| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.042 | | leniency | 0.083 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.51% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 870 | | totalAiIsmAdverbs | 2 | | 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) | |
| 31.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 870 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "silence" | | 1 | "unreadable" | | 2 | "stomach" | | 3 | "pulse" | | 4 | "flicked" | | 5 | "racing" | | 6 | "beacon" |
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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 | 98 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 98 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 10 | | markdownWords | 11 | | totalWords | 861 | | ratio | 0.013 | | matches | | 0 | "wrong" | | 1 | "clean" | | 2 | "absorbed" | | 3 | "inside" | | 4 | "clean" | | 5 | "slick" | | 6 | "inside" | | 7 | "clean" | | 8 | "the Compass" | | 9 | "threat" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 26.30% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 768 | | uniqueNames | 7 | | maxNameDensity | 2.47 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 19 | | Morris | 14 | | Veil | 5 | | Market | 5 | | Like | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Morris" | | 3 | "Market" |
| | places | | | globalScore | 0.263 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | 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 | 861 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 25.32 | | std | 22.95 | | cv | 0.906 | | sampleLengths | | 0 | 73 | | 1 | 86 | | 2 | 45 | | 3 | 9 | | 4 | 10 | | 5 | 5 | | 6 | 8 | | 7 | 30 | | 8 | 8 | | 9 | 20 | | 10 | 82 | | 11 | 14 | | 12 | 4 | | 13 | 31 | | 14 | 5 | | 15 | 55 | | 16 | 36 | | 17 | 33 | | 18 | 5 | | 19 | 59 | | 20 | 16 | | 21 | 8 | | 22 | 12 | | 23 | 10 | | 24 | 35 | | 25 | 10 | | 26 | 8 | | 27 | 6 | | 28 | 51 | | 29 | 24 | | 30 | 19 | | 31 | 5 | | 32 | 29 | | 33 | 10 |
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| 83.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 98 | | matches | | 0 | "were clenched" | | 1 | "been *absorbed" | | 2 | "been pulled" | | 3 | "been polished" | | 4 | "been pulled" | | 5 | "was known" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 132 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 9 | | totalSentences | 118 | | ratio | 0.076 | | matches | | 0 | "The air in the abandoned Tube station was thick with the scent of damp stone and something older—something that clung to the walls like a second skin." | | 1 | "His chest was bare, the skin splotched with dark, irregular bruises that didn’t match the bruises on his face—those were the kind a fist could leave, but these…" | | 2 | "The girl from the case three years ago—the one who’d vanished under circumstances Morris couldn’t explain." | | 3 | "Just the faintest hint of something metallic beneath the fabric—like a key, or a lockpick." | | 4 | "The bruises weren’t just on the skin—they were *inside* the flesh, like something had been pulled through." | | 5 | "And then there was the body’s hands—bound, but not broken." | | 6 | "The bruises weren’t just on the skin—they were *inside* the flesh, like something had been pulled through." | | 7 | "And then there was the body’s hands—bound, but not broken." | | 8 | "The Veil Market was known for its supernatural energy, and this compass—*the Compass*—was attuned to it." |
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| 91.08% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 777 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.05019305019305019 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006435006435006435 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 7.3 | | std | 5.45 | | cv | 0.747 | | sampleLengths | | 0 | 27 | | 1 | 20 | | 2 | 26 | | 3 | 14 | | 4 | 20 | | 5 | 12 | | 6 | 28 | | 7 | 6 | | 8 | 6 | | 9 | 7 | | 10 | 16 | | 11 | 20 | | 12 | 2 | | 13 | 8 | | 14 | 1 | | 15 | 4 | | 16 | 6 | | 17 | 5 | | 18 | 4 | | 19 | 4 | | 20 | 3 | | 21 | 1 | | 22 | 16 | | 23 | 9 | | 24 | 1 | | 25 | 5 | | 26 | 3 | | 27 | 3 | | 28 | 1 | | 29 | 1 | | 30 | 3 | | 31 | 4 | | 32 | 8 | | 33 | 10 | | 34 | 16 | | 35 | 4 | | 36 | 5 | | 37 | 15 | | 38 | 3 | | 39 | 9 | | 40 | 11 | | 41 | 2 | | 42 | 7 | | 43 | 14 | | 44 | 2 | | 45 | 2 | | 46 | 7 | | 47 | 17 | | 48 | 3 | | 49 | 4 |
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| 43.22% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.2542372881355932 | | totalSentences | 118 | | uniqueOpeners | 30 | |
| 36.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 91 | | matches | | 0 | "Just the faintest hint of" |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 91 | | matches | | 0 | "She stepped inside, her boots" | | 1 | "His chest was bare, the" | | 2 | "She turned to see DS" | | 3 | "She wore the same sharp" | | 4 | "She moved closer, her boots" | | 5 | "She reached out, brushing her" | | 6 | "Her breath hitched." | | 7 | "She knelt, her fingers brushing" | | 8 | "She pulled back, her eyes" | | 9 | "It was a place where" | | 10 | "She turned to Morris, her" | | 11 | "She knew what she had" | | 12 | "She had to find out" | | 13 | "She turned to Morris, her" | | 14 | "They moved toward the exit," | | 15 | "She reached into her satchel," | | 16 | "She held it up, the" | | 17 | "It pointed straight at the" | | 18 | "She had seen this before." | | 19 | "It pointed to the nearest" |
| | ratio | 0.242 | |
| 47.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 91 | | matches | | 0 | "The air in the abandoned" | | 1 | "Detective Harlow Quinn adjusted the" | | 2 | "The station’s flickering fluorescent lights" | | 3 | "She stepped inside, her boots" | | 4 | "The body lay in the" | | 5 | "A man in a tailored" | | 6 | "His chest was bare, the" | | 7 | "these were too precise, too" | | 8 | "a voice cut through the" | | 9 | "She turned to see DS" | | 10 | "She wore the same sharp" | | 11 | "Quinn exhaled through her nose," | | 12 | "Morris didn’t look convinced." | | 13 | "A beat of silence." | | 14 | "Quinn’s stomach twisted." | | 15 | "The girl from the case" | | 16 | "The one Quinn had sworn" | | 17 | "Morris gestured toward the body." | | 18 | "Quinn’s pulse spiked." | | 19 | "The place where supernatural things" |
| | ratio | 0.824 | |
| 54.95% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 91 | | matches | | 0 | "Because this wasn’t just a" |
| | ratio | 0.011 | |
| 89.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 2 | | matches | | 0 | "The air in the abandoned Tube station was thick with the scent of damp stone and something older—something that clung to the walls like a second skin." | | 1 | "His chest was bare, the skin splotched with dark, irregular bruises that didn’t match the bruises on his face—those were the kind a fist could leave, but these…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn said, her voice low," |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.042 | | leniency | 0.083 | | rawRatio | 0 | | effectiveRatio | 0 | |