| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.90% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1408 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 57.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1408 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "familiar" | | 1 | "tracing" | | 2 | "weight" | | 3 | "traced" | | 4 | "silence" | | 5 | "dancing" | | 6 | "unspoken" | | 7 | "throb" |
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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 | 2 | | narrationSentences | 190 | | matches | | 0 | "was bitter" | | 1 | "e in fear" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 0 | | narrationSentences | 190 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 190 | | 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 | 1408 | | ratio | 0 | | matches | (empty) | |
| 71.43% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 3 | | matches | | 0 | "London suits you, he said instead, eyes drifting over her dark hair, the faded adhesive patch peeking from her cuff, the…" | | 1 | "I wrote to you, he said quietly." | | 2 | "For now, Rory said." |
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| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 1408 | | uniqueNames | 29 | | maxNameDensity | 0.57 | | worstName | "You" | | maxWindowNameDensity | 3 | | worstWindowName | "You" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Ordnance | 1 | | Survey | 1 | | Golden | 1 | | Empress | 1 | | Whitechapel | 2 | | Cardiff | 3 | | University | 1 | | Hayes | 1 | | Evan | 2 | | London | 2 | | Matthew | 7 | | Paddington | 1 | | Station | 1 | | Scotland | 1 | | Yard | 1 | | Road | 1 | | Oxford | 1 | | Street | 1 | | Crown | 1 | | Prosecution | 1 | | Service | 1 | | Matt | 2 | | Rory | 6 | | Ro | 1 | | You | 8 | | Didn | 5 |
| | persons | | 0 | "Survey" | | 1 | "Hayes" | | 2 | "Evan" | | 3 | "Matthew" | | 4 | "Matt" | | 5 | "Rory" | | 6 | "You" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Golden" | | 3 | "Whitechapel" | | 4 | "Cardiff" | | 5 | "University" | | 6 | "London" | | 7 | "Paddington" | | 8 | "Scotland" | | 9 | "Road" | | 10 | "Oxford" | | 11 | "Street" | | 12 | "Crown" |
| | globalScore | 1 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared, moving with that deliberate, uneven cadence that spoke of cartilage ground to dust and stubborn joints" |
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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 | 1408 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 190 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 2 | | mean | 0 | | std | 0 | | cv | 0 | | sampleLengths | | |
| 96.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 190 | | matches | | 0 | "was papered" | | 1 | "was combed" | | 2 | "get paid" | | 3 | "married" | | 4 | "are meant" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 257 | | matches | | 0 | "was studying" | | 1 | "was learning" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 190 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1418 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 50 | | adverbRatio | 0.03526093088857546 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.010578279266572637 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 190 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 190 | | mean | 7.41 | | std | 7.89 | | cv | 1.064 | | sampleLengths | | 0 | 14 | | 1 | 23 | | 2 | 26 | | 3 | 19 | | 4 | 27 | | 5 | 5 | | 6 | 40 | | 7 | 31 | | 8 | 27 | | 9 | 19 | | 10 | 19 | | 11 | 5 | | 12 | 2 | | 13 | 36 | | 14 | 6 | | 15 | 4 | | 16 | 27 | | 17 | 1 | | 18 | 2 | | 19 | 3 | | 20 | 39 | | 21 | 2 | | 22 | 3 | | 23 | 13 | | 24 | 13 | | 25 | 2 | | 26 | 10 | | 27 | 13 | | 28 | 17 | | 29 | 12 | | 30 | 1 | | 31 | 4 | | 32 | 1 | | 33 | 10 | | 34 | 1 | | 35 | 5 | | 36 | 3 | | 37 | 1 | | 38 | 2 | | 39 | 6 | | 40 | 7 | | 41 | 4 | | 42 | 12 | | 43 | 3 | | 44 | 7 | | 45 | 5 | | 46 | 6 | | 47 | 4 | | 48 | 7 | | 49 | 3 |
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| 71.05% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.47368421052631576 | | totalSentences | 190 | | uniqueOpeners | 90 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 9 | | totalSentences | 141 | | matches | | 0 | "Just a sliver of navy" | | 1 | "Just the quiet acknowledgment of" | | 2 | "Still thinks I'm doing an" | | 3 | "Just let the words settle" | | 4 | "Just a twitch of his" | | 5 | "Bright blue eyes holding steady" | | 6 | "Just tapped twice on the" | | 7 | "Especially when the water's cold." | | 8 | "Somewhere behind the false bookshelf" |
| | ratio | 0.064 | |
| 58.30% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 57 | | totalSentences | 141 | | matches | | 0 | "She wasn't here for drinks." | | 1 | "She had just finished a" | | 2 | "He moved with a quiet" | | 3 | "She took the stool farthest" | | 4 | "She knew that spine." | | 5 | "She had spent three years" | | 6 | "Her knees protested." | | 7 | "She smoothed her worn canvas" | | 8 | "His face was softer than" | | 9 | "His hair was combed back," | | 10 | "His voice was lower." | | 11 | "I thought you were dead." | | 12 | "She almost smiled." | | 13 | "She slid onto the adjacent" | | 14 | "You look expensive, Matthew." | | 15 | "He exhaled, a short burst" | | 16 | "I look tired." | | 17 | "He gestured to the bartender." | | 18 | "She'll have a pint of" | | 19 | "He didn't ask if she" |
| | ratio | 0.404 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 93 | | totalSentences | 141 | | matches | | 0 | "Rain slicked the cobblestones outside," | | 1 | "Aurora kept her collar turned" | | 2 | "The distinctive green neon sign" | | 3 | "Every square inch of plaster" | | 4 | "She wasn't here for drinks." | | 5 | "She had just finished a" | | 6 | "Silas barely looked up from" | | 7 | "He moved with a quiet" | | 8 | "She took the stool farthest" | | 9 | "That's when she saw him." | | 10 | "Rory's breath caught, sharp and" | | 11 | "She knew that spine." | | 12 | "She had spent three years" | | 13 | "Her knees protested." | | 14 | "She smoothed her worn canvas" | | 15 | "The floorboards groaned under her" | | 16 | "His face was softer than" | | 17 | "Lines fanned from his eyes," | | 18 | "His hair was combed back," | | 19 | "His voice was lower." |
| | ratio | 0.66 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 6 | | totalSentences | 141 | | matches | | 0 | "Because I realized I was" | | 1 | "Because the law is just" | | 2 | "Because some doors don't open" | | 3 | "Even when the water's cold?" | | 4 | "To the paths we didn't" | | 5 | "To the ones we did." |
| | ratio | 0.043 | |
| 34.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 7 | | matches | | 0 | "Silas barely looked up from polishing a highball glass behind the bar, only offering a slow nod that acknowledged the rain, the exhaustion, the simple fact that…" | | 1 | "He moved with a quiet authority that belied the slight limp in his left leg, his grey-streaked auburn hair neatly combed, his beard trimmed to the jawline." | | 2 | "Just a sliver of navy wool sleeve, the careful cadence of hands resting on a tumbler of neat scotch, the way his shoulders sat a fraction too rigid in a chair t…" | | 3 | "Didn't tell him about the bruises that bloomed like midnight flowers, about the nights she counted ceiling cracks until dawn, about the single suitcase she drag…" | | 4 | "he asked, glancing at her forearms, noting the lean muscle, the way she held herself coiled tight." | | 5 | "The man who had packed her bags without asking questions when she finally called from a phone booth off Oxford Street." | | 6 | "Silas appeared, moving with that deliberate, uneven cadence that spoke of cartilage ground to dust and stubborn joints." |
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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 | |