| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 41 | | tagDensity | 0.512 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1293 | | 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) | |
| 38.13% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1293 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "navigated" | | 1 | "fluttered" | | 2 | "constructed" | | 3 | "velvet" | | 4 | "gloom" | | 5 | "echoed" | | 6 | "standard" | | 7 | "scanned" | | 8 | "traced" | | 9 | "pristine" | | 10 | "vibrated" | | 11 | "etched" | | 12 | "intricate" | | 13 | "navigate" | | 14 | "wavering" | | 15 | "calculated" |
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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 | 81 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 81 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1293 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.72% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 774 | | uniqueNames | 10 | | maxNameDensity | 2.33 | | worstName | "Harlow" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Underground | 1 | | Veil | 2 | | Market | 1 | | Miller | 10 | | Harlow | 18 | | Kowalski | 1 | | Eva | 7 | | Morris | 1 | | Compass | 1 |
| | persons | | 0 | "Market" | | 1 | "Miller" | | 2 | "Harlow" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Morris" |
| | places | (empty) | | globalScore | 0.337 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | 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 | 1293 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 89.24% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 35.92 | | std | 16.61 | | cv | 0.462 | | sampleLengths | | 0 | 46 | | 1 | 67 | | 2 | 27 | | 3 | 37 | | 4 | 60 | | 5 | 52 | | 6 | 21 | | 7 | 19 | | 8 | 14 | | 9 | 61 | | 10 | 14 | | 11 | 43 | | 12 | 51 | | 13 | 24 | | 14 | 35 | | 15 | 35 | | 16 | 52 | | 17 | 28 | | 18 | 3 | | 19 | 48 | | 20 | 45 | | 21 | 32 | | 22 | 30 | | 23 | 36 | | 24 | 19 | | 25 | 57 | | 26 | 44 | | 27 | 37 | | 28 | 17 | | 29 | 16 | | 30 | 19 | | 31 | 73 | | 32 | 48 | | 33 | 25 | | 34 | 38 | | 35 | 20 |
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| 92.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 81 | | matches | | 0 | "was caved" | | 1 | "were lifted" | | 2 | "was etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 119 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 101 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 778 | | adjectiveStacks | 2 | | stackExamples | | 0 | "open, revealing thick stacks" | | 1 | "same brutal, calculated efficiency" |
| | adverbCount | 5 | | adverbRatio | 0.006426735218508998 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0012853470437017994 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 12.8 | | std | 7.18 | | cv | 0.561 | | sampleLengths | | 0 | 11 | | 1 | 11 | | 2 | 8 | | 3 | 16 | | 4 | 10 | | 5 | 13 | | 6 | 12 | | 7 | 3 | | 8 | 11 | | 9 | 18 | | 10 | 17 | | 11 | 10 | | 12 | 12 | | 13 | 25 | | 14 | 5 | | 15 | 14 | | 16 | 14 | | 17 | 7 | | 18 | 20 | | 19 | 29 | | 20 | 23 | | 21 | 4 | | 22 | 17 | | 23 | 9 | | 24 | 10 | | 25 | 6 | | 26 | 4 | | 27 | 4 | | 28 | 31 | | 29 | 9 | | 30 | 21 | | 31 | 14 | | 32 | 19 | | 33 | 9 | | 34 | 15 | | 35 | 10 | | 36 | 12 | | 37 | 20 | | 38 | 9 | | 39 | 16 | | 40 | 8 | | 41 | 4 | | 42 | 31 | | 43 | 14 | | 44 | 10 | | 45 | 11 | | 46 | 8 | | 47 | 5 | | 48 | 7 | | 49 | 8 |
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| 57.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.38613861386138615 | | totalSentences | 101 | | uniqueOpeners | 39 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 80 | | matches | | 0 | "Her boots crunched over broken" | | 1 | "He clicked his pen, the" | | 2 | "She pulled a pair of" | | 3 | "She examined the back of" | | 4 | "She moved to the victim's" | | 5 | "She tapped the face of" | | 6 | "She pushed her round glasses" | | 7 | "Her worn leather satchel bulged" | | 8 | "She scanned the rest of" | | 9 | "She opened her satchel and" | | 10 | "She noted the lack of" | | 11 | "It was a small compass," | | 12 | "It spun in erratic circles." | | 13 | "It all mirrored the tactical" | | 14 | "It locked onto a solid" |
| | ratio | 0.188 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 80 | | matches | | 0 | "Harlow navigated the rusted turnstiles" | | 1 | "Her boots crunched over broken" | | 2 | "The air tasted of ozone" | | 3 | "Police tape fluttered in the" | | 4 | "Stalls constructed from salvaged carriage" | | 5 | "The air hung thick with" | | 6 | "The Veil Market." | | 7 | "A ghost bazaar that only" | | 8 | "Tonight, the moon was a" | | 9 | "DS Miller stood over the" | | 10 | "He clicked his pen, the" | | 11 | "Miller gestured with the pen" | | 12 | "Harlow knelt beside the body." | | 13 | "She pulled a pair of" | | 14 | "The victim was a male" | | 15 | "She examined the back of" | | 16 | "The skull was caved in," | | 17 | "Harlow pointed a gloved finger" | | 18 | "Miller scratched his neck." | | 19 | "Harlow swept her flashlight under" |
| | ratio | 0.975 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 93.60% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 2 | | matches | | 0 | "The same brutal, calculated efficiency that had left her previous partner, DS Morris, dead in an unexplained alleyway three years prior." | | 1 | "It locked onto a solid brick wall at the far end of the platform, vibrating with a low frequency that rattled her teeth." |
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| 5.95% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 5 | | matches | | 0 | "Harlow stood, her joints popping" | | 1 | "Eva stepped, her freckled face pale" | | 2 | "Eva whispered, her fingers hovering inches from the brass without touching it" | | 3 | "Miller asked, his skepticism wavering as he stared at the erratic metal" | | 4 | "Harlow marched, her hand dropping to the grip of her sidearm" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 41 | | tagDensity | 0.073 | | leniency | 0.146 | | rawRatio | 0.333 | | effectiveRatio | 0.049 | |