| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.135 | | leniency | 0.27 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.76% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 890 | | 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) | |
| 49.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 890 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "gloom" | | 1 | "unreadable" | | 2 | "pristine" | | 3 | "scanning" | | 4 | "flickered" | | 5 | "etched" | | 6 | "standard" | | 7 | "eyebrow" | | 8 | "racing" |
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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 | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 82 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 5 | | totalWords | 884 | | ratio | 0.006 | | matches | | 0 | "Camden Town. Mind the gap." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 55.21% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 30 | | wordCount | 633 | | uniqueNames | 6 | | maxNameDensity | 1.9 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Cole" | | discoveredNames | | Tube | 2 | | Town | 1 | | Sergeant | 1 | | Cole | 11 | | Quinn | 12 | | Eva | 3 |
| | persons | | 0 | "Sergeant" | | 1 | "Cole" | | 2 | "Quinn" | | 3 | "Eva" |
| | places | | | globalScore | 0.552 | | windowScore | 0.667 | |
| 47.96% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 49 | | glossingSentenceCount | 2 | | matches | | 0 | "as if reaching for something" | | 1 | "looked like dried blood" |
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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 | 884 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 113 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 17.68 | | std | 12.88 | | cv | 0.729 | | sampleLengths | | 0 | 39 | | 1 | 7 | | 2 | 12 | | 3 | 6 | | 4 | 43 | | 5 | 20 | | 6 | 5 | | 7 | 33 | | 8 | 45 | | 9 | 9 | | 10 | 8 | | 11 | 38 | | 12 | 9 | | 13 | 14 | | 14 | 9 | | 15 | 46 | | 16 | 9 | | 17 | 17 | | 18 | 10 | | 19 | 55 | | 20 | 6 | | 21 | 13 | | 22 | 14 | | 23 | 29 | | 24 | 8 | | 25 | 45 | | 26 | 26 | | 27 | 10 | | 28 | 20 | | 29 | 24 | | 30 | 23 | | 31 | 6 | | 32 | 15 | | 33 | 25 | | 34 | 20 | | 35 | 14 | | 36 | 10 | | 37 | 9 | | 38 | 17 | | 39 | 7 | | 40 | 11 | | 41 | 12 | | 42 | 4 | | 43 | 12 | | 44 | 4 | | 45 | 7 | | 46 | 14 | | 47 | 28 | | 48 | 8 | | 49 | 9 |
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| 88.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 82 | | matches | | 0 | "was frozen" | | 1 | "were covered" | | 2 | "were polished" | | 3 | "was etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 110 | | matches | | |
| 41.72% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 113 | | ratio | 0.035 | | matches | | 0 | "The fabric was pristine—no tears, no dirt, no scuffs." | | 1 | "But one symbol stood out—a circle with a jagged line through it, painted in what looked like dried blood." | | 2 | "Then she saw it—a small, brass object half-buried in the dirt." | | 3 | "But one thing was clear—this wasn’t a mugging." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 637 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.03296703296703297 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.01098901098901099 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 7.82 | | std | 4.52 | | cv | 0.578 | | sampleLengths | | 0 | 10 | | 1 | 13 | | 2 | 16 | | 3 | 7 | | 4 | 9 | | 5 | 3 | | 6 | 6 | | 7 | 16 | | 8 | 16 | | 9 | 2 | | 10 | 9 | | 11 | 13 | | 12 | 7 | | 13 | 5 | | 14 | 14 | | 15 | 7 | | 16 | 12 | | 17 | 5 | | 18 | 21 | | 19 | 14 | | 20 | 3 | | 21 | 2 | | 22 | 2 | | 23 | 7 | | 24 | 2 | | 25 | 6 | | 26 | 7 | | 27 | 9 | | 28 | 4 | | 29 | 7 | | 30 | 11 | | 31 | 9 | | 32 | 8 | | 33 | 6 | | 34 | 5 | | 35 | 4 | | 36 | 6 | | 37 | 13 | | 38 | 19 | | 39 | 3 | | 40 | 5 | | 41 | 4 | | 42 | 5 | | 43 | 14 | | 44 | 3 | | 45 | 3 | | 46 | 7 | | 47 | 6 | | 48 | 9 | | 49 | 4 |
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| 63.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.3893805309734513 | | totalSentences | 113 | | uniqueOpeners | 44 | |
| 47.62% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 70 | | matches | | 0 | "Then she saw it—a small," |
| | ratio | 0.014 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 70 | | matches | | 0 | "She followed the beam of" | | 1 | "He jerked his chin toward" | | 2 | "His face was frozen in" | | 3 | "She unzipped it carefully." | | 4 | "She stood, brushing her hands" | | 5 | "She stepped closer." | | 6 | "She pulled out her phone," | | 7 | "His trousers were pressed." | | 8 | "She crouched again, this time" | | 9 | "She walked to the edge" | | 10 | "Her breath misted in front" | | 11 | "She stepped down onto the" | | 12 | "She picked it up, brushing" | | 13 | "She slipped it into her" | | 14 | "She looked back at the" | | 15 | "She found the number she" | | 16 | "She turned back to the" |
| | ratio | 0.243 | |
| 38.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 59 | | totalSentences | 70 | | matches | | 0 | "The abandoned Tube station smelled" | | 1 | "Quinn stepped over the police" | | 2 | "A uniformed officer nodded at" | | 3 | "Quinn adjusted the worn leather" | | 4 | "She followed the beam of" | | 5 | "The station’s old signs still" | | 6 | "Detective Sergeant Cole, her current" | | 7 | "He jerked his chin toward" | | 8 | "Quinn crouched beside the corpse." | | 9 | "The man lay sprawled on" | | 10 | "His face was frozen in" | | 11 | "Quinn’s fingers hovered over the" | | 12 | "The fabric was pristine—no tears," | | 13 | "She unzipped it carefully." | | 14 | "The shirt beneath was just" | | 15 | "She stood, brushing her hands" | | 16 | "Cole exhaled through his nose." | | 17 | "Quinn ignored him, scanning the" | | 18 | "The walls were covered in" | | 19 | "She stepped closer." |
| | ratio | 0.843 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 1 | | matches | | 0 | "The man lay sprawled on his back, one arm twisted beneath him, the other flung out as if reaching for something." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn stood, her knees popping" | | 1 | "She stepped, her boots crunching on gravel" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.027 | | leniency | 0.054 | | rawRatio | 0 | | effectiveRatio | 0 | |