| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 13 | | tagDensity | 0.538 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 849 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 41.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 849 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "flicker" | | 1 | "gloom" | | 2 | "standard" | | 3 | "footsteps" | | 4 | "velvet" | | 5 | "aligned" | | 6 | "flicked" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 57 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 57 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 63 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 849 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 766 | | uniqueNames | 16 | | maxNameDensity | 1.57 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 1 | | Tomás | 1 | | Herrera | 9 | | Saint | 1 | | Christopher | 1 | | Glock | 2 | | Victorian | 1 | | Soho | 1 | | Morris | 1 | | Quinn | 12 | | Transport | 1 | | London | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Saint" | | 2 | "Christopher" | | 3 | "Morris" | | 4 | "Quinn" | | 5 | "Market" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Tomás" | | 4 | "Soho" | | 5 | "London" |
| | globalScore | 0.717 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 50 | | 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 | 849 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 63 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 28 | | mean | 30.32 | | std | 16.26 | | cv | 0.536 | | sampleLengths | | 0 | 22 | | 1 | 22 | | 2 | 48 | | 3 | 26 | | 4 | 43 | | 5 | 28 | | 6 | 54 | | 7 | 19 | | 8 | 81 | | 9 | 19 | | 10 | 36 | | 11 | 5 | | 12 | 46 | | 13 | 47 | | 14 | 30 | | 15 | 44 | | 16 | 29 | | 17 | 12 | | 18 | 26 | | 19 | 24 | | 20 | 31 | | 21 | 5 | | 22 | 19 | | 23 | 26 | | 24 | 30 | | 25 | 14 | | 26 | 48 | | 27 | 15 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 57 | | matches | (empty) | |
| 87.01% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 118 | | matches | | 0 | "was bleeding" | | 1 | "was weighing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 63 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 772 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.015544041450777202 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006476683937823834 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 63 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 63 | | mean | 13.48 | | std | 6.91 | | cv | 0.513 | | sampleLengths | | 0 | 22 | | 1 | 5 | | 2 | 17 | | 3 | 9 | | 4 | 20 | | 5 | 15 | | 6 | 4 | | 7 | 13 | | 8 | 13 | | 9 | 6 | | 10 | 18 | | 11 | 19 | | 12 | 6 | | 13 | 3 | | 14 | 6 | | 15 | 13 | | 16 | 17 | | 17 | 12 | | 18 | 6 | | 19 | 19 | | 20 | 3 | | 21 | 16 | | 22 | 8 | | 23 | 15 | | 24 | 16 | | 25 | 30 | | 26 | 12 | | 27 | 19 | | 28 | 20 | | 29 | 16 | | 30 | 5 | | 31 | 12 | | 32 | 23 | | 33 | 11 | | 34 | 16 | | 35 | 3 | | 36 | 28 | | 37 | 8 | | 38 | 22 | | 39 | 13 | | 40 | 18 | | 41 | 13 | | 42 | 11 | | 43 | 18 | | 44 | 11 | | 45 | 1 | | 46 | 26 | | 47 | 17 | | 48 | 7 | | 49 | 22 |
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| 66.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.42857142857142855 | | totalSentences | 63 | | uniqueOpeners | 27 | |
| 58.48% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 57 | | matches | | 0 | "Instead, he yanked open a" |
| | ratio | 0.018 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 57 | | matches | | 0 | "He hurdled an overturned bin," | | 1 | "He slipped inside, his brown" | | 2 | "She kicked the panel open," | | 3 | "She tapped the face of" | | 4 | "Her radio crackled with heavy" | | 5 | "She was not going to" | | 6 | "She gripped her weapon with" | | 7 | "She had heard whispers on" | | 8 | "She spotted Herrera fifty yards" | | 9 | "He pressed a blood-soaked sleeve" | | 10 | "She slipped past a corner" | | 11 | "He reached into his coat," |
| | ratio | 0.211 | |
| 3.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 57 | | matches | | 0 | "Quinn barked, her boots skidding" | | 1 | "Tomás Herrera did not stop." | | 2 | "He hurdled an overturned bin," | | 3 | "Quinn pressed forward, her lungs" | | 4 | "A silver medallion swung wildly" | | 5 | "The Saint Christopher pendant." | | 6 | "Quinn drew her Glock 17," | | 7 | "Herrera did not vault the" | | 8 | "He slipped inside, his brown" | | 9 | "Quinn hit the steel door" | | 10 | "The frame groaned." | | 11 | "The latch had failed to" | | 12 | "She kicked the panel open," | | 13 | "A subterranean draught rushed past" | | 14 | "Iron stairs spiralled down into" | | 15 | "Quinn clicked on her tactical" | | 16 | "The bright white beam cut" | | 17 | "Herrera was bleeding." | | 18 | "The knife attack scar on" | | 19 | "Quinn paused at the top" |
| | ratio | 0.912 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 57 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 2 | | matches | | 0 | "She had heard whispers on the street about a illegal underground market that moved every full moon, but the sheer scale of the operation made her skin prickle." | | 1 | "From the dark mouth of the railway tunnel ahead, six towering figures stepped onto the platform, their eyes glowing faintly red in the gloom as they unsheathed …" |
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| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 1 | | matches | | 0 | "Herrera said, his voice rough and breathless" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 4 | | fancyTags | | 0 | "Quinn barked (bark)" | | 1 | "Herrera pleaded (plead)" | | 2 | "Quinn shouted (shout)" | | 3 | "the merchant whispered (whisper)" |
| | dialogueSentences | 13 | | tagDensity | 0.462 | | leniency | 0.923 | | rawRatio | 0.667 | | effectiveRatio | 0.615 | |