| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 83.69% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1226 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "tightly" | | 1 | "softly" | | 2 | "really" | | 3 | "slowly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 51.06% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1226 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "perfect" | | 1 | "silence" | | 2 | "weight" | | 3 | "traced" | | 4 | "palpable" | | 5 | "throbbed" | | 6 | "tension" | | 7 | "scanned" | | 8 | "electric" |
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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 | 128 | | matches | (empty) | |
| 64.73% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 7 | | hedgeCount | 0 | | narrationSentences | 128 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 128 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1226 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 4 | | matches | | 0 | "You look well, Eva said." | | 1 | "I missed you, Eva said softly." | | 2 | "You are harder, Eva said." | | 3 | "I do not know, Rory said." |
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| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 76 | | wordCount | 1226 | | uniqueNames | 20 | | maxNameDensity | 1.31 | | worstName | "Rory" | | maxWindowNameDensity | 3 | | worstWindowName | "Eva" | | discoveredNames | | Soho | 2 | | Carter | 1 | | Raven | 2 | | Nest | 2 | | Golden | 1 | | Empress | 1 | | London | 3 | | Welsh | 1 | | Rory | 16 | | Cardiff | 3 | | Pre-Law | 1 | | Evan | 3 | | Eva | 16 | | Silas | 5 | | Bay | 1 | | Prague | 1 | | Yu-Fei | 1 | | Aurora | 2 | | You | 11 | | Like | 3 |
| | persons | | 0 | "Carter" | | 1 | "Raven" | | 2 | "Empress" | | 3 | "Rory" | | 4 | "Evan" | | 5 | "Eva" | | 6 | "Silas" | | 7 | "Aurora" | | 8 | "You" |
| | places | | 0 | "Soho" | | 1 | "Golden" | | 2 | "London" | | 3 | "Cardiff" | | 4 | "Bay" | | 5 | "Prague" |
| | globalScore | 0.847 | | windowScore | 0.667 | |
| 45.83% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like they knew where the bodies we" | | 1 | "felt like it belonged to a different pe" | | 2 | "tasted like ash" |
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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 | 1226 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 128 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 37.15 | | std | 33 | | cv | 0.888 | | sampleLengths | | 0 | 120 | | 1 | 133 | | 2 | 52 | | 3 | 57 | | 4 | 65 | | 5 | 22 | | 6 | 48 | | 7 | 21 | | 8 | 11 | | 9 | 36 | | 10 | 7 | | 11 | 8 | | 12 | 25 | | 13 | 28 | | 14 | 52 | | 15 | 4 | | 16 | 3 | | 17 | 50 | | 18 | 2 | | 19 | 52 | | 20 | 37 | | 21 | 53 | | 22 | 10 | | 23 | 4 | | 24 | 50 | | 25 | 36 | | 26 | 45 | | 27 | 5 | | 28 | 3 | | 29 | 11 | | 30 | 43 | | 31 | 21 | | 32 | 112 |
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| 97.04% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 128 | | matches | | 0 | "were buried" | | 1 | "were gone" | | 2 | "was gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 230 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 128 | | ratio | 0.008 | | matches | | 0 | "The walls were a collage of forgotten geography; old maps of London and black-and-white photographs of men in suits who looked like they knew where the bodies were buried." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1231 | | adjectiveStacks | 1 | | stackExamples | | 0 | "cold, shocking against her" |
| | adverbCount | 29 | | adverbRatio | 0.023558082859463852 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.007311129163281885 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 128 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 128 | | mean | 9.58 | | std | 8 | | cv | 0.835 | | sampleLengths | | 0 | 20 | | 1 | 28 | | 2 | 17 | | 3 | 18 | | 4 | 37 | | 5 | 11 | | 6 | 29 | | 7 | 17 | | 8 | 22 | | 9 | 19 | | 10 | 3 | | 11 | 10 | | 12 | 22 | | 13 | 17 | | 14 | 25 | | 15 | 10 | | 16 | 28 | | 17 | 28 | | 18 | 1 | | 19 | 9 | | 20 | 28 | | 21 | 25 | | 22 | 3 | | 23 | 3 | | 24 | 13 | | 25 | 3 | | 26 | 3 | | 27 | 8 | | 28 | 7 | | 29 | 9 | | 30 | 8 | | 31 | 16 | | 32 | 5 | | 33 | 16 | | 34 | 7 | | 35 | 4 | | 36 | 3 | | 37 | 3 | | 38 | 26 | | 39 | 4 | | 40 | 4 | | 41 | 3 | | 42 | 8 | | 43 | 4 | | 44 | 6 | | 45 | 7 | | 46 | 8 | | 47 | 3 | | 48 | 9 | | 49 | 7 |
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| 30.47% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 25 | | diversityRatio | 0.2421875 | | totalSentences | 128 | | uniqueOpeners | 31 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 121 | | matches | (empty) | | ratio | 0 | |
| 18.35% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 61 | | totalSentences | 121 | | matches | | 0 | "She shook the rain from" | | 1 | "She should have gone upstairs" | | 2 | "He paused when she entered," | | 3 | "He wore the silver signet" | | 4 | "He didn't speak." | | 5 | "He never did when she" | | 6 | "He just poured a water" | | 7 | "She was reaching for the" | | 8 | "She wore a coat that" | | 9 | "They hadn't spoken in three" | | 10 | "She didn't wave." | | 11 | "She just waited." | | 12 | "Her boots felt heavy on" | | 13 | "She slid into the booth" | | 14 | "You look well, Eva said." | | 15 | "Her voice was the same," | | 16 | "I look tired, Eva." | | 17 | "You look alive." | | 18 | "He is the landlord." | | 19 | "He looks like a man" |
| | ratio | 0.504 | |
| 46.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 121 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Aurora Carter pushed open the" | | 2 | "She shook the rain from" | | 3 | "The delivery bag from Golden" | | 4 | "She should have gone upstairs" | | 5 | "The walls were a collage" | | 6 | "Silas stood behind the bar," | | 7 | "He paused when she entered," | | 8 | "He wore the silver signet" | | 9 | "He didn't speak." | | 10 | "He never did when she" | | 11 | "He just poured a water" | | 12 | "Rory moved toward her usual" | | 13 | "The crescent-shaped scar there itched" | | 14 | "She was reaching for the" | | 15 | "She wore a coat that" | | 16 | "The air left Rory's lungs" | | 17 | "They hadn't spoken in three" | | 18 | "Eva had been the one" | | 19 | "Safety required silence." |
| | ratio | 0.826 | |
| 82.64% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 121 | | matches | | 0 | "Now I find you in" | | 1 | "Now you look like you" |
| | ratio | 0.017 | |
| 93.02% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 3 | | matches | | 0 | "Aurora Carter pushed open the door of The Raven's Nest, the bell above the frame chiming a dull, bronze note that smelled of brass polish and stale beer." | | 1 | "The walls were a collage of forgotten geography; old maps of London and black-and-white photographs of men in suits who looked like they knew where the bodies w…" | | 2 | "The crescent-shaped scar there itched when the pressure dropped, a phantom ache from a childhood accident that felt like it belonged to a different person." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |