| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1406 | | 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) | |
| 53.77% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1406 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "measured" | | 1 | "gloom" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "intricate" | | 5 | "etched" | | 6 | "shimmered" | | 7 | "tangible" | | 8 | "whisper" | | 9 | "tracing" | | 10 | "reminder" | | 11 | "chaotic" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 108 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 108 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 138 | | 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 | 1406 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 52 | | wordCount | 1134 | | uniqueNames | 14 | | maxNameDensity | 1.94 | | worstName | "Harlow" | | maxWindowNameDensity | 4 | | worstWindowName | "Tomas" | | discoveredNames | | Quinn | 1 | | Herrera | 1 | | Morris | 5 | | Camden | 1 | | Harlow | 22 | | Veil | 2 | | Market | 2 | | Glock | 1 | | Saint | 1 | | Christopher | 1 | | Tomas | 12 | | Token | 1 | | Required | 1 | | Exceptions | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Morris" | | 3 | "Harlow" | | 4 | "Market" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Tomas" |
| | places | (empty) | | globalScore | 0.53 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like grinding stones" |
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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 | 1406 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 138 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 76 | | mean | 18.5 | | std | 16.85 | | cv | 0.911 | | sampleLengths | | 0 | 54 | | 1 | 40 | | 2 | 3 | | 3 | 6 | | 4 | 40 | | 5 | 8 | | 6 | 22 | | 7 | 10 | | 8 | 7 | | 9 | 6 | | 10 | 8 | | 11 | 44 | | 12 | 1 | | 13 | 7 | | 14 | 9 | | 15 | 34 | | 16 | 43 | | 17 | 44 | | 18 | 45 | | 19 | 10 | | 20 | 21 | | 21 | 1 | | 22 | 8 | | 23 | 22 | | 24 | 28 | | 25 | 4 | | 26 | 10 | | 27 | 10 | | 28 | 22 | | 29 | 13 | | 30 | 98 | | 31 | 19 | | 32 | 32 | | 33 | 1 | | 34 | 7 | | 35 | 23 | | 36 | 1 | | 37 | 15 | | 38 | 5 | | 39 | 29 | | 40 | 7 | | 41 | 8 | | 42 | 5 | | 43 | 6 | | 44 | 12 | | 45 | 9 | | 46 | 9 | | 47 | 8 | | 48 | 7 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 108 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 191 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 138 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1137 | | adjectiveStacks | 1 | | stackExamples | | 0 | "illuminating cracked white tiles" |
| | adverbCount | 8 | | adverbRatio | 0.007036059806508356 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003518029903254178 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 138 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 138 | | mean | 10.19 | | std | 5.91 | | cv | 0.58 | | sampleLengths | | 0 | 5 | | 1 | 18 | | 2 | 15 | | 3 | 16 | | 4 | 7 | | 5 | 11 | | 6 | 5 | | 7 | 17 | | 8 | 3 | | 9 | 6 | | 10 | 4 | | 11 | 21 | | 12 | 15 | | 13 | 8 | | 14 | 22 | | 15 | 10 | | 16 | 7 | | 17 | 6 | | 18 | 8 | | 19 | 8 | | 20 | 11 | | 21 | 14 | | 22 | 11 | | 23 | 1 | | 24 | 7 | | 25 | 9 | | 26 | 6 | | 27 | 8 | | 28 | 20 | | 29 | 7 | | 30 | 7 | | 31 | 16 | | 32 | 13 | | 33 | 3 | | 34 | 18 | | 35 | 5 | | 36 | 18 | | 37 | 3 | | 38 | 11 | | 39 | 18 | | 40 | 13 | | 41 | 10 | | 42 | 21 | | 43 | 1 | | 44 | 8 | | 45 | 22 | | 46 | 2 | | 47 | 8 | | 48 | 5 | | 49 | 13 |
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| 39.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.2971014492753623 | | totalSentences | 138 | | uniqueOpeners | 41 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 105 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 105 | | matches | | 0 | "She glanced at the worn" | | 1 | "Her military precision kept her" | | 2 | "He vaulted a low brick" | | 3 | "She tapped the microphone clipped" | | 4 | "He crashed hard, his left" | | 5 | "He hissed in pain, clutching" | | 6 | "He scrambled up, clutching a" | | 7 | "His warm brown eyes caught" | | 8 | "He tightened his grip on" | | 9 | "She slipped through the narrow" | | 10 | "She tore the fabric free" | | 11 | "She clicked on her torch." | | 12 | "He pulled out a small," | | 13 | "He placed it in the" | | 14 | "He looked at Harlow, then" | | 15 | "He tapped a wooden sign" | | 16 | "He looked at the scar" | | 17 | "He turned and vanished into" | | 18 | "She thought of Morris's empty" | | 19 | "She thought of the three" |
| | ratio | 0.267 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 98 | | totalSentences | 105 | | matches | | 0 | "Rain lashed against the pavement." | | 1 | "Harlow Quinn drove her legs" | | 2 | "The suspect stayed two blocks" | | 3 | "Harlow's lungs burned with the" | | 4 | "She glanced at the worn" | | 5 | "Her military precision kept her" | | 6 | "Harlow's voice cut through the" | | 7 | "The suspect ignored her." | | 8 | "He vaulted a low brick" | | 9 | "Harlow cleared the obstacle without" | | 10 | "She tapped the microphone clipped" | | 11 | "Static hissed in her earpiece," | | 12 | "Harlow pushed harder, her thighs" | | 13 | "The suspect slipped on a" | | 14 | "He crashed hard, his left" | | 15 | "He hissed in pain, clutching" | | 16 | "He scrambled up, clutching a" | | 17 | "Harlow closed the gap to" | | 18 | "Tomas Herrera glanced over his" | | 19 | "His warm brown eyes caught" |
| | ratio | 0.933 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 105 | | matches | (empty) | | ratio | 0 | |
| 94.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 4 | | matches | | 0 | "He vaulted a low brick wall, his trainers slipping on the wet coping before he found his footing and kept running." | | 1 | "Harlow cleared the obstacle without breaking stride, her hands brushing the rough brick for balance." | | 2 | "Water dripped from the vaulted ceiling, splashing into deep puddles that reflected the sweeping beam of her torch." | | 3 | "Vendors in heavy cloaks haggled over glowing vials, rusted artifacts, and caged creatures that chittered in the dark." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |