| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 84.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1648 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "perfectly" | | 1 | "truly" | | 2 | "softly" | | 3 | "quickly" | | 4 | "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) | |
| 30.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1648 | | totalAiIsms | 23 | | found | | | highlights | | 0 | "calculating" | | 1 | "familiar" | | 2 | "measured" | | 3 | "sanctuary" | | 4 | "clandestine" | | 5 | "silence" | | 6 | "footsteps" | | 7 | "stomach" | | 8 | "lilt" | | 9 | "glinting" | | 10 | "traced" | | 11 | "weight" | | 12 | "unspoken" | | 13 | "throbbed" | | 14 | "electric" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 175 | | matches | (empty) | |
| 77.55% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 6 | | hedgeCount | 2 | | narrationSentences | 175 | | filterMatches | | 0 | "look" | | 1 | "know" | | 2 | "think" | | 3 | "see watch" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 175 | | 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 | 1648 | | ratio | 0 | | matches | (empty) | |
| 58.33% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 4 | | matches | | 0 | "You look tired, he said softly." | | 1 | "We should have stayed in touch, Elias said, his voice roughening." | | 2 | "We were twenty-two, she replied." | | 3 | "Outside, London continued its relentless march." |
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| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 63 | | wordCount | 1648 | | uniqueNames | 24 | | maxNameDensity | 0.67 | | worstName | "You" | | maxWindowNameDensity | 3 | | worstWindowName | "You" | | discoveredNames | | Raven | 3 | | Nest | 3 | | Soho | 2 | | Carter | 1 | | Evan | 1 | | London | 3 | | Brendan | 1 | | Jennifer | 1 | | Prague | 1 | | Cardiff | 4 | | Bay | 1 | | Welsh | 1 | | City | 1 | | Yu-Fei | 1 | | Cheung | 1 | | Golden | 1 | | Empress | 1 | | House | 1 | | Lords | 1 | | Elias | 8 | | Aurora | 5 | | Rory | 4 | | Silas | 6 | | You | 11 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Carter" | | 3 | "Evan" | | 4 | "Brendan" | | 5 | "Jennifer" | | 6 | "Yu-Fei" | | 7 | "Cheung" | | 8 | "Elias" | | 9 | "Aurora" | | 10 | "Rory" | | 11 | "Silas" | | 12 | "You" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Prague" | | 3 | "Cardiff" | | 4 | "Bay" | | 5 | "City" | | 6 | "House" | | 7 | "Lords" |
| | globalScore | 1 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 108 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like a language she had forgotten" | | 1 | "felt like a bridge, not a wall" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.607 | | wordCount | 1648 | | matches | | 0 | "not as an accusation, but as a fact" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 175 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 53.16 | | std | 48.33 | | cv | 0.909 | | sampleLengths | | 0 | 183 | | 1 | 199 | | 2 | 36 | | 3 | 1 | | 4 | 101 | | 5 | 21 | | 6 | 20 | | 7 | 29 | | 8 | 54 | | 9 | 40 | | 10 | 14 | | 11 | 81 | | 12 | 15 | | 13 | 36 | | 14 | 45 | | 15 | 37 | | 16 | 51 | | 17 | 17 | | 18 | 44 | | 19 | 10 | | 20 | 63 | | 21 | 32 | | 22 | 33 | | 23 | 57 | | 24 | 66 | | 25 | 55 | | 26 | 19 | | 27 | 67 | | 28 | 36 | | 29 | 14 | | 30 | 172 |
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| 87.22% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 9 | | totalSentences | 175 | | matches | | 0 | "were buried" | | 1 | "been seven" | | 2 | "been told" | | 3 | "was gone" | | 4 | "been honed" | | 5 | "was raided" | | 6 | "was supposed" | | 7 | "were supposed" | | 8 | "were calloused" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 299 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 175 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1663 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 58 | | adverbRatio | 0.03487672880336741 | | lyAdverbCount | 15 | | lyAdverbRatio | 0.009019843656043296 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 175 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 175 | | mean | 9.42 | | std | 7.3 | | cv | 0.775 | | sampleLengths | | 0 | 22 | | 1 | 16 | | 2 | 16 | | 3 | 23 | | 4 | 24 | | 5 | 26 | | 6 | 18 | | 7 | 11 | | 8 | 11 | | 9 | 11 | | 10 | 5 | | 11 | 11 | | 12 | 40 | | 13 | 22 | | 14 | 28 | | 15 | 15 | | 16 | 29 | | 17 | 22 | | 18 | 4 | | 19 | 12 | | 20 | 6 | | 21 | 10 | | 22 | 6 | | 23 | 13 | | 24 | 13 | | 25 | 4 | | 26 | 1 | | 27 | 3 | | 28 | 5 | | 29 | 29 | | 30 | 28 | | 31 | 19 | | 32 | 17 | | 33 | 1 | | 34 | 13 | | 35 | 7 | | 36 | 5 | | 37 | 3 | | 38 | 1 | | 39 | 11 | | 40 | 8 | | 41 | 16 | | 42 | 5 | | 43 | 2 | | 44 | 5 | | 45 | 23 | | 46 | 24 | | 47 | 8 | | 48 | 3 | | 49 | 5 |
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| 40.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.29714285714285715 | | totalSentences | 175 | | uniqueOpeners | 52 | |
| 64.10% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 156 | | matches | | 0 | "Somewhere behind a false bookshelf" | | 1 | "Just us, before the world" | | 2 | "Just don’t disappear again." |
| | ratio | 0.019 | |
| 9.74% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 82 | | totalSentences | 156 | | matches | | 0 | "Her straight shoulder-length black hair" | | 1 | "It had been years since" | | 2 | "She was twenty-five, but the" | | 3 | "Her father, Brendan, would have" | | 4 | "Her mother, Jennifer, would have" | | 5 | "He carried himself with a" | | 6 | "He had been a field" | | 7 | "He just poured, watched, and" | | 8 | "She lifted her gaze." | | 9 | "Her stomach tightened." | | 10 | "It had been seven years." | | 11 | "He stood in a charcoal" | | 12 | "His voice was smoother than" | | 13 | "I didn’t think I’d see" | | 14 | "She set her glass down." | | 15 | "You’re wearing a suit that" | | 16 | "He smiled, but it didn’t" | | 17 | "She nodded toward the bar." | | 18 | "He gave a barely perceptible" | | 19 | "I’m in the City now." |
| | ratio | 0.526 | |
| 43.33% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 130 | | totalSentences | 156 | | matches | | 0 | "The green neon sign of" | | 1 | "Aurora Carter kept to the" | | 2 | "Her straight shoulder-length black hair" | | 3 | "It had been years since" | | 4 | "She was twenty-five, but the" | | 5 | "Pre-law textbooks and debate trophies" | | 6 | "Cardiff felt like a language" | | 7 | "Her father, Brendan, would have" | | 8 | "Her mother, Jennifer, would have" | | 9 | "Rory just called it survival." | | 10 | "Silas moved behind the polished" | | 11 | "The silver signet ring on" | | 12 | "He carried himself with a" | | 13 | "He had been a field" | | 14 | "Silas never asked questions." | | 15 | "He just poured, watched, and" | | 16 | "That was why she came" | | 17 | "That was why she lived" | | 18 | "A chair scraped against the" | | 19 | "Aurora didn’t look up until" |
| | ratio | 0.833 | |
| 96.15% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 156 | | matches | | 0 | "Now he ran The Raven’s" | | 1 | "Now you’re just surviving." | | 2 | "If you ever want a" |
| | ratio | 0.019 | |
| 48.32% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 9 | | matches | | 0 | "The silver signet ring on his right hand caught the low light as he polished a glass, his neatly trimmed grey-streaked auburn beard framing a face that had lear…" | | 1 | "He carried himself with a quiet authority that made the rowdy Soho patrons lower their voices and lean back in their seats." | | 2 | "He stood in a charcoal suit that fit too perfectly, his posture rigid with the kind of confidence that comes from knowing you have never been told no." | | 3 | "In his place stood a man who had been honed into something sharp, polished, and entirely self-contained." | | 4 | "Silas had paused in his polishing, his hazel eyes flicking toward them with the quiet calculation of a man who recognized old ghosts." | | 5 | "He had the corner office, the polished veneer, the life that looked right from the outside." | | 6 | "She had the flat above a bar, the delivery routes for Yu-Fei Cheung’s Golden Empress, the scar on her wrist, the quiet vigilance of waking up in a city that had…" | | 7 | "She said I became someone who looked at her the way I looked at a spreadsheet." | | 8 | "The hidden room behind the bookshelf remained closed, its secrets safe, but the quiet understanding that passed between the three of them was enough." |
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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 | |