| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 39 | | tagDensity | 0.256 | | leniency | 0.513 | | rawRatio | 0.1 | | effectiveRatio | 0.051 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1298 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | 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) | |
| 65.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1298 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "weight" | | 1 | "silence" | | 2 | "mechanical" | | 3 | "flickered" |
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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 | 96 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 124 | | 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 | 0 | | markdownWords | 0 | | totalWords | 1296 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 976 | | uniqueNames | 14 | | maxNameDensity | 0.51 | | worstName | "Silas" | | maxWindowNameDensity | 1 | | worstWindowName | "London" | | discoveredNames | | Carter | 1 | | London | 2 | | Evan | 1 | | Soho | 1 | | Prague | 3 | | Blackwood | 1 | | Golden | 1 | | Empress | 1 | | Rory | 1 | | Cardiff | 1 | | Berlin | 1 | | Silas | 5 | | Evans | 1 | | Silver | 1 |
| | persons | | 0 | "Carter" | | 1 | "Evan" | | 2 | "Blackwood" | | 3 | "Empress" | | 4 | "Rory" | | 5 | "Silas" | | 6 | "Evans" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Prague" | | 3 | "Golden" | | 4 | "Cardiff" | | 5 | "Berlin" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a weeping ghost" |
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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 | 1296 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 124 | | matches | | 0 | "pretending that she" | | 1 | "knew that story" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 26.45 | | std | 27.51 | | cv | 1.04 | | sampleLengths | | 0 | 126 | | 1 | 85 | | 2 | 3 | | 3 | 16 | | 4 | 112 | | 5 | 33 | | 6 | 46 | | 7 | 73 | | 8 | 10 | | 9 | 13 | | 10 | 62 | | 11 | 10 | | 12 | 19 | | 13 | 37 | | 14 | 9 | | 15 | 53 | | 16 | 2 | | 17 | 37 | | 18 | 11 | | 19 | 9 | | 20 | 42 | | 21 | 12 | | 22 | 17 | | 23 | 33 | | 24 | 6 | | 25 | 60 | | 26 | 6 | | 27 | 11 | | 28 | 13 | | 29 | 21 | | 30 | 3 | | 31 | 29 | | 32 | 2 | | 33 | 3 | | 34 | 24 | | 35 | 4 | | 36 | 14 | | 37 | 22 | | 38 | 19 | | 39 | 35 | | 40 | 3 | | 41 | 12 | | 42 | 23 | | 43 | 7 | | 44 | 34 | | 45 | 8 | | 46 | 11 | | 47 | 1 | | 48 | 55 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 96 | | matches | | |
| 11.68% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 177 | | matches | | 0 | "was looking" | | 1 | "was polishing" | | 2 | "was living" | | 3 | "was holding" | | 4 | "wasn't living" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 124 | | ratio | 0.008 | | matches | | 0 | "She didn't look at the maps lining the walls—the faded cartographies of Soho or Prague, pinned and forgotten—but she felt the weight of the past pressing against the corner of her vision." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 983 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small, crescent-shaped scar" |
| | adverbCount | 24 | | adverbRatio | 0.024415055951169887 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002034587995930824 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 124 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 124 | | mean | 10.45 | | std | 6.94 | | cv | 0.664 | | sampleLengths | | 0 | 20 | | 1 | 21 | | 2 | 17 | | 3 | 27 | | 4 | 15 | | 5 | 26 | | 6 | 24 | | 7 | 14 | | 8 | 32 | | 9 | 15 | | 10 | 3 | | 11 | 16 | | 12 | 12 | | 13 | 16 | | 14 | 10 | | 15 | 22 | | 16 | 17 | | 17 | 15 | | 18 | 20 | | 19 | 9 | | 20 | 3 | | 21 | 21 | | 22 | 9 | | 23 | 6 | | 24 | 23 | | 25 | 8 | | 26 | 10 | | 27 | 27 | | 28 | 5 | | 29 | 9 | | 30 | 14 | | 31 | 8 | | 32 | 5 | | 33 | 3 | | 34 | 2 | | 35 | 13 | | 36 | 21 | | 37 | 8 | | 38 | 11 | | 39 | 5 | | 40 | 17 | | 41 | 10 | | 42 | 7 | | 43 | 7 | | 44 | 5 | | 45 | 10 | | 46 | 11 | | 47 | 3 | | 48 | 2 | | 49 | 2 |
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| 33.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 20 | | diversityRatio | 0.25806451612903225 | | totalSentences | 124 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 88 | | matches | | 0 | "She was reluctant to enter," | | 1 | "She shook the water from" | | 2 | "She felt the small, crescent-shaped" | | 3 | "She was twenty-five, and she" | | 4 | "She didn't look at the" | | 5 | "She was looking for a" | | 6 | "He set the glass down" | | 7 | "He hadn't changed much since" | | 8 | "He was shorter in stature" | | 9 | "He had a beard, personalised" | | 10 | "He said it like it" | | 11 | "It had been six years," | | 12 | "She didn't trust her name" | | 13 | "She wasn't living it, she" | | 14 | "He sat down, his left" | | 15 | "He didn't offer a chair." | | 16 | "He leaned his elbows on" | | 17 | "They were the only two" | | 18 | "He watched her, and for" | | 19 | "They were the only two" |
| | ratio | 0.58 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 88 | | matches | | 0 | "The rain battered against the" | | 1 | "Aurora Carter pushed the heavy" | | 2 | "She was reluctant to enter," | | 3 | "She shook the water from" | | 4 | "She felt the small, crescent-shaped" | | 5 | "She was twenty-five, and she" | | 6 | "She didn't look at the" | | 7 | "She was looking for a" | | 8 | "The voice came from the" | | 9 | "Silas Blackwood didn't look up" | | 10 | "The movement of his wrist" | | 11 | "He set the glass down" | | 12 | "Hazel eyes, hard as polished" | | 13 | "He hadn't changed much since" | | 14 | "He was shorter in stature" | | 15 | "He had a beard, personalised" | | 16 | "He said it like it" | | 17 | "A life sentence." | | 18 | "It had been six years," | | 19 | "The tone of her voice" |
| | ratio | 0.92 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 90.59% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 41 | | technicalSentenceCount | 3 | | matches | | 0 | "The movement of his wrist was too rigid, a mechanical adjustment that made everyone look twice." | | 1 | "The limp had always been there, a permanent mark on a man who had served his country in Prague, a knee that would never hold him straight." | | 2 | "She was blue-eyed and short, he was a man who had lost a wife." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 2 | | matches | | 0 | "She reached out, her hand stopping inches from his" | | 1 | "She reached out, her hand stopping inches from his" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 39 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0.333 | | effectiveRatio | 0.051 | |