| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 1 | | adverbTags | | 0 | "The name landed like [like]" |
| | dialogueSentences | 55 | | tagDensity | 0.418 | | leniency | 0.836 | | rawRatio | 0.043 | | effectiveRatio | 0.036 | |
| 97.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1675 | | totalAiIsmAdverbs | 1 | | 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) | |
| 64.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1675 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "flickered" | | 1 | "weight" | | 2 | "familiar" | | 3 | "warmth" | | 4 | "chill" | | 5 | "unsettled" | | 6 | "measured" | | 7 | "echoing" | | 8 | "unspoken" | | 9 | "silence" | | 10 | "flicker" |
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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 | 0 | | narrationSentences | 88 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 88 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 117 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 12 | | markdownWords | 42 | | totalWords | 1671 | | ratio | 0.025 | | matches | | 0 | "The Raven’s Nest" | | 1 | "Yu-Fei’s Golden Empress" | | 2 | "barrister" | | 3 | "the Spymaster" | | 4 | "good timing" | | 5 | "clink" | | 6 | "Let me be the kid who needed this." | | 7 | "her mother’s patience at the school gates" | | 8 | "Llun" | | 9 | "a few" | | 10 | "Rory, when you thought of ‘public service,’ did you think of…”" | | 11 | "P.S." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1144 | | uniqueNames | 17 | | maxNameDensity | 1.31 | | worstName | "Silas" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Aurora | 1 | | Carter | 1 | | Golden | 2 | | November | 1 | | Blackwood | 1 | | Dai | 1 | | Silas | 15 | | Evan | 2 | | Eva | 1 | | Moscow | 1 | | Lagos | 1 | | Prague | 1 | | Let | 1 | | Wales | 1 | | Cardiff | 1 | | Empress | 2 |
| | persons | | 0 | "Raven" | | 1 | "Aurora" | | 2 | "Carter" | | 3 | "November" | | 4 | "Blackwood" | | 5 | "Silas" | | 6 | "Evan" | | 7 | "Eva" | | 8 | "Empress" |
| | places | | 0 | "Moscow" | | 1 | "Lagos" | | 2 | "Prague" | | 3 | "Wales" | | 4 | "Cardiff" |
| | globalScore | 0.844 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.598 | | wordCount | 1671 | | matches | | 0 | "not in suspicion, but in a kind of slow recognition of something in the scullery o" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 117 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 34.81 | | std | 25.33 | | cv | 0.728 | | sampleLengths | | 0 | 87 | | 1 | 115 | | 2 | 24 | | 3 | 67 | | 4 | 30 | | 5 | 81 | | 6 | 24 | | 7 | 39 | | 8 | 15 | | 9 | 18 | | 10 | 63 | | 11 | 73 | | 12 | 43 | | 13 | 43 | | 14 | 53 | | 15 | 40 | | 16 | 12 | | 17 | 28 | | 18 | 18 | | 19 | 49 | | 20 | 45 | | 21 | 20 | | 22 | 16 | | 23 | 24 | | 24 | 46 | | 25 | 15 | | 26 | 50 | | 27 | 48 | | 28 | 8 | | 29 | 25 | | 30 | 2 | | 31 | 3 | | 32 | 96 | | 33 | 29 | | 34 | 6 | | 35 | 24 | | 36 | 8 | | 37 | 28 | | 38 | 1 | | 39 | 18 | | 40 | 52 | | 41 | 27 | | 42 | 2 | | 43 | 33 | | 44 | 14 | | 45 | 41 | | 46 | 46 | | 47 | 22 |
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| 93.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 88 | | matches | | 0 | "been swapped" | | 1 | "was lined" | | 2 | "been torn" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 188 | | matches | | |
| 20.76% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 1 | | flaggedSentences | 5 | | totalSentences | 117 | | ratio | 0.043 | | matches | | 0 | "The bar’s familiar scent—a blend of bourbon and old paper—hit her first." | | 1 | "Black-and-white faces from decades past stared out from behind smudged glass; she wondered if any of them had ever slipped through that hidden bookshelf into the secret room beyond." | | 2 | "His hazel eyes narrowed—not in suspicion, but in a kind of slow recognition of something in the scullery of a woman now perched on a stool." | | 3 | "But names stuck, she’d learned—like Evan’s fingerprints." | | 4 | "“Break’s in twenty minutes, if you’re keeping track,” she said, though the lie was light—Eva had already locked up the flat and left for the night." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1119 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 38 | | adverbRatio | 0.03395889186773905 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004468275245755138 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 117 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 117 | | mean | 14.28 | | std | 8.21 | | cv | 0.575 | | sampleLengths | | 0 | 24 | | 1 | 12 | | 2 | 29 | | 3 | 22 | | 4 | 16 | | 5 | 20 | | 6 | 19 | | 7 | 31 | | 8 | 29 | | 9 | 24 | | 10 | 10 | | 11 | 26 | | 12 | 14 | | 13 | 17 | | 14 | 6 | | 15 | 24 | | 16 | 8 | | 17 | 24 | | 18 | 7 | | 19 | 15 | | 20 | 27 | | 21 | 12 | | 22 | 12 | | 23 | 13 | | 24 | 26 | | 25 | 7 | | 26 | 8 | | 27 | 16 | | 28 | 2 | | 29 | 4 | | 30 | 30 | | 31 | 2 | | 32 | 17 | | 33 | 10 | | 34 | 18 | | 35 | 18 | | 36 | 24 | | 37 | 13 | | 38 | 9 | | 39 | 24 | | 40 | 10 | | 41 | 9 | | 42 | 20 | | 43 | 6 | | 44 | 8 | | 45 | 6 | | 46 | 19 | | 47 | 16 | | 48 | 12 | | 49 | 29 |
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| 72.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.452991452991453 | | totalSentences | 117 | | uniqueOpeners | 53 | |
| 41.15% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 81 | | matches | | 0 | "Then Silas sighed, a sound" |
| | ratio | 0.012 | |
| 17.53% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 81 | | matches | | 0 | "She hadn’t meant to stop" | | 1 | "He hadn’t turned at her" | | 2 | "His signet ring gleamed in" | | 3 | "His hazel eyes narrowed—not in" | | 4 | "he said, his voice a" | | 5 | "She smiled, though it tightened" | | 6 | "He slid a whisky tumbler" | | 7 | "She took a measured sip," | | 8 | "she said, though the lie" | | 9 | "she said, glancing at the" | | 10 | "He studied her again." | | 11 | "He lit a lamp, and" | | 12 | "he said, nodding at her" | | 13 | "She sipped her drink again," | | 14 | "He turned his ring on" | | 15 | "He pointed to a chair," | | 16 | "She noticed he didn’t answer" | | 17 | "She meant the scar, but" | | 18 | "She placed the tumbler too" | | 19 | "His hand froze mid-turn." |
| | ratio | 0.506 | |
| 52.59% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 81 | | matches | | 0 | "The green neon above *The" | | 1 | "The bar’s familiar scent—a blend" | | 2 | "A delivery rider’s uniform still" | | 3 | "She hadn’t meant to stop" | | 4 | "Silas Blackwood was behind the" | | 5 | "He hadn’t turned at her" | | 6 | "His signet ring gleamed in" | | 7 | "The bar was nearly empty," | | 8 | "“ Dai lyfrau, I" | | 9 | "Silas looked up then, and" | | 10 | "His hazel eyes narrowed—not in" | | 11 | "he said, his voice a" | | 12 | "She smiled, though it tightened" | | 13 | "The almost-wink sidled between them" | | 14 | "The scar on her wrist" | | 15 | "He slid a whisky tumbler" | | 16 | "She took a measured sip," | | 17 | "she said, though the lie" | | 18 | "Silas tapped the counter, his" | | 19 | "she said, glancing at the" |
| | ratio | 0.815 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 81 | | matches | | 0 | "Now, leaning against Silas, who" | | 1 | "Now, it was lined with" |
| | ratio | 0.025 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 5 | | matches | | 0 | "Silas Blackwood was behind the bar, leaning on his forearm with a patience that unsettled her." | | 1 | "Now, leaning against Silas, who had once been *the Spymaster* in a hushed university conversation between her father’s cafes, the weight of it all felt suddenly…" | | 2 | "The bookshelf door breathed open, letting them into a room that smelled of pipe smoke and fainter regrets." | | 3 | "The man who had once whispered about classified satellite work to a curious undergraduate was ceding ground to a man who had limped away from ghosts every day." | | 4 | "She saw herself in the pause between them: a woman who had fled tennis courts and high-rise contracts only to find a different kind of high rise here." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 6 | | matches | | 0 | "he said, his voice a low, gravelly rasp that had not changed" | | 1 | "she said, though the lie was light—Eva had already locked up the flat and left for the night" | | 2 | "He pointed, his gait uneven but controlled" | | 3 | "She leaned, her voice softening like a blade retracted" | | 4 | "His voice had, warmer" | | 5 | "She pointed, the faces anonymized with black bars except one" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 55 | | tagDensity | 0.182 | | leniency | 0.364 | | rawRatio | 0 | | effectiveRatio | 0 | |