| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 25 | | adverbTagCount | 2 | | adverbTags | | 0 | "Marcus gestured vaguely [vaguely]" | | 1 | "Silas said slowly [slowly]" |
| | dialogueSentences | 58 | | tagDensity | 0.431 | | leniency | 0.862 | | rawRatio | 0.08 | | effectiveRatio | 0.069 | |
| 94.42% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1793 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 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) | |
| 69.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1793 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "footsteps" | | 1 | "weight" | | 2 | "aftermath" | | 3 | "throb" | | 4 | "familiar" | | 5 | "silence" | | 6 | "comfortable" | | 7 | "traced" | | 8 | "reminder" |
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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 | 114 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 114 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 145 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1785 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 62 | | wordCount | 1155 | | uniqueNames | 11 | | maxNameDensity | 2.16 | | worstName | "Silas" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Marcus" | | discoveredNames | | Soho | 2 | | Silas | 25 | | Vienna | 1 | | Marcus | 25 | | Bellamy | 1 | | Minsk | 2 | | Tangier | 2 | | Laphroaig | 1 | | Silence | 1 | | Prague | 1 | | Sharp | 1 |
| | persons | | 0 | "Silas" | | 1 | "Marcus" | | 2 | "Bellamy" | | 3 | "Silence" |
| | places | | 0 | "Soho" | | 1 | "Vienna" | | 2 | "Minsk" | | 3 | "Tangier" | | 4 | "Prague" |
| | globalScore | 0.418 | | windowScore | 0.167 | |
| 39.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | glossingSentenceCount | 3 | | matches | | 0 | "felt like ceremony" | | 1 | "quite articulate" | | 2 | "looked like the man Silas had known" |
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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 | 1785 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 145 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 60 | | mean | 29.75 | | std | 23.28 | | cv | 0.783 | | sampleLengths | | 0 | 68 | | 1 | 9 | | 2 | 66 | | 3 | 3 | | 4 | 81 | | 5 | 37 | | 6 | 20 | | 7 | 51 | | 8 | 11 | | 9 | 59 | | 10 | 43 | | 11 | 2 | | 12 | 39 | | 13 | 3 | | 14 | 48 | | 15 | 35 | | 16 | 25 | | 17 | 39 | | 18 | 3 | | 19 | 67 | | 20 | 4 | | 21 | 56 | | 22 | 39 | | 23 | 72 | | 24 | 41 | | 25 | 36 | | 26 | 5 | | 27 | 3 | | 28 | 18 | | 29 | 2 | | 30 | 30 | | 31 | 78 | | 32 | 14 | | 33 | 8 | | 34 | 19 | | 35 | 48 | | 36 | 89 | | 37 | 45 | | 38 | 42 | | 39 | 3 | | 40 | 39 | | 41 | 11 | | 42 | 41 | | 43 | 7 | | 44 | 3 | | 45 | 41 | | 46 | 2 | | 47 | 32 | | 48 | 24 | | 49 | 34 |
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| 96.03% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 114 | | matches | | 0 | "were tired" | | 1 | "been destroyed" | | 2 | "been remade" |
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| 29.93% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 196 | | matches | | 0 | "was drying" | | 1 | "was lying" | | 2 | "was carrying" | | 3 | "were marking" | | 4 | "was still deciding" |
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| 24.63% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 145 | | ratio | 0.041 | | matches | | 0 | "Silas was drying a pint glass when the door opened and let in a slice of Soho noise—taxi horns, laughter, the wet hiss of tyres on asphalt." | | 1 | "Not the most careful—that had never been Marcus's style—but the most instinctive." | | 2 | "His hands were different—rougher, the knuckles swollen." | | 3 | "His eyes were the same—sharp, grey, capable of reading a room in seconds—but they were tired in a way that went deeper than lack of sleep." | | 4 | "The bar was a front, yes—but it was also a museum of everything he'd tried to leave behind." | | 5 | "His gait was different, too—heavier, less fluid." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1170 | | adjectiveStacks | 1 | | stackExamples | | 0 | "dim, map-covered room." |
| | adverbCount | 41 | | adverbRatio | 0.03504273504273504 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005982905982905983 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 145 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 145 | | mean | 12.31 | | std | 10.95 | | cv | 0.89 | | sampleLengths | | 0 | 27 | | 1 | 6 | | 2 | 19 | | 3 | 3 | | 4 | 2 | | 5 | 11 | | 6 | 9 | | 7 | 5 | | 8 | 14 | | 9 | 37 | | 10 | 2 | | 11 | 8 | | 12 | 3 | | 13 | 8 | | 14 | 12 | | 15 | 24 | | 16 | 19 | | 17 | 18 | | 18 | 13 | | 19 | 24 | | 20 | 17 | | 21 | 3 | | 22 | 10 | | 23 | 16 | | 24 | 7 | | 25 | 2 | | 26 | 16 | | 27 | 7 | | 28 | 4 | | 29 | 30 | | 30 | 4 | | 31 | 25 | | 32 | 11 | | 33 | 16 | | 34 | 16 | | 35 | 2 | | 36 | 29 | | 37 | 10 | | 38 | 3 | | 39 | 11 | | 40 | 11 | | 41 | 13 | | 42 | 13 | | 43 | 5 | | 44 | 8 | | 45 | 16 | | 46 | 6 | | 47 | 2 | | 48 | 18 | | 49 | 5 |
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| 48.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.3310344827586207 | | totalSentences | 145 | | uniqueOpeners | 48 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 87 | | matches | | 0 | "Then the man said," | | 1 | "Then he poured one for" | | 2 | "Then he went back to" |
| | ratio | 0.034 | |
| 82.07% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 87 | | matches | | 0 | "He didn't look up at" | | 1 | "He took his time with" | | 2 | "He'd saved Silas's life twice," | | 3 | "He pulled out a stool" | | 4 | "He stared at the amber" | | 5 | "His hands were different—rougher, the" | | 6 | "It wasn't a question." | | 7 | "He closed his eyes." | | 8 | "He'd stopped noticing it most" | | 9 | "His finger traced the rim" | | 10 | "He'd learned patience the hard" | | 11 | "You couldn't rush a confession." | | 12 | "He took a longer drink" | | 13 | "He rarely drank on duty," | | 14 | "His eyes were the same—sharp," | | 15 | "He trailed off, searching for" | | 16 | "He thought about the silver" | | 17 | "It had belonged to his" | | 18 | "He'd got out, yes." | | 19 | "He'd changed his habits, his" |
| | ratio | 0.345 | |
| 40.46% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 73 | | totalSentences | 87 | | matches | | 0 | "Silas was drying a pint" | | 1 | "He didn't look up at" | | 2 | "The motion was habit, a" | | 3 | "A man, alone." | | 4 | "The kind that meant city" | | 5 | "Silas set the glass down." | | 6 | "He took his time with" | | 7 | "Time had a way of" | | 8 | "Marcus Bellamy had been the" | | 9 | "The one who could walk" | | 10 | "He'd saved Silas's life twice," | | 11 | "Marcus said, but there was" | | 12 | "He pulled out a stool" | | 13 | "Silas poured two fingers of" | | 14 | "Marcus wrapped his hand around" | | 15 | "He stared at the amber" | | 16 | "His hands were different—rougher, the" | | 17 | "It wasn't a question." | | 18 | "Marcus finally raised the glass" | | 19 | "He closed his eyes." |
| | ratio | 0.839 | |
| 57.47% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 87 | | matches | | 0 | "Because Marcus wasn't wrong, and" |
| | ratio | 0.011 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 4 | | matches | | 0 | "The kind that meant city shoes with leather soles, not trainers." | | 1 | "The old knee ached, a dull throb that had become as familiar as his own heartbeat." | | 2 | "His eyes were the same—sharp, grey, capable of reading a room in seconds—but they were tired in a way that went deeper than lack of sleep." | | 3 | "The memories lived in the photographs on his walls, the maps that didn't mark his routes but marked his losses." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 25 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 58 | | tagDensity | 0.224 | | leniency | 0.448 | | rawRatio | 0 | | effectiveRatio | 0 | |