| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1234 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 55.43% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1234 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "loomed" | | 1 | "footsteps" | | 2 | "silence" | | 3 | "pulse" | | 4 | "weight" | | 5 | "flickered" | | 6 | "etched" | | 7 | "resolved" | | 8 | "measured" | | 9 | "quickened" |
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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 | 123 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 123 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 126 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1234 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 1205 | | uniqueNames | 11 | | maxNameDensity | 0.58 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Delancey | 1 | | Street | 2 | | Morris | 2 | | London | 2 | | High | 1 | | Camden | 3 | | Yard | 1 | | Veil | 1 | | Market | 1 | | South | 1 | | Quinn | 7 |
| | persons | | 0 | "Morris" | | 1 | "Market" | | 2 | "Quinn" |
| | places | | 0 | "Delancey" | | 1 | "Street" | | 2 | "London" | | 3 | "High" | | 4 | "Camden" | | 5 | "South" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 86 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1234 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 126 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 47.46 | | std | 24.53 | | cv | 0.517 | | sampleLengths | | 0 | 55 | | 1 | 66 | | 2 | 48 | | 3 | 60 | | 4 | 55 | | 5 | 54 | | 6 | 8 | | 7 | 57 | | 8 | 52 | | 9 | 64 | | 10 | 62 | | 11 | 69 | | 12 | 101 | | 13 | 34 | | 14 | 61 | | 15 | 4 | | 16 | 69 | | 17 | 75 | | 18 | 56 | | 19 | 20 | | 20 | 10 | | 21 | 60 | | 22 | 54 | | 23 | 16 | | 24 | 19 | | 25 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 123 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 202 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 126 | | ratio | 0.008 | | matches | | 0 | "The token in her pocket bought entry; it bought nothing about walking out again." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1211 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.03633360858794385 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004128819157720892 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 126 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 126 | | mean | 9.79 | | std | 6.78 | | cv | 0.693 | | sampleLengths | | 0 | 18 | | 1 | 10 | | 2 | 27 | | 3 | 9 | | 4 | 10 | | 5 | 16 | | 6 | 9 | | 7 | 22 | | 8 | 4 | | 9 | 8 | | 10 | 2 | | 11 | 5 | | 12 | 23 | | 13 | 6 | | 14 | 15 | | 15 | 10 | | 16 | 16 | | 17 | 13 | | 18 | 3 | | 19 | 2 | | 20 | 1 | | 21 | 8 | | 22 | 4 | | 23 | 15 | | 24 | 12 | | 25 | 9 | | 26 | 7 | | 27 | 12 | | 28 | 16 | | 29 | 13 | | 30 | 7 | | 31 | 6 | | 32 | 8 | | 33 | 2 | | 34 | 4 | | 35 | 27 | | 36 | 4 | | 37 | 7 | | 38 | 13 | | 39 | 26 | | 40 | 6 | | 41 | 6 | | 42 | 14 | | 43 | 5 | | 44 | 11 | | 45 | 9 | | 46 | 1 | | 47 | 5 | | 48 | 17 | | 49 | 9 |
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| 79.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.48412698412698413 | | totalSentences | 126 | | uniqueOpeners | 61 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 8 | | totalSentences | 106 | | matches | | 0 | "Too narrow for cars." | | 1 | "Only the rain drumming metal" | | 2 | "Then she saw it on" | | 3 | "Further along, where the tunnels" | | 4 | "Then the shapes resolved under" | | 5 | "Potentially hostile every shadow." | | 6 | "Even the lamps dimmed." | | 7 | "Too tall, too silent." |
| | ratio | 0.075 | |
| 91.70% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 106 | | matches | | 0 | "She closed the gap by" | | 1 | "It flattened her salt-and-pepper crop" | | 2 | "He glanced back once." | | 3 | "She would teach this one" | | 4 | "He took the side road" | | 5 | "She knew the bottleneck well" | | 6 | "Her palm still burned from" | | 7 | "She wiped the blood on" | | 8 | "He ducked left again, through" | | 9 | "She cursed under her breath" | | 10 | "His footsteps splashed ahead, then" | | 11 | "She drew her torch." | | 12 | "She thumbed the switch off" | | 13 | "She approached the door the" | | 14 | "It swung with a soft" | | 15 | "She should call it in." | | 16 | "They were wrong about so" | | 17 | "Her hand hovered near the" | | 18 | "He had dropped his entry" | | 19 | "She started down." |
| | ratio | 0.321 | |
| 63.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 84 | | totalSentences | 106 | | matches | | 0 | "The man vaulted the iron" | | 1 | "Quinn's boots pounded after him," | | 2 | "She closed the gap by" | | 3 | "A late bus hissed its" | | 4 | "The driver cursed as the" | | 5 | "Quinn followed without breaking stride," | | 6 | "The rain needled her face," | | 7 | "It flattened her salt-and-pepper crop" | | 8 | "He glanced back once." | | 9 | "That made her push harder." | | 10 | "She would teach this one" | | 11 | "Camden High Street loomed, neon" | | 12 | "Music thumped from a basement" | | 13 | "The suspect veered right, past" | | 14 | "Quinn's worn leather watch caught" | | 15 | "Static spat back." | | 16 | "He took the side road" | | 17 | "She knew the bottleneck well" | | 18 | "Her palm still burned from" | | 19 | "She wiped the blood on" |
| | ratio | 0.792 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 106 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 48 | | technicalSentenceCount | 8 | | matches | | 0 | "Quinn followed without breaking stride, shoulder glancing a parked taxi that blared its horn too late." | | 1 | "She knew the bottleneck well enough from other nights that ended with knives or worse." | | 2 | "The stairwell opened onto a wide platform that belonged to some forgotten line, tiles cracked and black with decades of soot." | | 3 | "She held no warrant that stretched this deep, no authority these buyers would recognise." | | 4 | "He ignored it and angled for a gap between two overloaded tables of tools that hummed under their own power." | | 5 | "She recovered quick, shoulder knocking a crate that clattered open to spill small dark bones across the floor." | | 6 | "In his hand he held a thin blade, the edge dark and irregular as if forged from frozen oil." | | 7 | "He smiled with too many teeth and dropped something that was not the knife: another of those bone tokens, this one larger, etched with a different spiral." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |