| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "Eva said sharply [sharply]" |
| | dialogueSentences | 20 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.2 | | effectiveRatio | 0.1 | |
| 74.62% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 591 | | totalAiIsmAdverbs | 3 | | 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) | |
| 49.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 591 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "scanning" | | 1 | "glinting" | | 2 | "etched" | | 3 | "whisper" | | 4 | "flicker" | | 5 | "flickered" |
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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 | 48 | | matches | (empty) | |
| 53.57% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 48 | | filterMatches | | | hedgeMatches | | |
| 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 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 3 | | totalWords | 580 | | ratio | 0.005 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 463 | | uniqueNames | 9 | | maxNameDensity | 3.24 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 1 | | Quinn | 15 | | Veil | 2 | | Market | 2 | | Kowalski | 1 | | Eva | 13 | | Met | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Market" | | 4 | "Kowalski" | | 5 | "Eva" |
| | places | (empty) | | globalScore | 0 | | windowScore | 0.167 | |
| 63.79% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 29 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a tailor-made suit—expensive" |
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| 27.59% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.724 | | wordCount | 580 | | matches | | 0 | "not north, but toward the tunnel’s dark mouth" |
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| 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 | 20.71 | | std | 14.58 | | cv | 0.704 | | sampleLengths | | 0 | 59 | | 1 | 49 | | 2 | 7 | | 3 | 7 | | 4 | 4 | | 5 | 42 | | 6 | 21 | | 7 | 16 | | 8 | 25 | | 9 | 26 | | 10 | 33 | | 11 | 16 | | 12 | 38 | | 13 | 9 | | 14 | 33 | | 15 | 12 | | 16 | 15 | | 17 | 8 | | 18 | 36 | | 19 | 32 | | 20 | 6 | | 21 | 22 | | 22 | 23 | | 23 | 9 | | 24 | 17 | | 25 | 6 | | 26 | 6 | | 27 | 3 |
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| 90.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 48 | | matches | | 0 | "were curled" | | 1 | "was etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 82 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 63 | | ratio | 0.127 | | matches | | 0 | "The dim emergency lighting cast long shadows across the platform, illuminating patches of graffiti and the occasional discarded bone token—proof this place had once been the infamous Veil Market." | | 1 | "She tucked a curl of red hair behind her ear—twice—before noticing Quinn’s arrival." | | 2 | "Male, mid-forties, dressed in what looked like a tailor-made suit—expensive, but rumpled now, stained with something dark and viscous." | | 3 | "Three years ago, she’d seen marks like these at another crime scene—one that had ended with her partner dead and no answers." | | 4 | "The beam illuminated something glinting near his throat—a small brass compass, its verdigris patina catching the light." | | 5 | "The casing was etched with sigils—protective ones, if she remembered Eva’s ramblings correctly." | | 6 | "The air in the station shifted—a whisper of movement in the shadows." | | 7 | "Eva opened her mouth to argue, but the lights flickered again—this time, they didn’t come back on." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 474 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.035864978902953586 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.014767932489451477 | |
| 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 | 9.21 | | std | 6.33 | | cv | 0.688 | | sampleLengths | | 0 | 16 | | 1 | 14 | | 2 | 29 | | 3 | 24 | | 4 | 12 | | 5 | 13 | | 6 | 7 | | 7 | 6 | | 8 | 1 | | 9 | 2 | | 10 | 2 | | 11 | 7 | | 12 | 12 | | 13 | 19 | | 14 | 2 | | 15 | 2 | | 16 | 21 | | 17 | 4 | | 18 | 12 | | 19 | 2 | | 20 | 19 | | 21 | 4 | | 22 | 15 | | 23 | 11 | | 24 | 3 | | 25 | 22 | | 26 | 5 | | 27 | 3 | | 28 | 4 | | 29 | 12 | | 30 | 2 | | 31 | 11 | | 32 | 8 | | 33 | 17 | | 34 | 9 | | 35 | 5 | | 36 | 15 | | 37 | 13 | | 38 | 4 | | 39 | 8 | | 40 | 7 | | 41 | 8 | | 42 | 4 | | 43 | 4 | | 44 | 12 | | 45 | 13 | | 46 | 11 | | 47 | 5 | | 48 | 12 | | 49 | 15 |
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| 75.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.4603174603174603 | | totalSentences | 63 | | uniqueOpeners | 29 | |
| 83.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 40 | | matches | | 0 | "Just the flicker of the" |
| | ratio | 0.025 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 40 | | matches | | 0 | "Her satchel slumped against her" | | 1 | "She tucked a curl of" | | 2 | "She forced herself to focus." | | 3 | "She reached for her flashlight," | | 4 | "She was too busy flipping" |
| | ratio | 0.125 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 40 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Detective Harlow Quinn stepped carefully" | | 2 | "The dim emergency lighting cast" | | 3 | "Eva Kowalski stood near the" | | 4 | "Her satchel slumped against her" | | 5 | "She tucked a curl of" | | 6 | "Eva said, not looking up" | | 7 | "Quinn checked her worn leather" | | 8 | "Quinn ignored the jab, scanning" | | 9 | "The body lay sprawled near" | | 10 | "Quinn crouched beside the corpse," | | 11 | "The Veil Market moved locations" | | 12 | "Eva hesitated, then flipped her" | | 13 | "Quinn’s jaw tightened." | | 14 | "She forced herself to focus." | | 15 | "Eva adjusted her glasses." | | 16 | "The man’s fingers were curled" | | 17 | "She reached for her flashlight," | | 18 | "The beam illuminated something glinting" | | 19 | "Eva said sharply" |
| | ratio | 0.9 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 40 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva said, not looking up" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "Eva breathed (breathe)" |
| | dialogueSentences | 20 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.25 | | effectiveRatio | 0.1 | |