| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 874 | | 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) | |
| 54.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 874 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "footsteps" | | 1 | "echoing" | | 2 | "glistening" | | 3 | "silence" | | 4 | "velvet" | | 5 | "scanned" | | 6 | "gloom" |
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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 | 77 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 84 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 29 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 871 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 15 | | wordCount | 820 | | uniqueNames | 5 | | maxNameDensity | 1.1 | | worstName | "Harlow" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Harlow" | | discoveredNames | | Quinn | 3 | | Soho | 1 | | Veil | 1 | | Market | 1 | | Harlow | 9 |
| | persons | | | places | | | globalScore | 0.951 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 60 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 85.19% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.148 | | wordCount | 871 | | matches | | 0 | "Not just terrified, but desperate" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 28.1 | | std | 20.93 | | cv | 0.745 | | sampleLengths | | 0 | 64 | | 1 | 58 | | 2 | 23 | | 3 | 17 | | 4 | 81 | | 5 | 1 | | 6 | 31 | | 7 | 6 | | 8 | 39 | | 9 | 17 | | 10 | 1 | | 11 | 15 | | 12 | 32 | | 13 | 51 | | 14 | 4 | | 15 | 38 | | 16 | 28 | | 17 | 57 | | 18 | 3 | | 19 | 49 | | 20 | 3 | | 21 | 11 | | 22 | 11 | | 23 | 48 | | 24 | 47 | | 25 | 19 | | 26 | 22 | | 27 | 17 | | 28 | 8 | | 29 | 24 | | 30 | 46 |
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| 96.15% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 77 | | matches | | 0 | "were covered" | | 1 | "were gone" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 155 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 84 | | ratio | 0.012 | | matches | | 0 | "It was no longer just rot; it was sweet." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 823 | | adjectiveStacks | 1 | | stackExamples | | 0 | "narrow, gas-lit alley." |
| | adverbCount | 18 | | adverbRatio | 0.02187120291616039 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.007290400972053463 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 84 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 84 | | mean | 10.37 | | std | 6.19 | | cv | 0.597 | | sampleLengths | | 0 | 14 | | 1 | 16 | | 2 | 17 | | 3 | 8 | | 4 | 9 | | 5 | 9 | | 6 | 16 | | 7 | 14 | | 8 | 7 | | 9 | 12 | | 10 | 6 | | 11 | 15 | | 12 | 2 | | 13 | 9 | | 14 | 8 | | 15 | 3 | | 16 | 5 | | 17 | 11 | | 18 | 10 | | 19 | 13 | | 20 | 10 | | 21 | 13 | | 22 | 16 | | 23 | 1 | | 24 | 10 | | 25 | 8 | | 26 | 13 | | 27 | 6 | | 28 | 15 | | 29 | 24 | | 30 | 7 | | 31 | 10 | | 32 | 1 | | 33 | 7 | | 34 | 3 | | 35 | 5 | | 36 | 9 | | 37 | 9 | | 38 | 14 | | 39 | 21 | | 40 | 5 | | 41 | 12 | | 42 | 13 | | 43 | 4 | | 44 | 7 | | 45 | 10 | | 46 | 21 | | 47 | 13 | | 48 | 11 | | 49 | 4 |
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| 55.16% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.34523809523809523 | | totalSentences | 84 | | uniqueOpeners | 29 | |
| 43.86% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 76 | | matches | | 0 | "Then, the sky above opened" |
| | ratio | 0.013 | |
| 62.11% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 76 | | matches | | 0 | "Her shoulder collided with the" | | 1 | "He stumbled, his heel catching" | | 2 | "She lunged, grabbing the collar" | | 3 | "He twisted violently, his hand" | | 4 | "It wasn't supposed to end" | | 5 | "she snarled, jerking him backward" | | 6 | "Her grip on his jacket" | | 7 | "He gasped for air, his" | | 8 | "She didn't listen." | | 9 | "Her training overrode his begging." | | 10 | "She spun him around, shoving" | | 11 | "He groaned, sliding down to" | | 12 | "She pulled out her cuffs," | | 13 | "She secured the cuffs and" | | 14 | "They needed to get him" | | 15 | "It came from deeper in" | | 16 | "He was the key to" | | 17 | "She reached the metal door." | | 18 | "It was heavy, locked on" | | 19 | "She kicked it open." |
| | ratio | 0.395 | |
| 12.63% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 76 | | matches | | 0 | "Footsteps slapped against the wet" | | 1 | "Harlow Quinn vaulted the low" | | 2 | "Her shoulder collided with the" | | 3 | "He stumbled, his heel catching" | | 4 | "She lunged, grabbing the collar" | | 5 | "He twisted violently, his hand" | | 6 | "Quinn intercepted the fist with" | | 7 | "Rain slicked his face, turning" | | 8 | "It wasn't supposed to end" | | 9 | "she snarled, jerking him backward" | | 10 | "Her grip on his jacket" | | 11 | "He gasped for air, his" | | 12 | "She didn't listen." | | 13 | "Her training overrode his begging." | | 14 | "She spun him around, shoving" | | 15 | "He groaned, sliding down to" | | 16 | "She pulled out her cuffs," | | 17 | "Quinn caught him by the" | | 18 | "The resistance lasted only a" | | 19 | "She secured the cuffs and" |
| | ratio | 0.895 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 76 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 1 | | matches | | 0 | "It came from deeper in the alley, behind a rusted metal door that led down to the basement level of the old commercial building." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "she shouted, her badge flashing in the gloom" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she snarled (snarl)" | | 1 | "she shouted (shout)" |
| | dialogueSentences | 12 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0.667 | | effectiveRatio | 0.333 | |