| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 40 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 85.90% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1418 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "very" | | 1 | "carefully" | | 2 | "lazily" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 18.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1418 | | totalAiIsms | 23 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "echoed" | | 1 | "profound" | | 2 | "scanned" | | 3 | "standard" | | 4 | "pristine" | | 5 | "perfect" | | 6 | "tracing" | | 7 | "intricate" | | 8 | "resolved" | | 9 | "etched" | | 10 | "flickered" | | 11 | "raced" | | 12 | "gloom" | | 13 | "glint" | | 14 | "silence" | | 15 | "familiar" | | 16 | "quivered" | | 17 | "grave" | | 18 | "spectral" | | 19 | "echoing" |
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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 | 0 | | narrationSentences | 112 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 112 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 131 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1410 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 981 | | uniqueNames | 16 | | maxNameDensity | 1.22 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Pritchard" | | discoveredNames | | Victorian-era | 1 | | Morris | 2 | | Italian | 1 | | Harlow | 12 | | Weatherby | 4 | | Pritchard | 10 | | Camden | 1 | | Oxford | 1 | | London | 1 | | Veil | 3 | | Market | 1 | | Tube | 1 | | Compass | 1 | | Shade | 1 | | Home | 1 | | Counties | 1 |
| | persons | | 0 | "Morris" | | 1 | "Harlow" | | 2 | "Weatherby" | | 3 | "Pritchard" | | 4 | "Market" |
| | places | | 0 | "Oxford" | | 1 | "London" | | 2 | "Veil" | | 3 | "Compass" | | 4 | "Home" |
| | globalScore | 0.888 | | windowScore | 0.833 | |
| 82.43% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like crushed white chalk" | | 1 | "It was as if Weatherby had materialised in the centre and dropped dead on the spot" |
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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 | 1410 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 131 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 40 | | mean | 35.25 | | std | 23.77 | | cv | 0.674 | | sampleLengths | | 0 | 53 | | 1 | 37 | | 2 | 44 | | 3 | 71 | | 4 | 38 | | 5 | 69 | | 6 | 22 | | 7 | 44 | | 8 | 16 | | 9 | 5 | | 10 | 90 | | 11 | 24 | | 12 | 46 | | 13 | 6 | | 14 | 64 | | 15 | 11 | | 16 | 27 | | 17 | 41 | | 18 | 21 | | 19 | 14 | | 20 | 74 | | 21 | 45 | | 22 | 5 | | 23 | 54 | | 24 | 4 | | 25 | 56 | | 26 | 80 | | 27 | 19 | | 28 | 18 | | 29 | 76 | | 30 | 9 | | 31 | 54 | | 32 | 44 | | 33 | 14 | | 34 | 9 | | 35 | 24 | | 36 | 12 | | 37 | 18 | | 38 | 19 | | 39 | 33 |
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| 83.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 7 | | totalSentences | 112 | | matches | | 0 | "was sprawled" | | 1 | "was frozen" | | 2 | "been marked" | | 3 | "was etched" | | 4 | "been translated" | | 5 | "been terminated" | | 6 | "been pushed" | | 7 | "was locked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 149 | | matches | | |
| 77.43% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 2 | | flaggedSentences | 3 | | totalSentences | 131 | | ratio | 0.023 | | matches | | 0 | "The air here didn’t just smell of damp and mildew; it carried the sharp, metallic tang of old magic, ozone, and something far less pleasant." | | 1 | "As she squinted, faint, intricate patterns resolved in the chalk – interlocking spirals, angular sigils." | | 2 | "He hadn’t been translated; he’d been terminated." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 621 | | adjectiveStacks | 1 | | stackExamples | | 0 | "single, mud-caked trainer," |
| | adverbCount | 11 | | adverbRatio | 0.017713365539452495 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.001610305958132045 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 131 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 131 | | mean | 10.76 | | std | 7.44 | | cv | 0.691 | | sampleLengths | | 0 | 13 | | 1 | 25 | | 2 | 15 | | 3 | 10 | | 4 | 11 | | 5 | 16 | | 6 | 15 | | 7 | 20 | | 8 | 9 | | 9 | 9 | | 10 | 17 | | 11 | 6 | | 12 | 27 | | 13 | 3 | | 14 | 2 | | 15 | 7 | | 16 | 13 | | 17 | 25 | | 18 | 14 | | 19 | 4 | | 20 | 14 | | 21 | 7 | | 22 | 6 | | 23 | 8 | | 24 | 16 | | 25 | 10 | | 26 | 5 | | 27 | 7 | | 28 | 13 | | 29 | 31 | | 30 | 16 | | 31 | 5 | | 32 | 26 | | 33 | 5 | | 34 | 15 | | 35 | 2 | | 36 | 24 | | 37 | 9 | | 38 | 4 | | 39 | 5 | | 40 | 10 | | 41 | 14 | | 42 | 23 | | 43 | 23 | | 44 | 2 | | 45 | 4 | | 46 | 11 | | 47 | 21 | | 48 | 4 | | 49 | 28 |
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| 56.23% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3969465648854962 | | totalSentences | 131 | | uniqueOpeners | 52 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 102 | | matches | (empty) | | ratio | 0 | |
| 98.43% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 102 | | matches | | 0 | "Her worn leather watch felt" | | 1 | "His suit was expensive, Italian" | | 2 | "His face was frozen in" | | 3 | "She scanned the platform." | | 4 | "It was as if Weatherby" | | 5 | "she asked, though she already" | | 6 | "It wasn’t just a circle." | | 7 | "She’d seen their like before," | | 8 | "She’d filed the report." | | 9 | "It had been marked" | | 10 | "She pointed to a specific" | | 11 | "Her jaw tightened" | | 12 | "She shoved it down." | | 13 | "He checked the other." | | 14 | "It was a transaction gone" | | 15 | "She moved away from the" | | 16 | "It caught a glint of" | | 17 | "She stepped down onto the" | | 18 | "She fished the object out" | | 19 | "It spun lazily, then quivered," |
| | ratio | 0.304 | |
| 38.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 102 | | matches | | 0 | "Harlow’s boots echoed against the" | | 1 | "The air here didn’t just" | | 2 | "Her worn leather watch felt" | | 3 | "The name was out before" | | 4 | "A ghost of a habit," | | 5 | "The voice came from the" | | 6 | "A young man in a" | | 7 | "Harlow’s gaze never left the" | | 8 | "The body, male, mid-forties, was" | | 9 | "His suit was expensive, Italian" | | 10 | "His face was frozen in" | | 11 | "Nothing out of place, except" | | 12 | "Pritchard said, pointing a gloved" | | 13 | "Harlow stood, her movements sharp" | | 14 | "She scanned the platform." | | 15 | "It was as if Weatherby" | | 16 | "she asked, though she already" | | 17 | "Pritchard was by the book." | | 18 | "The book had no chapters" | | 19 | "Pritchard offered, his tone sceptical" |
| | ratio | 0.843 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 102 | | matches | (empty) | | ratio | 0 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 3 | | matches | | 0 | "Morris’s face, pale and confused in the flashing blue lights of an alley that shouldn’t have existed, flickered behind her eyes." | | 1 | "Something that didn’t belong in his world of stock options and champagne." | | 2 | "In the tunnel, two pinpricks of sour yellow light flickered to life, like eyes reflecting a torch that wasn’t there." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 5 | | matches | | 0 | "Harlow stood, her movements sharp with military precision" | | 1 | "Pritchard offered, his tone sceptical of his own suggestions" | | 2 | "Harlow walked, her eyes tracing the line" | | 3 | "Harlow said, her voice low" | | 4 | "she called, her voice cutting through the drip-drip of distant water" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "Harlow stated (state)" | | 1 | "Harlow muttered (mutter)" |
| | dialogueSentences | 40 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.25 | | effectiveRatio | 0.1 | |