| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 26 | | adverbTagCount | 3 | | adverbTags | | 0 | "She turned slowly [slowly]" | | 1 | "Marcus said finally [finally]" | | 2 | "She gestured around [around]" |
| | dialogueSentences | 70 | | tagDensity | 0.371 | | leniency | 0.743 | | rawRatio | 0.115 | | effectiveRatio | 0.086 | |
| 83.85% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1548 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "really" | | 1 | "slowly" | | 2 | "very" |
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| 60.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 51.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1548 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "familiar" | | 1 | "lilt" | | 2 | "eyebrow" | | 3 | "silence" | | 4 | "unspoken" | | 5 | "etching" | | 6 | "optimized" | | 7 | "flicker" | | 8 | "perfect" | | 9 | "warmth" | | 10 | "uncanny" |
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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 | 92 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 92 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1533 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 48 | | wordCount | 952 | | uniqueNames | 24 | | maxNameDensity | 0.84 | | worstName | "Marcus" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Marcus" | | discoveredNames | | Silas | 5 | | Welsh | 3 | | London | 3 | | Cardiff | 1 | | Savile | 1 | | Row | 1 | | Raven | 2 | | Nest | 2 | | Macallan | 1 | | Mrs | 1 | | Winters | 2 | | Associates | 1 | | Rory | 6 | | Jade | 1 | | Empire | 1 | | Empress | 1 | | Chinese | 1 | | Yu-Fei | 2 | | Courtroom | 1 | | Dusty | 1 | | Springfield | 1 | | Evan | 1 | | British | 1 | | Marcus | 8 |
| | persons | | 0 | "Silas" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Mrs" | | 4 | "Winters" | | 5 | "Associates" | | 6 | "Rory" | | 7 | "Jade" | | 8 | "Empire" | | 9 | "Empress" | | 10 | "Yu-Fei" | | 11 | "Dusty" | | 12 | "Springfield" | | 13 | "Evan" | | 14 | "Marcus" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 77.54% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 69 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like her fifth gin and tonic, masc" | | 1 | "quite cover" |
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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 | 1533 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 135 | | matches | | 0 | "chose that moment" | | 1 | "hated that song" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 64 | | mean | 23.95 | | std | 17.76 | | cv | 0.741 | | sampleLengths | | 0 | 7 | | 1 | 36 | | 2 | 24 | | 3 | 80 | | 4 | 24 | | 5 | 48 | | 6 | 32 | | 7 | 4 | | 8 | 14 | | 9 | 29 | | 10 | 65 | | 11 | 15 | | 12 | 17 | | 13 | 4 | | 14 | 2 | | 15 | 3 | | 16 | 4 | | 17 | 46 | | 18 | 9 | | 19 | 25 | | 20 | 16 | | 21 | 5 | | 22 | 23 | | 23 | 50 | | 24 | 9 | | 25 | 18 | | 26 | 24 | | 27 | 46 | | 28 | 5 | | 29 | 53 | | 30 | 12 | | 31 | 16 | | 32 | 17 | | 33 | 2 | | 34 | 50 | | 35 | 20 | | 36 | 6 | | 37 | 29 | | 38 | 35 | | 39 | 8 | | 40 | 39 | | 41 | 30 | | 42 | 7 | | 43 | 13 | | 44 | 57 | | 45 | 28 | | 46 | 50 | | 47 | 5 | | 48 | 31 | | 49 | 18 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 92 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 171 | | matches | (empty) | |
| 58.20% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 135 | | ratio | 0.03 | | matches | | 0 | "The cadence gave him away—that particular lilt where Welsh valleys met London streets." | | 1 | "The Raven's Nest chose that moment to assert itself—a drunk at the corner table knocked over his chair, setting off a chain reaction of Welsh profanity that would've made their old professors blush." | | 2 | "Marcus ordered a whisky—Macallan 25, because of course he did—and they sat in the kind of silence that only comes from knowing someone too well to pretend otherwise." | | 3 | "Chen's spring rolls; him looking like he'd stepped out of a corporate headshot." |
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| 92.25% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 962 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 47 | | adverbRatio | 0.04885654885654886 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.014553014553014554 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 11.36 | | std | 8.06 | | cv | 0.71 | | sampleLengths | | 0 | 7 | | 1 | 13 | | 2 | 23 | | 3 | 11 | | 4 | 13 | | 5 | 14 | | 6 | 30 | | 7 | 14 | | 8 | 22 | | 9 | 7 | | 10 | 8 | | 11 | 9 | | 12 | 33 | | 13 | 15 | | 14 | 21 | | 15 | 11 | | 16 | 3 | | 17 | 1 | | 18 | 10 | | 19 | 4 | | 20 | 2 | | 21 | 15 | | 22 | 8 | | 23 | 2 | | 24 | 2 | | 25 | 28 | | 26 | 24 | | 27 | 13 | | 28 | 13 | | 29 | 2 | | 30 | 7 | | 31 | 10 | | 32 | 4 | | 33 | 2 | | 34 | 3 | | 35 | 4 | | 36 | 2 | | 37 | 9 | | 38 | 6 | | 39 | 22 | | 40 | 7 | | 41 | 6 | | 42 | 3 | | 43 | 6 | | 44 | 19 | | 45 | 11 | | 46 | 5 | | 47 | 5 | | 48 | 10 | | 49 | 13 |
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| 81.73% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5259259259259259 | | totalSentences | 135 | | uniqueOpeners | 71 | |
| 80.32% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 83 | | matches | | 0 | "Still crooked, still pulling harder" | | 1 | "Then the professional mask slammed" |
| | ratio | 0.024 | |
| 99.52% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 83 | | matches | | 0 | "She turned slowly, already knowing" | | 1 | "His hair, once an unruly" | | 2 | "His grin hadn't changed, though" | | 3 | "His smile faltered." | | 4 | "He gestured at the empty" | | 5 | "She gave an imperceptible shake" | | 6 | "He swirled the whisky, watching" | | 7 | "He tapped his temple" | | 8 | "She'd thought it hadn't mattered" | | 9 | "She signaled Silas for another" | | 10 | "He had the grace to" | | 11 | "She thought of Yu-Fei in" | | 12 | "He turned to face her" | | 13 | "She drained half her pint" | | 14 | "She gestured around the bar" | | 15 | "He laughed, but there was" | | 16 | "His voice had gone cold," | | 17 | "She stood, pound coins clattering" | | 18 | "She laughed, and several heads" | | 19 | "They stood facing each other," |
| | ratio | 0.301 | |
| 62.41% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 66 | | totalSentences | 83 | | matches | | 0 | "The voice cut through the" | | 1 | "Rory's hand froze halfway to" | | 2 | "She turned slowly, already knowing" | | 3 | "The cadence gave him away—that" | | 4 | "Marcus stood three feet away," | | 5 | "The last time she'd seen" | | 6 | "His hair, once an unruly" | | 7 | "His grin hadn't changed, though" | | 8 | "The Raven's Nest chose that" | | 9 | "The green neon from the" | | 10 | "The words came out harder" | | 11 | "His smile faltered." | | 12 | "He gestured at the empty" | | 13 | "Silas caught her eye from" | | 14 | "She gave an imperceptible shake" | | 15 | "Marcus ordered a whisky—Macallan 25," | | 16 | "The mirror behind the bar" | | 17 | "Chen's spring rolls; him looking" | | 18 | "He swirled the whisky, watching" | | 19 | "The word came out flat," |
| | ratio | 0.795 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 83 | | matches | | 0 | "Now he filled out a" | | 1 | "Because explaining would mean admitting" |
| | ratio | 0.024 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 3 | | matches | | 0 | "His hair, once an unruly mess that defied gravity and good sense, lay slicked back with precision that spoke of hundred-pound haircuts." | | 1 | "The Raven's Nest chose that moment to assert itself—a drunk at the corner table knocked over his chair, setting off a chain reaction of Welsh profanity that wou…" | | 2 | "Golden Empress's main competitor, the corporate machine that had been slowly strangling every family-run Chinese restaurant in London." |
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| 67.31% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 26 | | uselessAdditionCount | 3 | | matches | | 0 | "The word came out, final" | | 1 | "The name came out, bitter" | | 2 | "She laughed, and several heads turned" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "He laughed (laugh)" | | 1 | "She laughed (laugh)" |
| | dialogueSentences | 70 | | tagDensity | 0.043 | | leniency | 0.086 | | rawRatio | 0.667 | | effectiveRatio | 0.057 | |