| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 21 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1053 | | 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) | |
| 66.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1053 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "mechanical" | | 1 | "weight" | | 2 | "stark" | | 3 | "maw" | | 4 | "vibrated" | | 5 | "measured" | | 6 | "firmly" |
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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 | 73 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 73 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 14 | | totalWords | 1055 | | ratio | 0.013 | | matches | | 0 | "Camden Crescent - Northern Line - Disused 1934" | | 1 | "Grade-Four Wolfsbane and Purified Drake Gall" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 934 | | uniqueNames | 24 | | maxNameDensity | 1.5 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Herrera | 12 | | London | 2 | | Quinn | 14 | | Stucley | 1 | | Place | 1 | | Saint | 1 | | Christopher | 1 | | Camden | 2 | | Lock | 1 | | Morris | 3 | | Crescent | 1 | | Northern | 1 | | Line | 1 | | Disused | 1 | | Metropolitan | 1 | | Police | 1 | | Underground | 1 | | Blitz | 1 | | Victorian | 1 | | Wolfsbane | 1 | | Purified | 1 | | Drake | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Quinn" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Morris" | | 5 | "Line" | | 6 | "Police" | | 7 | "Underground" | | 8 | "Purified" | | 9 | "Drake" | | 10 | "Market" |
| | places | | 0 | "London" | | 1 | "Stucley" | | 2 | "Place" | | 3 | "Victorian" |
| | globalScore | 0.751 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like walking into the mouth of a f" |
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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 | 1055 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 91 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 51 | | mean | 20.69 | | std | 16.99 | | cv | 0.821 | | sampleLengths | | 0 | 22 | | 1 | 40 | | 2 | 56 | | 3 | 5 | | 4 | 63 | | 5 | 62 | | 6 | 32 | | 7 | 16 | | 8 | 5 | | 9 | 26 | | 10 | 3 | | 11 | 10 | | 12 | 6 | | 13 | 7 | | 14 | 6 | | 15 | 9 | | 16 | 15 | | 17 | 7 | | 18 | 2 | | 19 | 12 | | 20 | 42 | | 21 | 39 | | 22 | 18 | | 23 | 50 | | 24 | 15 | | 25 | 50 | | 26 | 40 | | 27 | 3 | | 28 | 39 | | 29 | 16 | | 30 | 11 | | 31 | 7 | | 32 | 8 | | 33 | 38 | | 34 | 11 | | 35 | 33 | | 36 | 2 | | 37 | 4 | | 38 | 10 | | 39 | 14 | | 40 | 18 | | 41 | 38 | | 42 | 16 | | 43 | 9 | | 44 | 4 | | 45 | 9 | | 46 | 19 | | 47 | 46 | | 48 | 15 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 73 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 139 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 91 | | ratio | 0.011 | | matches | | 0 | "An old enamel sign hung crookedly from a beam: *Camden Crescent - Northern Line - Disused 1934*." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 935 | | adjectiveStacks | 1 | | stackExamples | | 0 | "illuminating cracked white tiles" |
| | adverbCount | 19 | | adverbRatio | 0.020320855614973262 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.011764705882352941 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 11.59 | | std | 6.16 | | cv | 0.532 | | sampleLengths | | 0 | 22 | | 1 | 10 | | 2 | 13 | | 3 | 17 | | 4 | 8 | | 5 | 16 | | 6 | 17 | | 7 | 15 | | 8 | 5 | | 9 | 5 | | 10 | 18 | | 11 | 22 | | 12 | 18 | | 13 | 13 | | 14 | 1 | | 15 | 1 | | 16 | 21 | | 17 | 26 | | 18 | 16 | | 19 | 3 | | 20 | 13 | | 21 | 16 | | 22 | 5 | | 23 | 15 | | 24 | 11 | | 25 | 3 | | 26 | 10 | | 27 | 6 | | 28 | 7 | | 29 | 6 | | 30 | 9 | | 31 | 15 | | 32 | 7 | | 33 | 2 | | 34 | 12 | | 35 | 7 | | 36 | 18 | | 37 | 17 | | 38 | 11 | | 39 | 13 | | 40 | 15 | | 41 | 18 | | 42 | 5 | | 43 | 9 | | 44 | 20 | | 45 | 16 | | 46 | 15 | | 47 | 14 | | 48 | 10 | | 49 | 7 |
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| 75.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.4725274725274725 | | totalSentences | 91 | | uniqueOpeners | 43 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 68 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 68 | | matches | | 0 | "Her lungs burned, but eighteen" | | 1 | "It clattered against Quinn’s knee," | | 2 | "He took the sharp corner" | | 3 | "He did not look back." | | 4 | "He hugged a heavy canvas" | | 5 | "He darted left, then right," | | 6 | "His dark curly hair stuck" | | 7 | "It felt like walking into" | | 8 | "She stepped off the final" | | 9 | "She had seen whispers of" | | 10 | "Her fingers rested firmly on" | | 11 | "His eyes were small, amber," | | 12 | "She drove the heel of" | | 13 | "He whipped around, his jaw" |
| | ratio | 0.206 | |
| 11.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 61 | | totalSentences | 68 | | matches | | 0 | "Tomás Herrera cleared the iron" | | 1 | "Quinn’s boot struck the wet" | | 2 | "Water sprayed across her shins," | | 3 | "Her lungs burned, but eighteen" | | 4 | "Herrera threw a blue plastic" | | 5 | "It clattered against Quinn’s knee," | | 6 | "He took the sharp corner" | | 7 | "A silver Saint Christopher medallion" | | 8 | "He did not look back." | | 9 | "He hugged a heavy canvas" | | 10 | "A white jagged scar ran" | | 11 | "He darted left, then right," | | 12 | "The smell of rotting cabbage" | | 13 | "The sour, chemical tang that" | | 14 | "Herrera slammed his shoulder into" | | 15 | "The chains snapped." | | 16 | "The wood screamed, giving way" | | 17 | "Quinn halted at the threshold," | | 18 | "Herrera turned at the base" | | 19 | "His dark curly hair stuck" |
| | ratio | 0.897 | |
| 73.53% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 68 | | matches | | 0 | "Before he recovered his footing," |
| | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 3 | | matches | | 0 | "Tomás Herrera cleared the iron railing with the frantic grace of a man who knew precisely what lay in the dark below." | | 1 | "Hooded figures milled between the stalls, their voices blending into a low, buzzing hum that vibrated inside Quinn’s jawbone." | | 2 | "The vendor with the long fingers froze, dropping a pinch of powder that hissed violently against the stone floor." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 3 | | fancyTags | | 0 | "the larger man muttered (mutter)" | | 1 | "Herrera yelled (yell)" | | 2 | "he hissed (hiss)" |
| | dialogueSentences | 21 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 1 | | effectiveRatio | 0.286 | |