| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 2 | | adverbTags | | 0 | "Harlow crouched again [again]" | | 1 | "Harlow said quietly [quietly]" |
| | dialogueSentences | 42 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0.111 | | effectiveRatio | 0.095 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1233 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 63.50% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1233 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "echoed" | | 1 | "velvet" | | 2 | "glint" | | 3 | "etched" | | 4 | "intricate" | | 5 | "trembled" | | 6 | "maw" | | 7 | "footsteps" |
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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 | 82 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 82 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 104 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1231 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 65.03% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 765 | | uniqueNames | 12 | | maxNameDensity | 1.7 | | worstName | "Patel" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Patel" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Detective | 1 | | Harlow | 11 | | Quinn | 1 | | Veil | 2 | | Market | 2 | | Liam | 1 | | Patel | 13 | | Finch | 4 | | Morris | 2 | | Walter | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Liam" | | 4 | "Patel" | | 5 | "Finch" | | 6 | "Morris" | | 7 | "Walter" |
| | places | | | globalScore | 0.65 | | windowScore | 0.833 | |
| 60.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 2 | | matches | | 0 | "seemed placed not thrown" | | 1 | "seemed thinner in a rough circle about three feet across" |
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| 37.53% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.625 | | wordCount | 1231 | | matches | | 0 | "Not disturbed, but…" | | 1 | "no mark on him but" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 104 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 39 | | mean | 31.56 | | std | 20.14 | | cv | 0.638 | | sampleLengths | | 0 | 73 | | 1 | 3 | | 2 | 44 | | 3 | 44 | | 4 | 53 | | 5 | 27 | | 6 | 48 | | 7 | 26 | | 8 | 33 | | 9 | 40 | | 10 | 38 | | 11 | 5 | | 12 | 60 | | 13 | 14 | | 14 | 27 | | 15 | 11 | | 16 | 5 | | 17 | 17 | | 18 | 18 | | 19 | 4 | | 20 | 43 | | 21 | 4 | | 22 | 41 | | 23 | 41 | | 24 | 45 | | 25 | 31 | | 26 | 8 | | 27 | 69 | | 28 | 22 | | 29 | 12 | | 30 | 45 | | 31 | 4 | | 32 | 54 | | 33 | 10 | | 34 | 51 | | 35 | 11 | | 36 | 69 | | 37 | 39 | | 38 | 42 |
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| 83.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 82 | | matches | | 0 | "was trained" | | 1 | "were curled" | | 2 | "was etched" | | 3 | "was smudged" | | 4 | "were gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 132 | | matches | | |
| 87.91% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 104 | | ratio | 0.019 | | matches | | 0 | "The face was etched with intricate, interlocking symbols—sigils she didn’t recognize but that made the hair on her neck prickle." | | 1 | "On the ground lay a small, carved bone token—the entry requirement for the Veil Market." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 769 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.018205461638491547 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007802340702210663 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 104 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 104 | | mean | 11.84 | | std | 8.4 | | cv | 0.71 | | sampleLengths | | 0 | 20 | | 1 | 24 | | 2 | 29 | | 3 | 3 | | 4 | 9 | | 5 | 14 | | 6 | 8 | | 7 | 13 | | 8 | 16 | | 9 | 12 | | 10 | 6 | | 11 | 10 | | 12 | 10 | | 13 | 23 | | 14 | 20 | | 15 | 13 | | 16 | 13 | | 17 | 1 | | 18 | 21 | | 19 | 11 | | 20 | 16 | | 21 | 14 | | 22 | 12 | | 23 | 2 | | 24 | 31 | | 25 | 7 | | 26 | 4 | | 27 | 8 | | 28 | 7 | | 29 | 2 | | 30 | 8 | | 31 | 3 | | 32 | 1 | | 33 | 14 | | 34 | 8 | | 35 | 6 | | 36 | 10 | | 37 | 5 | | 38 | 7 | | 39 | 11 | | 40 | 20 | | 41 | 6 | | 42 | 16 | | 43 | 9 | | 44 | 5 | | 45 | 12 | | 46 | 11 | | 47 | 4 | | 48 | 11 | | 49 | 5 |
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| 71.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.46153846153846156 | | totalSentences | 104 | | uniqueOpeners | 48 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 60.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 75 | | matches | | 0 | "Her torch beam cut through" | | 1 | "His own torch was trained" | | 2 | "His eyes were open, milky" | | 3 | "His hands were curled into" | | 4 | "He was a compact man" | | 5 | "She played her light over" | | 6 | "It wasn’t adding up." | | 7 | "his expression wasn’t one of" | | 8 | "It was blank." | | 9 | "Her light caught a glint" | | 10 | "She nudged the table leg" | | 11 | "She picked it up, careful" | | 12 | "She turned it in her" | | 13 | "It trembled, then swung decisively" | | 14 | "They were clean." | | 15 | "She held the compass flat" | | 16 | "It pointed along the tunnel" | | 17 | "She didn’t elaborate" | | 18 | "She’d kept that page." | | 19 | "She moved past the body," |
| | ratio | 0.4 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 75 | | matches | | 0 | "The damp, cold air of" | | 1 | "Her torch beam cut through" | | 2 | "The Veil Market had moved" | | 3 | "DS Liam Patel’s voice echoed" | | 4 | "His own torch was trained" | | 5 | "Harlow approached, her boots crunching" | | 6 | "The military precision of her" | | 7 | "The body was a man," | | 8 | "His eyes were open, milky" | | 9 | "His hands were curled into" | | 10 | "Patel said, not looking up" | | 11 | "He was a compact man" | | 12 | "Harlow crouched, the worn leather" | | 13 | "She played her light over" | | 14 | "Patel gestured with his pen" | | 15 | "Harlow’s voice was flat" | | 16 | "Harlow stood, her beam sweeping" | | 17 | "It wasn’t adding up." | | 18 | "The stall was too orderly" | | 19 | "The fallen items seemed placed," |
| | ratio | 0.92 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 91.84% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 2 | | matches | | 0 | "The Veil Market had moved on, leaving behind the detritus of its trade: scattered chicken bones, spilt powders that glimmered faintly, and the lingering scent o…" | | 1 | "He was a compact man with a perpetual five-o’clock shadow and the pragmatic weariness of a detective who’d seen too many basement overdoses." |
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| 97.22% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 1 | | matches | | 0 | "Patel said, not looking up from his notebook" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.119 | | leniency | 0.238 | | rawRatio | 0 | | effectiveRatio | 0 | |