16 | "She walked to where the" | | 17 | "Matthews joined her, squinting at" | | 18 | "Quinn pulled out her phone," | | 19 | "A flash of red caught" |
| | ratio | 0.875 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 32 | | matches | (empty) | | ratio | 0 | |
| 67.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 19 | | technicalSentenceCount | 2 | | matches | | 0 | "The casing was covered in strange markings she'd never seen before, its face etched with intricate patterns that seemed to shift in the harsh crime scene lighti…" | | 1 | "Quinn pocketed the paper, her hand brushing against her watch - the same watch she'd been wearing three years ago when Morris disappeared." |
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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 | |