| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 79.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1686 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "loosely" | | 1 | "slowly" | | 2 | "softly" | | 3 | "truly" | | 4 | "sharply" | | 5 | "very" |
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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) | |
| 37.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1686 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | |
| | highlights | | 0 | "chill" | | 1 | "trembled" | | 2 | "sentinel" | | 3 | "silence" | | 4 | "affection" | | 5 | "traced" | | 6 | "raced" | | 7 | "navigating" | | 8 | "weight" | | 9 | "unspoken" | | 10 | "fractured" | | 11 | "long shadow" | | 12 | "measured" | | 13 | "rhythmic" | | 14 | "resolving" | | 15 | "standard" | | 16 | "unreadable" | | 17 | "warmth" | | 18 | "flickered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 1 | | found | | 0 | | label | "air was thick with" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "the air was thick with" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 192 | | matches | (empty) | |
| 83.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 6 | | hedgeCount | 2 | | narrationSentences | 192 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 192 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1686 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 11 | | matches | | 0 | "Upstairs or downstairs tonight, he asked, his voice a low gravel that seemed to vibrate through the floorboards." | | 1 | "Thomas, she said, the name slipping out before she could weigh it." | | 2 | "Rory, he said." | | 3 | "Neither does the city above us, Aurora said softly." | | 4 | "They do, she said, though the certainty in her voice felt thinner now." | | 5 | "I ran, she said finally." | | 6 | "Probably neither of us would be standing here, she said." | | 7 | "Long time, he asked." | | 8 | "Years, she said." | | 9 | "Upstairs or downstairs tonight, he asked again, mirroring the first question, as if the loop had finally closed." | | 10 | "Down, she said." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 63 | | wordCount | 1686 | | uniqueNames | 23 | | maxNameDensity | 0.71 | | worstName | "You" | | maxWindowNameDensity | 2 | | worstWindowName | "You" | | discoveredNames | | Soho | 2 | | London | 2 | | Raven | 1 | | Nest | 2 | | Aurora | 9 | | Silas | 7 | | Thomas | 7 | | Cardiff | 2 | | Harrow | 1 | | Vane | 1 | | Kensington | 1 | | Yu-Fei | 1 | | Sundays | 1 | | Rory | 4 | | Evan | 1 | | Maida | 1 | | Vale | 1 | | Eva | 3 | | Leave | 1 | | Tom | 1 | | Si | 1 | | Carter | 1 | | You | 12 |
| | persons | | 0 | "Raven" | | 1 | "Aurora" | | 2 | "Silas" | | 3 | "Thomas" | | 4 | "Rory" | | 5 | "Evan" | | 6 | "Eva" | | 7 | "Tom" | | 8 | "Carter" | | 9 | "You" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Cardiff" | | 3 | "Harrow" | | 4 | "Kensington" | | 5 | "Yu-Fei" | | 6 | "Maida" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 104 | | glossingSentenceCount | 2 | | matches | | 0 | "gravel that seemed to vibrate through the floorboards" | | 1 | "tasted like copper and regret" |
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| 81.38% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.186 | | wordCount | 1686 | | matches | | 0 | "not to listen, but his posture remained angled toward them, a silent sentinel" | | 1 | "not as a burden, but as ballast" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 192 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 38.32 | | std | 34.99 | | cv | 0.913 | | sampleLengths | | 0 | 160 | | 1 | 18 | | 2 | 29 | | 3 | 132 | | 4 | 12 | | 5 | 33 | | 6 | 15 | | 7 | 55 | | 8 | 26 | | 9 | 7 | | 10 | 53 | | 11 | 8 | | 12 | 38 | | 13 | 30 | | 14 | 52 | | 15 | 49 | | 16 | 107 | | 17 | 17 | | 18 | 59 | | 19 | 96 | | 20 | 81 | | 21 | 41 | | 22 | 4 | | 23 | 77 | | 24 | 32 | | 25 | 36 | | 26 | 22 | | 27 | 10 | | 28 | 37 | | 29 | 32 | | 30 | 13 | | 31 | 38 | | 32 | 3 | | 33 | 29 | | 34 | 74 | | 35 | 4 | | 36 | 3 | | 37 | 6 | | 38 | 3 | | 39 | 21 | | 40 | 26 | | 41 | 18 | | 42 | 7 | | 43 | 73 |
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| 99.78% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 192 | | matches | | 0 | "was combed" | | 1 | "was cropped" | | 2 | "were gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 325 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 192 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1698 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 64 | | adverbRatio | 0.03769140164899882 | | lyAdverbCount | 23 | | lyAdverbRatio | 0.013545347467608953 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 192 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 192 | | mean | 8.78 | | std | 7.24 | | cv | 0.825 | | sampleLengths | | 0 | 19 | | 1 | 18 | | 2 | 6 | | 3 | 16 | | 4 | 34 | | 5 | 18 | | 6 | 15 | | 7 | 23 | | 8 | 11 | | 9 | 18 | | 10 | 9 | | 11 | 17 | | 12 | 2 | | 13 | 1 | | 14 | 20 | | 15 | 21 | | 16 | 20 | | 17 | 27 | | 18 | 25 | | 19 | 7 | | 20 | 12 | | 21 | 12 | | 22 | 2 | | 23 | 5 | | 24 | 8 | | 25 | 5 | | 26 | 13 | | 27 | 3 | | 28 | 9 | | 29 | 1 | | 30 | 2 | | 31 | 7 | | 32 | 13 | | 33 | 19 | | 34 | 16 | | 35 | 11 | | 36 | 5 | | 37 | 2 | | 38 | 8 | | 39 | 7 | | 40 | 3 | | 41 | 10 | | 42 | 4 | | 43 | 3 | | 44 | 9 | | 45 | 4 | | 46 | 16 | | 47 | 2 | | 48 | 2 | | 49 | 8 |
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| 64.06% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.421875 | | totalSentences | 192 | | uniqueOpeners | 81 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 169 | | matches | | 0 | "Just a dry, humorless laugh." | | 1 | "Still pre-law on my own" | | 2 | "Still reading the footnotes." | | 3 | "Still believing the rules matter" | | 4 | "Just not for everyone." | | 5 | "Maybe fortresses keep out arrows." | | 6 | "Probably neither of us would" | | 7 | "Only when the literal truths" |
| | ratio | 0.047 | |
| 49.59% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 72 | | totalSentences | 169 | | matches | | 0 | "His grey-streaked auburn hair was" | | 1 | "He noticed her first, the" | | 2 | "He didn’t smile, but his" | | 3 | "She just needed to feel" | | 4 | "Her attention slid past the" | | 5 | "He wore a charcoal suit" | | 6 | "His hair was cropped short," | | 7 | "Her straight, shoulder-length black hair" | | 8 | "His pupils dilated, then narrowed." | | 9 | "His voice was deeper, flattened" | | 10 | "She pulled out the chair" | | 11 | "You’re late, Thomas said, but" | | 12 | "I forget which way the" | | 13 | "It always does." | | 14 | "He ran a thumb over" | | 15 | "I heard you left." | | 16 | "I never pictured this." | | 17 | "He glanced around at the" | | 18 | "He smiled, but it didn’t" | | 19 | "He paused, swallowing hard." |
| | ratio | 0.426 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 116 | | totalSentences | 169 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Aurora pulled her wool coat" | | 2 | "The Raven’s Nest swallowed her" | | 3 | "Silas stood behind the mahogany" | | 4 | "His grey-streaked auburn hair was" | | 5 | "He noticed her first, the" | | 6 | "He didn’t smile, but his" | | 7 | "She just needed to feel" | | 8 | "Her attention slid past the" | | 9 | "He wore a charcoal suit" | | 10 | "His hair was cropped short," | | 11 | "Rory stopped breathing for half" | | 12 | "Her straight, shoulder-length black hair" | | 13 | "Thomas, she said, the name" | | 14 | "Time did its quiet violence." | | 15 | "His pupils dilated, then narrowed." | | 16 | "The glass in his hand" | | 17 | "Rory, he said." | | 18 | "His voice was deeper, flattened" | | 19 | "She pulled out the chair" |
| | ratio | 0.686 | |
| 29.59% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 169 | | matches | | | ratio | 0.006 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 11 | | matches | | 0 | "Low amber light bled from brass sconces, catching the dust motes that drifted over walls papered with faded cartography and black-and-white photographs of stran…" | | 1 | "Silas stood behind the mahogany counter, polishing a tumbler with a linen rag that had seen better centuries." | | 2 | "Upstairs or downstairs tonight, he asked, his voice a low gravel that seemed to vibrate through the floorboards." | | 3 | "As she moved toward a corner booth, her gaze swept past the tall oak bookshelf that lined the far wall." | | 4 | "He wore a charcoal suit that cost more than her monthly rent, but the fabric hung loosely on shoulders that had begun to slope under invisible weights." | | 5 | "His hair was cropped short, salted with premature grey, and his face bore the careful exhaustion of someone who smiled for clients until it ached." | | 6 | "She traced the small crescent-shaped scar on her left wrist, a childhood reflex that grounded her when conversations tilted too close to the bone." | | 7 | "She thought of Evan, of the slammed doors and the suffocating apartment in Maida Vale, of Eva’s urgent texts that read Leave." | | 8 | "For a moment, the careful armor slipped, revealing the twenty-two-year-old who had shared cheap lager and loud debates about contract law in cramped library car…" | | 9 | "Upstairs or downstairs tonight, he asked again, mirroring the first question, as if the loop had finally closed." | | 10 | "The green neon sign flickered outside, buzzing against the damp dark, and inside the Nest, the maps on the walls held true north for everyone who knew how to re…" |
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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 | |