| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1214 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 50.58% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1214 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "to the core" | | 1 | "velvet" | | 2 | "crystal" | | 3 | "rhythmic" | | 4 | "blown wide" | | 5 | "whisper" | | 6 | "etched" | | 7 | "perfect" | | 8 | "gloom" | | 9 | "pulsed" | | 10 | "flickered" |
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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 | 86 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 86 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 102 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1214 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 1 | | matches | | 0 | "Behind her, Patel screamed." |
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| 76.47% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 952 | | uniqueNames | 7 | | maxNameDensity | 1.47 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Patel | 12 | | Tube | 1 | | Camden | 1 | | Victorian | 1 | | Veil | 1 | | Market | 1 | | Quinn | 14 |
| | persons | | 0 | "Patel" | | 1 | "Camden" | | 2 | "Market" | | 3 | "Quinn" |
| | places | (empty) | | globalScore | 0.765 | | windowScore | 0.833 | |
| 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 | 1214 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 102 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 36.79 | | std | 25.87 | | cv | 0.703 | | sampleLengths | | 0 | 15 | | 1 | 19 | | 2 | 49 | | 3 | 112 | | 4 | 74 | | 5 | 57 | | 6 | 5 | | 7 | 15 | | 8 | 33 | | 9 | 35 | | 10 | 21 | | 11 | 48 | | 12 | 16 | | 13 | 65 | | 14 | 13 | | 15 | 19 | | 16 | 85 | | 17 | 14 | | 18 | 38 | | 19 | 50 | | 20 | 43 | | 21 | 22 | | 22 | 20 | | 23 | 31 | | 24 | 2 | | 25 | 78 | | 26 | 47 | | 27 | 24 | | 28 | 17 | | 29 | 10 | | 30 | 26 | | 31 | 33 | | 32 | 78 |
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| 97.10% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 86 | | matches | | 0 | "were blown" | | 1 | "were fixed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 157 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 102 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 958 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.021920668058455117 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0041753653444676405 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 102 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 102 | | mean | 11.9 | | std | 8.94 | | cv | 0.751 | | sampleLengths | | 0 | 6 | | 1 | 9 | | 2 | 12 | | 3 | 7 | | 4 | 10 | | 5 | 23 | | 6 | 16 | | 7 | 12 | | 8 | 27 | | 9 | 45 | | 10 | 28 | | 11 | 17 | | 12 | 19 | | 13 | 21 | | 14 | 7 | | 15 | 10 | | 16 | 22 | | 17 | 7 | | 18 | 6 | | 19 | 22 | | 20 | 5 | | 21 | 7 | | 22 | 8 | | 23 | 7 | | 24 | 17 | | 25 | 9 | | 26 | 4 | | 27 | 11 | | 28 | 12 | | 29 | 3 | | 30 | 1 | | 31 | 4 | | 32 | 13 | | 33 | 8 | | 34 | 6 | | 35 | 10 | | 36 | 12 | | 37 | 10 | | 38 | 4 | | 39 | 1 | | 40 | 5 | | 41 | 9 | | 42 | 7 | | 43 | 10 | | 44 | 23 | | 45 | 7 | | 46 | 9 | | 47 | 3 | | 48 | 1 | | 49 | 12 |
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| 58.50% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.39215686274509803 | | totalSentences | 102 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 74 | | matches | | 0 | "She moved around the corpse," | | 1 | "His eyes stared upward, brown" | | 2 | "He did not move as" | | 3 | "He held up a small" | | 4 | "She took it from him," | | 5 | "She pocketed the compass and" | | 6 | "She pried them open with" | | 7 | "It was too viscous, too" | | 8 | "It stayed in a perfect" | | 9 | "It cut off abruptly, a" | | 10 | "It absorbed the light." | | 11 | "She looked at the compass" | | 12 | "It pointed with unerring accuracy" | | 13 | "It lay disturbed, scraped raw" | | 14 | "Her eyes were fixed on" | | 15 | "Her hand rested on her" | | 16 | "She turned to face Patel," | | 17 | "She drew her weapon." | | 18 | "It stepped forward, and the" |
| | ratio | 0.257 | |
| 20.81% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 74 | | matches | | 0 | "DC Patel looked up from" | | 1 | "She moved around the corpse," | | 2 | "The abandoned Tube station beneath" | | 3 | "The Veil Market had claimed" | | 4 | "The gas hissed in sharp," | | 5 | "This section of the platform" | | 6 | "The dead man lay on" | | 7 | "His eyes stared upward, brown" | | 8 | "These eyes had witnessed something" | | 9 | "A merchant in a fox" | | 10 | "He did not move as" | | 11 | "None of the other vendors" | | 12 | "The market had fallen silent," | | 13 | "Patel sighed, a sound of" | | 14 | "He held up a small" | | 15 | "The casing bore a patina" | | 16 | "The needle spun in lazy," | | 17 | "She took it from him," | | 18 | "A hairline crack spiderwebbed across" | | 19 | "Patel said, standing and brushing" |
| | ratio | 0.878 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 5 | | matches | | 0 | "The Veil Market had claimed this space for the night, stalls lining the old tracks behind the police tape, hissing gas lamps casting violet shadows across velve…" | | 1 | "The gas hissed in sharp, rhythmic bursts, and somewhere in the deeper tunnels, something that might have been music played on a broken music box, discordant and…" | | 2 | "The casing bore a patina of verdigris, the face etched with protective sigils that caught the lamplight." | | 3 | "The fingers were stiff, rigor beginning to set in the extremities despite the torso remaining limp, a contradiction that made her skin prickle." | | 4 | "The creature's hand closed around her wrist, the claws punching through her glove, drawing blood that steamed in the sudden cold." |
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| 93.75% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 1 | | matches | | 0 | "Patel said, though his voice had thinned, losing its edge" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 32 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0 | | effectiveRatio | 0 | |