| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said softly [softly]" |
| | dialogueSentences | 30 | | tagDensity | 0.533 | | leniency | 1 | | rawRatio | 0.063 | | effectiveRatio | 0.063 | |
| 82.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1461 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "softly" | | 1 | "perfectly" | | 2 | "really" |
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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) | |
| 21.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1461 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "electric" | | 1 | "could feel" | | 2 | "warmth" | | 3 | "weight" | | 4 | "tracing" | | 5 | "charged" | | 6 | "velvet" | | 7 | "gloom" | | 8 | "flicker" | | 9 | "lilt" | | 10 | "familiar" | | 11 | "traced" | | 12 | "silence" | | 13 | "porcelain" | | 14 | "tension" | | 15 | "whisper" | | 16 | "rhythmic" | | 17 | "reminder" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 86 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 86 | | filterMatches | (empty) | | hedgeMatches | | 0 | "tended to" | | 1 | "happened to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1455 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 0 | | matches | (empty) | |
| 95.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1189 | | uniqueNames | 16 | | maxNameDensity | 1.09 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 2 | | Nest | 3 | | Rory | 13 | | Tuesday | 1 | | Soho | 1 | | London | 2 | | Pre-Law | 1 | | Cardiff | 1 | | New | 1 | | York | 1 | | Julian | 12 | | Golden | 1 | | Empress | 1 | | Evan | 1 | | Prague | 1 | | Silas | 5 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Rory" | | 3 | "Julian" | | 4 | "Evan" | | 5 | "Silas" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Cardiff" | | 3 | "New" | | 4 | "York" | | 5 | "Prague" |
| | globalScore | 0.953 | | windowScore | 1 | |
| 5.07% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 4 | | matches | | 0 | "looked like a sketch of someone she used" | | 1 | "felt like a stone in her mouth, heavy a" | | 2 | "felt like a canyon" | | 3 | "quite reach his eyes" |
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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 | 1455 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 100 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 35.49 | | std | 21.02 | | cv | 0.592 | | sampleLengths | | 0 | 79 | | 1 | 45 | | 2 | 66 | | 3 | 55 | | 4 | 6 | | 5 | 54 | | 6 | 3 | | 7 | 64 | | 8 | 27 | | 9 | 18 | | 10 | 72 | | 11 | 11 | | 12 | 31 | | 13 | 28 | | 14 | 49 | | 15 | 13 | | 16 | 50 | | 17 | 27 | | 18 | 19 | | 19 | 53 | | 20 | 24 | | 21 | 47 | | 22 | 17 | | 23 | 63 | | 24 | 48 | | 25 | 11 | | 26 | 60 | | 27 | 11 | | 28 | 23 | | 29 | 42 | | 30 | 60 | | 31 | 22 | | 32 | 50 | | 33 | 17 | | 34 | 23 | | 35 | 26 | | 36 | 32 | | 37 | 7 | | 38 | 31 | | 39 | 5 | | 40 | 66 |
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| 88.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 86 | | matches | | 0 | "been blurred" | | 1 | "was guarded" | | 2 | "was intended" | | 3 | "was tempered" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 193 | | matches | | 0 | "was sitting" | | 1 | "was still drumming" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 3 | | flaggedSentences | 7 | | totalSentences | 100 | | ratio | 0.07 | | matches | | 0 | "He didn't ask why she looked frayed; he simply slid a glass of sparkling water toward her." | | 1 | "He looked like a sketch of someone she used to know—the same sharp jawline, the same way he tilted his head when he listened, but the edges had been blurred by time and something harder." | | 2 | "Julian looked at her—really looked at her—and for a second, the mask slipped." | | 3 | "Rory looked at him—really looked at the hollows of his cheeks and the sterile perfection of his suit—and realized that while she had lost her status, he had lost his soul." | | 4 | "They weren't the lawyer and the delivery girl; they were two twenty-year-olds in a rain-slicked city, terrified of the future and desperate for someone to tell them they were enough." | | 5 | "Silas knew the anatomy of a reunion; he had seen a thousand of them in the Nest." | | 6 | "She thought of the life she had built—the small flat, the friendship with Silas, the quiet peace of her own anonymity." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1204 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, crescent-shaped scar" |
| | adverbCount | 30 | | adverbRatio | 0.024916943521594685 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.01079734219269103 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 14.55 | | std | 9.57 | | cv | 0.658 | | sampleLengths | | 0 | 21 | | 1 | 33 | | 2 | 25 | | 3 | 24 | | 4 | 21 | | 5 | 10 | | 6 | 29 | | 7 | 10 | | 8 | 17 | | 9 | 14 | | 10 | 16 | | 11 | 11 | | 12 | 14 | | 13 | 6 | | 14 | 19 | | 15 | 35 | | 16 | 3 | | 17 | 12 | | 18 | 52 | | 19 | 3 | | 20 | 5 | | 21 | 7 | | 22 | 12 | | 23 | 18 | | 24 | 11 | | 25 | 9 | | 26 | 16 | | 27 | 20 | | 28 | 16 | | 29 | 11 | | 30 | 7 | | 31 | 6 | | 32 | 5 | | 33 | 10 | | 34 | 3 | | 35 | 6 | | 36 | 22 | | 37 | 13 | | 38 | 36 | | 39 | 8 | | 40 | 5 | | 41 | 7 | | 42 | 4 | | 43 | 20 | | 44 | 10 | | 45 | 9 | | 46 | 8 | | 47 | 12 | | 48 | 7 | | 49 | 14 |
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| 40.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.26 | | totalSentences | 100 | | uniqueOpeners | 26 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 22.60% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 77 | | matches | | 0 | "It was a rainy Tuesday" | | 1 | "He wore a dark waistcoat" | | 2 | "He caught Rory’s eye and" | | 3 | "He didn't ask why she" | | 4 | "She shifted her weight, her" | | 5 | "It was a nervous habit," | | 6 | "He was sitting in a" | | 7 | "He looked like a sketch" | | 8 | "He looked up." | | 9 | "His eyes narrowed, then widened." | | 10 | "His voice had deepened, losing" | | 11 | "She walked toward him, her" | | 12 | "His expression was guarded, a" | | 13 | "she said, sliding into the" | | 14 | "He didn't reach for her" | | 15 | "He didn't even lean in." | | 16 | "He sat perfectly still, a" | | 17 | "He saw the fatigue under" | | 18 | "he said softly" | | 19 | "She didn't mention Evan." |
| | ratio | 0.494 | |
| 5.45% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 77 | | matches | | 0 | "The green neon sign of" | | 1 | "It was a rainy Tuesday" | | 2 | "Rory pushed through the heavy" | | 3 | "Maps of forgotten empires and" | | 4 | "Silas was behind the bar," | | 5 | "He wore a dark waistcoat" | | 6 | "He caught Rory’s eye and" | | 7 | "He didn't ask why she" | | 8 | "Rory leaned against the bar," | | 9 | "She shifted her weight, her" | | 10 | "It was a nervous habit," | | 11 | "Tonight, however, the air felt" | | 12 | "That was when she saw" | | 13 | "He was sitting in a" | | 14 | "He looked like a sketch" | | 15 | "The name felt like a" | | 16 | "Julian had been the center" | | 17 | "He looked up." | | 18 | "His eyes narrowed, then widened." | | 19 | "The distance between the bar" |
| | ratio | 0.909 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 77 | | matches | (empty) | | ratio | 0 | |
| 35.04% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 8 | | matches | | 0 | "Inside, the atmosphere was a thick blend of old tobacco, expensive gin, and the muffled murmur of people who didn't want to be overheard." | | 1 | "He wore a dark waistcoat that hugged his broad shoulders, and as he reached for a highball glass, the silver signet ring on his right hand caught the light." | | 2 | "Julian had been the center of her world during those suffocating years of Pre-Law in Cardiff, the one person who had seen the flicker of rebellion in her and en…" | | 3 | "His expression was guarded, a polished mask of professional indifference that didn't quite reach his eyes." | | 4 | "He saw the fatigue under her bright blue eyes, the way she held herself with a defensive stiffness, and the utter lack of the ambition that had once fueled thei…" | | 5 | "The silence that followed was heavy, thick with the weight of the years they had spent pretending the other didn't exist." | | 6 | "It was the silence of things left unsaid: the apologies that had arrived too late, the phone calls that were never made, the shared dreams that had decayed into…" | | 7 | "Rory pushed through the heavy oak door and stepped back out into the London rain, the green neon sign of The Raven's Nest buzzing behind her, fading into the mi…" |
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| 62.50% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 2 | | matches | | 0 | "she said, her voice steady" | | 1 | "he asked, his voice barely a whisper" |
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| 16.67% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 4 | | fancyTags | | 0 | "she whispered (whisper)" | | 1 | "Julian murmured (murmur)" | | 2 | "Rory corrected (correct)" | | 3 | "she lied (lie)" |
| | dialogueSentences | 30 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0.267 | | effectiveRatio | 0.267 | |