| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.75 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.50% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 667 | | 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) | |
| 17.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 667 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "clandestine" | | 1 | "determined" | | 2 | "depths" | | 3 | "echoing" | | 4 | "scanned" | | 5 | "glinting" | | 6 | "navigated" | | 7 | "sinister" | | 8 | "treacherous" | | 9 | "scanning" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "heart pounded in chest" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 2 |
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| | highlights | | 0 | "heart pounded in her chest" | | 1 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 46 | | matches | | |
| 0.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 4 | | narrationSentences | 46 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 47 | | 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 | 667 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 43.46% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 657 | | uniqueNames | 9 | | maxNameDensity | 2.13 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 14 | | Herrera | 9 | | Camden | 1 | | Tube | 2 | | Veil | 3 | | Market | 3 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" |
| | places | | | globalScore | 0.435 | | windowScore | 0.667 | |
| 36.36% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 44 | | glossingSentenceCount | 2 | | matches | | 0 | "artifacts that seemed to shift and writhe in the light, and even what appeared to be a taxidermied creature with eyes that glowed like embers" | | 1 | "eyes that seemed to see right through Quinn, beckoned her closer" |
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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 | 667 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 47 | | matches | (empty) | |
| 22.78% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 47.64 | | std | 10.95 | | cv | 0.23 | | sampleLengths | | 0 | 48 | | 1 | 53 | | 2 | 44 | | 3 | 58 | | 4 | 47 | | 5 | 41 | | 6 | 63 | | 7 | 40 | | 8 | 48 | | 9 | 48 | | 10 | 63 | | 11 | 30 | | 12 | 25 | | 13 | 59 |
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| 74.75% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 46 | | matches | | 0 | "was determined" | | 1 | "was rumored" | | 2 | "was involved" | | 3 | "was determined" |
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| 85.06% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 116 | | matches | | 0 | "was gaining" | | 1 | "was getting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 661 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.0226928895612708 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0030257186081694403 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 47 | | echoCount | 0 | | echoWords | (empty) | |
| 96.62% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 47 | | mean | 14.19 | | std | 5.56 | | cv | 0.392 | | sampleLengths | | 0 | 15 | | 1 | 15 | | 2 | 18 | | 3 | 11 | | 4 | 21 | | 5 | 21 | | 6 | 18 | | 7 | 12 | | 8 | 14 | | 9 | 12 | | 10 | 10 | | 11 | 21 | | 12 | 7 | | 13 | 8 | | 14 | 14 | | 15 | 12 | | 16 | 21 | | 17 | 13 | | 18 | 5 | | 19 | 18 | | 20 | 5 | | 21 | 14 | | 22 | 15 | | 23 | 18 | | 24 | 16 | | 25 | 12 | | 26 | 16 | | 27 | 12 | | 28 | 16 | | 29 | 32 | | 30 | 13 | | 31 | 11 | | 32 | 12 | | 33 | 12 | | 34 | 12 | | 35 | 10 | | 36 | 15 | | 37 | 26 | | 38 | 10 | | 39 | 10 | | 40 | 10 | | 41 | 12 | | 42 | 9 | | 43 | 4 | | 44 | 15 | | 45 | 16 | | 46 | 28 |
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| 61.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.3829787234042553 | | totalSentences | 47 | | uniqueOpeners | 18 | |
| 72.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 46 | | matches | | 0 | "Suddenly, Herrera ducked behind a" |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 46 | | matches | | 0 | "Her worn leather watch dug" | | 1 | "She was gaining on Herrera," | | 2 | "She hesitated, her hand on" | | 3 | "She fumbled in her pocket" | | 4 | "She had heard rumors of" | | 5 | "She spotted Herrera weaving through" | | 6 | "She navigated through the stalls," | | 7 | "She was getting close, but" | | 8 | "She pushed through the crowd," | | 9 | "she said, her voice dripping" | | 10 | "She knew she was in" |
| | ratio | 0.239 | |
| 90.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 34 | | totalSentences | 46 | | matches | | 0 | "Rain lashed down on the" | | 1 | "Detective Harlow Quinn sprinted through" | | 2 | "Tomás Herrera, the paramedic-turned-clandestine-medic, had" | | 3 | "Quinn's sharp jaw was set," | | 4 | "Her worn leather watch dug" | | 5 | "She was gaining on Herrera," | | 6 | "The sign creaked in the" | | 7 | "Herrera hesitated for a moment," | | 8 | "Quinn skidded to a stop," | | 9 | "She hesitated, her hand on" | | 10 | "The abandoned Tube station was" | | 11 | "The air was thick with" | | 12 | "She fumbled in her pocket" | | 13 | "Quinn's skin prickled with unease." | | 14 | "She had heard rumors of" | | 15 | "The tunnel opened up into" | | 16 | "The room was crowded with" | | 17 | "Quinn's eyes scanned the room," | | 18 | "She spotted Herrera weaving through" | | 19 | "Quinn's hand tightened around her" |
| | ratio | 0.739 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 46 | | matches | (empty) | | ratio | 0 | |
| 87.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 3 | | matches | | 0 | "She had heard rumors of the Veil Market, a hidden supernatural black market that operated beneath the city." | | 1 | "Quinn saw jars of glowing potions, strange artifacts that seemed to shift and writhe in the light, and even what appeared to be a taxidermied creature with eyes…" | | 2 | "Little did she know, the Veil Market was just the beginning of a journey that would take her down a rabbit hole of supernatural secrets and ancient conspiracies…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 1 | | matches | | 0 | "she said, her voice dripping with malice" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 4 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |