| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.439 | | leniency | 0.878 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.76% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1179 | | totalAiIsmAdverbs | 1 | | 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) | |
| 57.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1179 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "wavering" | | 1 | "tension" | | 2 | "pristine" | | 3 | "silk" | | 4 | "echoed" | | 5 | "stark" | | 6 | "glint" | | 7 | "flickered" | | 8 | "etched" | | 9 | "magnetic" |
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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 | 78 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1179 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 54.42% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 44 | | wordCount | 837 | | uniqueNames | 9 | | maxNameDensity | 1.91 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Frank | 1 | | Briggs | 14 | | Quinn | 16 | | Cold | 1 | | Rotherhithe | 1 | | Morris | 1 | | Kowalski | 1 | | Eva | 7 |
| | persons | | 0 | "Frank" | | 1 | "Briggs" | | 2 | "Quinn" | | 3 | "Morris" | | 4 | "Kowalski" | | 5 | "Eva" |
| | places | | | globalScore | 0.544 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | 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 | 1179 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 24.06 | | std | 15.34 | | cv | 0.637 | | sampleLengths | | 0 | 11 | | 1 | 37 | | 2 | 23 | | 3 | 47 | | 4 | 15 | | 5 | 5 | | 6 | 8 | | 7 | 8 | | 8 | 31 | | 9 | 51 | | 10 | 16 | | 11 | 28 | | 12 | 25 | | 13 | 5 | | 14 | 56 | | 15 | 51 | | 16 | 27 | | 17 | 11 | | 18 | 38 | | 19 | 22 | | 20 | 9 | | 21 | 3 | | 22 | 30 | | 23 | 33 | | 24 | 20 | | 25 | 61 | | 26 | 21 | | 27 | 21 | | 28 | 10 | | 29 | 19 | | 30 | 15 | | 31 | 21 | | 32 | 22 | | 33 | 10 | | 34 | 4 | | 35 | 54 | | 36 | 14 | | 37 | 35 | | 38 | 10 | | 39 | 42 | | 40 | 23 | | 41 | 24 | | 42 | 43 | | 43 | 5 | | 44 | 14 | | 45 | 28 | | 46 | 15 | | 47 | 11 | | 48 | 47 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 78 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 136 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 101 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 842 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.020190023752969122 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.011876484560570071 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 11.67 | | std | 6.81 | | cv | 0.583 | | sampleLengths | | 0 | 11 | | 1 | 20 | | 2 | 17 | | 3 | 23 | | 4 | 22 | | 5 | 25 | | 6 | 6 | | 7 | 9 | | 8 | 5 | | 9 | 8 | | 10 | 8 | | 11 | 5 | | 12 | 14 | | 13 | 12 | | 14 | 26 | | 15 | 16 | | 16 | 9 | | 17 | 10 | | 18 | 6 | | 19 | 15 | | 20 | 1 | | 21 | 1 | | 22 | 11 | | 23 | 7 | | 24 | 14 | | 25 | 4 | | 26 | 5 | | 27 | 16 | | 28 | 12 | | 29 | 8 | | 30 | 4 | | 31 | 16 | | 32 | 16 | | 33 | 11 | | 34 | 10 | | 35 | 14 | | 36 | 27 | | 37 | 8 | | 38 | 3 | | 39 | 10 | | 40 | 13 | | 41 | 3 | | 42 | 12 | | 43 | 19 | | 44 | 3 | | 45 | 6 | | 46 | 3 | | 47 | 3 | | 48 | 13 | | 49 | 17 |
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| 61.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.42574257425742573 | | totalSentences | 101 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 70 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 70 | | matches | | 0 | "She knelt, the knee of" | | 1 | "It clung to the sharp" | | 2 | "She checked the worn leather" | | 3 | "Her fingernails caught on a" | | 4 | "She pinched the object inside" | | 5 | "It was a sliver of" | | 6 | "Her voice was thin, but" | | 7 | "She slipped the weapon back" | | 8 | "She sucked in a sharp" | | 9 | "She turned her torch toward" | | 10 | "She pushed the victim's hip" | | 11 | "It spun in wild, erratic" | | 12 | "She tucked her hair behind" |
| | ratio | 0.186 | |
| 17.14% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 70 | | matches | | 0 | "The dead man's fingers gripped" | | 1 | "Quinn dropped through the maintenance" | | 2 | "Decades of tunnel grime rose" | | 3 | "Briggs said, gesturing with his" | | 4 | "Quinn clicked her own torch" | | 5 | "The white beam pinned the" | | 6 | "Briggs frowned, the beam of" | | 7 | "She knelt, the knee of" | | 8 | "It clung to the sharp" | | 9 | "The outer edge stopped abruptly," | | 10 | "Briggs offered, though his tone" | | 11 | "Quinn touched the fabric of" | | 12 | "The dead man wore polished" | | 13 | "The leather held no dust," | | 14 | "The soles were pristine." | | 15 | "Quinn leaned closer to the" | | 16 | "The skin along the sternum" | | 17 | "A bitter draught pulled through" | | 18 | "The fine hairs on Quinn's" | | 19 | "She checked the worn leather" |
| | ratio | 0.886 | |
| 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 | 41 | | technicalSentenceCount | 2 | | matches | | 0 | "Decades of tunnel grime rose in a foul, powdery cloud that tasted of rust and dried grease." | | 1 | "A heavy patina of verdigris crusted its rim, yet the protective sigils etched into the face gleamed as if freshly cut." |
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| 97.22% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 1 | | matches | | 0 | "Briggs offered, though his tone lost its certainty" |
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| 52.44% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 4 | | fancyTags | | 0 | "Briggs muttered (mutter)" | | 1 | "Briggs snapped (snap)" | | 2 | "Eva whispered (whisper)" | | 3 | "Quinn murmured (murmur)" |
| | dialogueSentences | 41 | | tagDensity | 0.317 | | leniency | 0.634 | | rawRatio | 0.308 | | effectiveRatio | 0.195 | |