| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 43 | | tagDensity | 0.442 | | leniency | 0.884 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1139 | | 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) | |
| 29.76% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1139 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "echoed" | | 1 | "etched" | | 2 | "intricate" | | 3 | "chaotic" | | 4 | "footsteps" | | 5 | "scanned" | | 6 | "standard" | | 7 | "silence" | | 8 | "vibrated" | | 9 | "dance" | | 10 | "flickered" | | 11 | "raced" | | 12 | "shattered" |
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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 | 91 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 91 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1139 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 14.50% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 738 | | uniqueNames | 9 | | maxNameDensity | 2.71 | | worstName | "Harlow" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Aris | 10 | | Harlow | 20 | | London | 1 | | Shade | 1 | | Morris | 1 | | Kowalski | 1 | | Eva | 9 | | Glock | 1 |
| | persons | | 0 | "Aris" | | 1 | "Harlow" | | 2 | "Morris" | | 3 | "Kowalski" | | 4 | "Eva" |
| | places | | | globalScore | 0.145 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | 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 | 1139 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 44 | | mean | 25.89 | | std | 16.78 | | cv | 0.648 | | sampleLengths | | 0 | 38 | | 1 | 48 | | 2 | 20 | | 3 | 6 | | 4 | 9 | | 5 | 54 | | 6 | 24 | | 7 | 50 | | 8 | 26 | | 9 | 31 | | 10 | 8 | | 11 | 52 | | 12 | 23 | | 13 | 7 | | 14 | 14 | | 15 | 52 | | 16 | 6 | | 17 | 36 | | 18 | 24 | | 19 | 19 | | 20 | 60 | | 21 | 7 | | 22 | 27 | | 23 | 10 | | 24 | 21 | | 25 | 35 | | 26 | 17 | | 27 | 10 | | 28 | 42 | | 29 | 25 | | 30 | 10 | | 31 | 51 | | 32 | 46 | | 33 | 6 | | 34 | 14 | | 35 | 28 | | 36 | 15 | | 37 | 16 | | 38 | 44 | | 39 | 23 | | 40 | 8 | | 41 | 14 | | 42 | 59 | | 43 | 4 |
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| 85.98% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 91 | | matches | | 0 | "were tinted" | | 1 | "were locked" | | 2 | "were etched" | | 3 | "was broken" | | 4 | "were elongated" |
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| 87.96% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 119 | | matches | | 0 | "was not pointing" | | 1 | "was vibrating" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 115 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 739 | | adjectiveStacks | 1 | | stackExamples | | 0 | "fine, small, old, round," |
| | adverbCount | 9 | | adverbRatio | 0.012178619756427604 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0040595399188092015 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 9.9 | | std | 5.3 | | cv | 0.536 | | sampleLengths | | 0 | 8 | | 1 | 14 | | 2 | 5 | | 3 | 11 | | 4 | 14 | | 5 | 14 | | 6 | 8 | | 7 | 12 | | 8 | 3 | | 9 | 7 | | 10 | 5 | | 11 | 5 | | 12 | 6 | | 13 | 9 | | 14 | 14 | | 15 | 4 | | 16 | 15 | | 17 | 8 | | 18 | 13 | | 19 | 6 | | 20 | 4 | | 21 | 14 | | 22 | 8 | | 23 | 14 | | 24 | 6 | | 25 | 4 | | 26 | 9 | | 27 | 9 | | 28 | 12 | | 29 | 14 | | 30 | 12 | | 31 | 19 | | 32 | 8 | | 33 | 15 | | 34 | 7 | | 35 | 10 | | 36 | 20 | | 37 | 4 | | 38 | 19 | | 39 | 7 | | 40 | 9 | | 41 | 5 | | 42 | 8 | | 43 | 8 | | 44 | 20 | | 45 | 16 | | 46 | 6 | | 47 | 17 | | 48 | 10 | | 49 | 9 |
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| 54.20% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.3565217391304348 | | totalSentences | 115 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 24 | | totalSentences | 83 | | matches | | 0 | "She knelt beside the corpse." | | 1 | "He stood near the rusted" | | 2 | "She leaned closer to the" | | 3 | "She moved her attention to" | | 4 | "She pried the stiff digits" | | 5 | "Her sharp jaw tightened." | | 6 | "She had seen one of" | | 7 | "She brushed the dust from" | | 8 | "Her military precision demanded order," | | 9 | "Her worn leather satchel bumped" | | 10 | "She pushed her round glasses" | | 11 | "Her green eyes scanned the" | | 12 | "She examined the victim's lapel." | | 13 | "She pointed the tweezers at" | | 14 | "She reached into her bag" | | 15 | "She looked up at Harlow." | | 16 | "She stood up and brushed" | | 17 | "She was five foot nine," | | 18 | "He took a step back." | | 19 | "It was vibrating." |
| | ratio | 0.289 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 81 | | totalSentences | 83 | | matches | | 0 | "Harlow snapped her second latex" | | 1 | "The sharp crack echoed off" | | 2 | "She knelt beside the corpse." | | 3 | "The torn fabric of the" | | 4 | "DS Aris said" | | 5 | "He stood near the rusted" | | 6 | "The pale beam cut through" | | 7 | "Harlow ignored him." | | 8 | "She leaned closer to the" | | 9 | "The lips were tinted blue." | | 10 | "Frost clung to the eyelashes." | | 11 | "Harlow grabbed the victim's chin" | | 12 | "The neck was stiff." | | 13 | "Frostbite did not happen in" | | 14 | "She moved her attention to" | | 15 | "The fingers were locked in" | | 16 | "She pried the stiff digits" | | 17 | "The metal was freezing." | | 18 | "A fine, small, old, round," | | 19 | "The casing held a thick" |
| | ratio | 0.976 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 83 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 46.05% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 3 | | matches | | 0 | "Aris walked, his boots crunching on broken glass" | | 1 | "Aris said, his voice flat" | | 2 | "Aris said, though his hand drifted to his own baton" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 2 | | fancyTags | | 0 | "Harlow ordered (order)" | | 1 | "Eva whispered (whisper)" |
| | dialogueSentences | 43 | | tagDensity | 0.256 | | leniency | 0.512 | | rawRatio | 0.182 | | effectiveRatio | 0.093 | |