| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 78.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1170 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "sharply" | | 1 | "slowly" | | 2 | "slightly" | | 3 | "completely" |
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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) | |
| 61.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1170 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "reminder" | | 1 | "tracing" | | 2 | "footsteps" | | 3 | "echoing" | | 4 | "depths" | | 5 | "vibrated" | | 6 | "resolved" | | 7 | "weight" |
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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 | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 81 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 92 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1169 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1109 | | uniqueNames | 16 | | maxNameDensity | 0.9 | | worstName | "Herrera" | | maxWindowNameDensity | 2 | | worstWindowName | "Herrera" | | discoveredNames | | Harlow | 9 | | Herrera | 10 | | Saint | 1 | | Christopher | 2 | | October | 1 | | Spaniard | 1 | | Victorian | 1 | | Camden | 1 | | Tube | 1 | | Metropolitan | 1 | | Police | 1 | | Veil | 1 | | Market | 1 | | Crown | 1 | | Morris | 3 | | St | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Spaniard" | | 4 | "Victorian" | | 5 | "Market" | | 6 | "Crown" | | 7 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like an old substation entrance, r" |
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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 | 1169 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 92 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 26.57 | | std | 21.44 | | cv | 0.807 | | sampleLengths | | 0 | 26 | | 1 | 50 | | 2 | 72 | | 3 | 2 | | 4 | 13 | | 5 | 46 | | 6 | 62 | | 7 | 34 | | 8 | 19 | | 9 | 6 | | 10 | 45 | | 11 | 31 | | 12 | 7 | | 13 | 31 | | 14 | 11 | | 15 | 8 | | 16 | 39 | | 17 | 7 | | 18 | 28 | | 19 | 10 | | 20 | 76 | | 21 | 12 | | 22 | 39 | | 23 | 5 | | 24 | 41 | | 25 | 59 | | 26 | 10 | | 27 | 1 | | 28 | 11 | | 29 | 51 | | 30 | 8 | | 31 | 6 | | 32 | 6 | | 33 | 64 | | 34 | 14 | | 35 | 5 | | 36 | 5 | | 37 | 27 | | 38 | 31 | | 39 | 71 | | 40 | 30 | | 41 | 23 | | 42 | 22 | | 43 | 5 |
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| 96.60% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 81 | | matches | | 0 | "was carved" | | 1 | "was cleared" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 172 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 92 | | ratio | 0.011 | | matches | | 0 | "The sleeve of his jacket pulled back, revealing the jagged scar running along his left forearm—a pale reminder of a knife attack that had ended his medical career." |
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| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1118 | | adjectiveStacks | 3 | | stackExamples | | 0 | "desperate, loose-limbed gait." | | 1 | "warm, amber-lit cavern" | | 2 | "small, heavy silver coin" |
| | adverbCount | 25 | | adverbRatio | 0.022361359570661897 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004472271914132379 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 92 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 92 | | mean | 12.71 | | std | 6.72 | | cv | 0.529 | | sampleLengths | | 0 | 11 | | 1 | 15 | | 2 | 8 | | 3 | 21 | | 4 | 21 | | 5 | 4 | | 6 | 15 | | 7 | 4 | | 8 | 4 | | 9 | 18 | | 10 | 9 | | 11 | 18 | | 12 | 2 | | 13 | 13 | | 14 | 5 | | 15 | 16 | | 16 | 12 | | 17 | 13 | | 18 | 6 | | 19 | 7 | | 20 | 11 | | 21 | 28 | | 22 | 10 | | 23 | 21 | | 24 | 13 | | 25 | 3 | | 26 | 16 | | 27 | 6 | | 28 | 19 | | 29 | 9 | | 30 | 17 | | 31 | 9 | | 32 | 22 | | 33 | 7 | | 34 | 11 | | 35 | 14 | | 36 | 6 | | 37 | 11 | | 38 | 8 | | 39 | 6 | | 40 | 13 | | 41 | 20 | | 42 | 7 | | 43 | 6 | | 44 | 22 | | 45 | 10 | | 46 | 12 | | 47 | 17 | | 48 | 24 | | 49 | 23 |
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| 43.84% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.31521739130434784 | | totalSentences | 92 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 61.98% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 81 | | matches | | 0 | "She ignored the sting, keeping" | | 1 | "He skidded around a stack" | | 2 | "She did not slip." | | 3 | "She did not slow." | | 4 | "Her salt-and-pepper hair clung to" | | 5 | "She reached down, checking her" | | 6 | "Her voice cut through the" | | 7 | "He veered left, darting into" | | 8 | "Her boots clattered against the" | | 9 | "He recovered his footing with" | | 10 | "It looked like an old" | | 11 | "He fell to his knees," | | 12 | "Her chest rose and fell" | | 13 | "Her military background anchored her," | | 14 | "He reached into his coat" | | 15 | "He held a small, polished" | | 16 | "It was carved in the" | | 17 | "He stepped backward into the" | | 18 | "His boots found a flight" | | 19 | "It was the rhythm of" |
| | ratio | 0.395 | |
| 21.73% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 81 | | matches | | 0 | "Rainwater channeled down Harlow’s collar," | | 1 | "She ignored the sting, keeping" | | 2 | "Tomás Herrera ran with a" | | 3 | "He skidded around a stack" | | 4 | "The silver Saint Christopher medallion" | | 5 | "Harlow adjusted her stride." | | 6 | "Years on the beat had" | | 7 | "She did not slip." | | 8 | "She did not slow." | | 9 | "Her salt-and-pepper hair clung to" | | 10 | "She reached down, checking her" | | 11 | "The worn leather strap of" | | 12 | "Her voice cut through the" | | 13 | "The Spaniard didn't look back." | | 14 | "He veered left, darting into" | | 15 | "The passage was dark, a" | | 16 | "The smell of wet brick" | | 17 | "Harlow pursued him into the" | | 18 | "Her boots clattered against the" | | 19 | "The sleeve of his jacket" |
| | ratio | 0.877 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 81 | | matches | | 0 | "If she stepped past this" | | 1 | "If she turned back, Herrera" |
| | ratio | 0.025 | |
| 81.28% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 58 | | technicalSentenceCount | 5 | | matches | | 0 | "The sleeve of his jacket pulled back, revealing the jagged scar running along his left forearm—a pale reminder of a knife attack that had ended his medical care…" | | 1 | "With a heavy, metallic groan, the hatch swung upward, releasing a puff of air that smelled of copper, dried sage, and ozone." | | 2 | "The guard stood over six feet tall, dressed in a heavy trench coat that smelled of damp wool and wet dog." | | 3 | "She looked past the giant's shoulder to Herrera, who was watching her with a mixture of pity and anxiety." | | 4 | "If she stepped past this threshold without a token, she would be completely exposed, deep in the territory of people who didn't recognize her law." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |