| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.571 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.82% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 705 | | 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) | |
| 29.08% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 705 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "unravel" | | 1 | "clandestine" | | 2 | "scanned" | | 3 | "glinting" | | 4 | "shattered" | | 5 | "silence" | | 6 | "stark" | | 7 | "weight" | | 8 | "delve" |
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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 | 1 | | narrationSentences | 50 | | matches | | |
| 85.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 50 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 56 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 32 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 708 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 60.42% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 33 | | wordCount | 614 | | uniqueNames | 15 | | maxNameDensity | 1.79 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 11 | | Raven | 1 | | Nest | 1 | | Market | 3 | | Herrera | 7 | | Saint | 1 | | Christopher | 1 | | Soho | 1 | | Spaniard | 1 | | Undercity | 1 | | Morris | 1 | | Tomás | 1 | | Veil | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Market" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Spaniard" | | 9 | "Morris" | | 10 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "Undercity" | | 2 | "Veil" |
| | globalScore | 0.604 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | 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 | 708 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 56 | | matches | (empty) | |
| 75.88% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 35.4 | | std | 14.71 | | cv | 0.416 | | sampleLengths | | 0 | 39 | | 1 | 39 | | 2 | 49 | | 3 | 22 | | 4 | 37 | | 5 | 10 | | 6 | 19 | | 7 | 34 | | 8 | 29 | | 9 | 51 | | 10 | 24 | | 11 | 50 | | 12 | 32 | | 13 | 20 | | 14 | 46 | | 15 | 42 | | 16 | 50 | | 17 | 10 | | 18 | 36 | | 19 | 69 |
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| 98.25% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 50 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 112 | | matches | | |
| 40.82% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 56 | | ratio | 0.036 | | matches | | 0 | "For weeks, she'd been tracking the rogue paramedic – since discovering his involvement with theVeil Market." | | 1 | "Quinn scanned the dimly lit room, her eyes landing on a man fitting Herrera's description – olive skin, short curly hair – seated at the counter, a Saint Christopher medallion glinting at his neck." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 183 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 4 | | adverbRatio | 0.02185792349726776 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.01092896174863388 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 56 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 56 | | mean | 12.64 | | std | 5.64 | | cv | 0.446 | | sampleLengths | | 0 | 27 | | 1 | 12 | | 2 | 10 | | 3 | 16 | | 4 | 13 | | 5 | 15 | | 6 | 34 | | 7 | 6 | | 8 | 6 | | 9 | 10 | | 10 | 17 | | 11 | 5 | | 12 | 11 | | 13 | 4 | | 14 | 10 | | 15 | 19 | | 16 | 11 | | 17 | 12 | | 18 | 11 | | 19 | 11 | | 20 | 8 | | 21 | 10 | | 22 | 19 | | 23 | 19 | | 24 | 13 | | 25 | 11 | | 26 | 11 | | 27 | 2 | | 28 | 10 | | 29 | 9 | | 30 | 8 | | 31 | 15 | | 32 | 8 | | 33 | 15 | | 34 | 8 | | 35 | 9 | | 36 | 7 | | 37 | 13 | | 38 | 18 | | 39 | 16 | | 40 | 12 | | 41 | 10 | | 42 | 10 | | 43 | 22 | | 44 | 11 | | 45 | 12 | | 46 | 13 | | 47 | 14 | | 48 | 10 | | 49 | 12 |
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| 100.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.6428571428571429 | | totalSentences | 56 | | uniqueOpeners | 36 | |
| 66.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 50 | | matches | | 0 | "Suddenly, the Spaniard stopped at" |
| | ratio | 0.02 | |
| 84.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 50 | | matches | | 0 | "She adjusted her worn leather" | | 1 | "she muttered under her breath" | | 2 | "She approached, her bearing military" | | 3 | "she shouted above the chaos" | | 4 | "he replied, yanking a knife" | | 5 | "She grabbed Herrera, dragging him" | | 6 | "They emerged into a narrow" | | 7 | "They ran through the labyrinth" | | 8 | "His movements were swift, precise," | | 9 | "he said, pulling a bone" | | 10 | "She hesitated, her hand instinctively" | | 11 | "She couldn't afford to lose" | | 12 | "They descended into the bowels" | | 13 | "She had no idea what" | | 14 | "she said, her voice low" | | 15 | "He turned to face her," | | 16 | "She scanned the room, her" |
| | ratio | 0.34 | |
| 90.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 50 | | matches | | 0 | "Rain lashed the pavement, the" | | 1 | "She adjusted her worn leather" | | 2 | "she muttered under her breath" | | 3 | "Tonight, she would confront him" | | 4 | "Quinn scanned the dimly lit" | | 5 | "She approached, her bearing military" | | 6 | "The man's warm brown eyes" | | 7 | "Patrons screamed, diving for cover." | | 8 | "Quinn dropped behind the counter," | | 9 | "Herrera crouched beside her." | | 10 | "she shouted above the chaos" | | 11 | "he replied, yanking a knife" | | 12 | "Another burst of gunfire, and" | | 13 | "She grabbed Herrera, dragging him" | | 14 | "They emerged into a narrow" | | 15 | "Herrera called, sprinting for the" | | 16 | "Quinn hesitated only for a" | | 17 | "They ran through the labyrinth" | | 18 | "Quinn watched Herrera closely, his" | | 19 | "His movements were swift, precise," |
| | ratio | 0.74 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 50 | | matches | | 0 | "If the alternative was getting" | | 1 | "To follow him into the" | | 2 | "To uncover the truth, she" |
| | ratio | 0.06 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 2 | | matches | | 0 | "Rain lashed the pavement, the gutters flowing like rivers as Detective Harlow Quinn stepped from her car, the green neon sign of The Raven's Nest flickering ove…" | | 1 | "Quinn scanned the dimly lit room, her eyes landing on a man fitting Herrera's description – olive skin, short curly hair – seated at the counter, a Saint Christ…" |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice low," |
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| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she shouted (shout)" |
| | dialogueSentences | 14 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.286 | | effectiveRatio | 0.286 | |