| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 3 | | adverbTags | | 0 | "Quinn said softly [softly]" | | 1 | "Miller laughed nervously [nervously]" | | 2 | "Quinn turned back [back]" |
| | dialogueSentences | 36 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.188 | | effectiveRatio | 0.167 | |
| 75.49% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1224 | | totalAiIsmAdverbs | 6 | | found | | | highlights | | 0 | "sharply" | | 1 | "slightly" | | 2 | "lazily" | | 3 | "softly" | | 4 | "nervously" | | 5 | "quickly" |
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| 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) | |
| 30.56% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1224 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "flickered" | | 1 | "pulse" | | 2 | "gloom" | | 3 | "chill" | | 4 | "standard" | | 5 | "echoing" | | 6 | "etched" | | 7 | "intricate" | | 8 | "magnetic" | | 9 | "weight" | | 10 | "silence" | | 11 | "footsteps" | | 12 | "could feel" | | 13 | "warmth" |
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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 | 1 | | narrationSentences | 93 | | matches | | |
| 96.77% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 113 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1222 | | ratio | 0.002 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 54.56% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 943 | | uniqueNames | 11 | | maxNameDensity | 1.91 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 18 | | Tube | 1 | | Camden | 1 | | Miller | 12 | | Southwark | 1 | | Morris | 2 | | Veil | 1 | | Market | 1 | | Aurora | 1 | | Eva | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Morris" | | 4 | "Eva" |
| | places | | | globalScore | 0.546 | | windowScore | 0.667 | |
| 69.35% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 2 | | matches | | 0 | "sigils that seemed to shift when she looked at them from the corner of her eye" | | 1 | "seemed deeper than the rest, pooling like oil on the floor" |
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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 | 1222 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 113 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 33.94 | | std | 22.15 | | cv | 0.652 | | sampleLengths | | 0 | 86 | | 1 | 44 | | 2 | 34 | | 3 | 77 | | 4 | 25 | | 5 | 16 | | 6 | 49 | | 7 | 20 | | 8 | 67 | | 9 | 5 | | 10 | 42 | | 11 | 17 | | 12 | 11 | | 13 | 83 | | 14 | 8 | | 15 | 15 | | 16 | 53 | | 17 | 17 | | 18 | 21 | | 19 | 44 | | 20 | 30 | | 21 | 1 | | 22 | 52 | | 23 | 18 | | 24 | 54 | | 25 | 3 | | 26 | 27 | | 27 | 29 | | 28 | 12 | | 29 | 44 | | 30 | 27 | | 31 | 21 | | 32 | 56 | | 33 | 31 | | 34 | 57 | | 35 | 26 |
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| 93.94% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 93 | | matches | | 0 | "was supposed" | | 1 | "was covered" | | 2 | "was etched" |
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| 39.36% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 166 | | matches | | 0 | "wasn't pointing" | | 1 | "was spinning" | | 2 | "were running" | | 3 | "was pointing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 113 | | ratio | 0.009 | | matches | | 0 | "She suspected the clique—Aurora and that researcher friend of hers, Eva—were running the show." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 948 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.03164556962025317 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.011603375527426161 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 113 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 113 | | mean | 10.81 | | std | 6.48 | | cv | 0.599 | | sampleLengths | | 0 | 23 | | 1 | 25 | | 2 | 38 | | 3 | 16 | | 4 | 16 | | 5 | 12 | | 6 | 16 | | 7 | 18 | | 8 | 4 | | 9 | 10 | | 10 | 22 | | 11 | 9 | | 12 | 19 | | 13 | 13 | | 14 | 8 | | 15 | 11 | | 16 | 6 | | 17 | 4 | | 18 | 12 | | 19 | 5 | | 20 | 15 | | 21 | 3 | | 22 | 16 | | 23 | 10 | | 24 | 11 | | 25 | 9 | | 26 | 19 | | 27 | 4 | | 28 | 5 | | 29 | 26 | | 30 | 5 | | 31 | 8 | | 32 | 5 | | 33 | 4 | | 34 | 16 | | 35 | 4 | | 36 | 18 | | 37 | 7 | | 38 | 10 | | 39 | 7 | | 40 | 4 | | 41 | 4 | | 42 | 15 | | 43 | 10 | | 44 | 5 | | 45 | 6 | | 46 | 24 | | 47 | 5 | | 48 | 14 | | 49 | 8 |
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| 37.76% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3008849557522124 | | totalSentences | 113 | | uniqueOpeners | 34 | |
| 74.07% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 90 | | matches | | 0 | "Instead, the brass was etched" | | 1 | "Then she wrote one name:" |
| | ratio | 0.022 | |
| 20.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 45 | | totalSentences | 90 | | matches | | 0 | "She stood at the edge" | | 1 | "He looked uncomfortable, his shoulders" | | 2 | "He glanced back at Quinn," | | 3 | "She moved with military precision," | | 4 | "She was forty-one, but the" | | 5 | "She stopped three feet from" | | 6 | "He lay on his back," | | 7 | "She reached out, her gloved" | | 8 | "She pointed to a small," | | 9 | "It wasn't blood." | | 10 | "It was a greyish residue," | | 11 | "She ignored the suggestion." | | 12 | "It didn't fit the pattern." | | 13 | "She had seen this pattern" | | 14 | "He patted the victim's trousers" | | 15 | "He held it up." | | 16 | "It was a compass, small" | | 17 | "She looked at the face." | | 18 | "It wasn't marked with cardinal" | | 19 | "It was spinning lazily, then" |
| | ratio | 0.5 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 90 | | matches | | 0 | "The air down here tasted" | | 1 | "She stood at the edge" | | 2 | "The abandoned Tube station beneath" | | 3 | "DS Miller stood near the" | | 4 | "He looked uncomfortable, his shoulders" | | 5 | "He glanced back at Quinn," | | 6 | "Miller said, his voice echoing" | | 7 | "Quinn didn't answer immediately." | | 8 | "She moved with military precision," | | 9 | "She was forty-one, but the" | | 10 | "She stopped three feet from" | | 11 | "The victim was a young" | | 12 | "He lay on his back," | | 13 | "Quinn said, her voice flat" | | 14 | "She reached out, her gloved" | | 15 | "Miller leaned in, squinting." | | 16 | "She pointed to a small," | | 17 | "It wasn't blood." | | 18 | "It was a greyish residue," | | 19 | "Miller suggested, though he sounded" |
| | ratio | 0.956 | |
| 55.56% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 90 | | matches | | | ratio | 0.011 | |
| 48.87% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 5 | | matches | | 0 | "The air down here tasted of wet concrete and old electricity, a stale cocktail that settled in the back of Harlow Quinn’s throat." | | 1 | "He looked uncomfortable, his shoulders hunched as if the damp chill had seeped through his uniform." | | 2 | "The victim was a young man, dressed in clothes that seemed too expensive for the grime of the underworld." | | 3 | "It hummed against her palm, a vibration that traveled up her arm and settled in her teeth." | | 4 | "Her brown eyes were hard, devoid of the doubt that plagued most officers." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 5 | | matches | | 0 | "Quinn said, her voice flat" | | 1 | "Miller asked, his voice dropping" | | 2 | "she said, her tone hardening" | | 3 | "Quinn said, her voice low" | | 4 | "she said, her voice cutting through the damp air" |
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| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 4 | | fancyTags | | 0 | "Quinn corrected (correct)" | | 1 | "Miller suggested (suggest)" | | 2 | "she ordered (order)" | | 3 | "Miller laughed nervously (laugh)" |
| | dialogueSentences | 36 | | tagDensity | 0.361 | | leniency | 0.722 | | rawRatio | 0.308 | | effectiveRatio | 0.222 | |