| 88.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said again [again]" |
| | dialogueSentences | 18 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.167 | | effectiveRatio | 0.111 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1202 | | 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) | |
| 75.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1202 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "echoed" | | 1 | "charged" | | 2 | "flickered" | | 3 | "pulse" | | 4 | "racing" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "without second thought" | | count | 1 |
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| | highlights | | 0 | "without a second thought" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 115 | | matches | (empty) | |
| 80.75% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 115 | | filterMatches | | | hedgeMatches | | 0 | "appeared to" | | 1 | "seemed to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1206 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 98.84% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1075 | | uniqueNames | 14 | | maxNameDensity | 1.02 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Quinn | 11 | | Camden | 1 | | Tube | 2 | | Morris | 3 | | Raven | 1 | | Nest | 1 | | Veil | 1 | | Market | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 4 | | Was | 1 | | Three | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Market" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Herrera" | | 6 | "Was" |
| | places | | 0 | "London" | | 1 | "Camden" | | 2 | "Raven" |
| | globalScore | 0.988 | | windowScore | 1 | |
| 58.54% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | glossingSentenceCount | 3 | | matches | | 0 | "herbs that seemed to shift colours when she wasn't looking directly at them" | | 1 | "looked like bones" | | 2 | "looked like an old ticket booth, now conv" |
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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 | 1206 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 127 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 24.12 | | std | 18.32 | | cv | 0.76 | | sampleLengths | | 0 | 57 | | 1 | 33 | | 2 | 37 | | 3 | 5 | | 4 | 2 | | 5 | 39 | | 6 | 53 | | 7 | 33 | | 8 | 14 | | 9 | 48 | | 10 | 22 | | 11 | 8 | | 12 | 52 | | 13 | 30 | | 14 | 3 | | 15 | 71 | | 16 | 27 | | 17 | 19 | | 18 | 16 | | 19 | 7 | | 20 | 41 | | 21 | 8 | | 22 | 50 | | 23 | 3 | | 24 | 25 | | 25 | 25 | | 26 | 20 | | 27 | 8 | | 28 | 3 | | 29 | 12 | | 30 | 39 | | 31 | 8 | | 32 | 8 | | 33 | 7 | | 34 | 9 | | 35 | 29 | | 36 | 48 | | 37 | 10 | | 38 | 66 | | 39 | 16 | | 40 | 9 | | 41 | 19 | | 42 | 31 | | 43 | 9 | | 44 | 6 | | 45 | 55 | | 46 | 28 | | 47 | 17 | | 48 | 14 | | 49 | 7 |
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| 99.16% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 115 | | matches | | 0 | "been carved" | | 1 | "been transformed" |
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| 87.64% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 178 | | matches | | 0 | "was running" | | 1 | "wasn't listening" | | 2 | "wasn't looking" |
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| 30.37% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 127 | | ratio | 0.039 | | matches | | 0 | "Her watch — the old leather band worn smooth from years of wear — caught the light as she checked the time." | | 1 | "But something else lingered beneath it — an odd sweetness, like incense burning in a church." | | 2 | "But the market — it was still active." | | 3 | "About the case that had killed him — though officially, it hadn't." | | 4 | "The air had changed again — thicker now, charged with something that made the fine hairs on her arms stand on end." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1071 | | adjectiveStacks | 1 | | stackExamples | | 0 | "hammered against wet cobblestones," |
| | adverbCount | 30 | | adverbRatio | 0.028011204481792718 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007469654528478058 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 9.5 | | std | 6.2 | | cv | 0.653 | | sampleLengths | | 0 | 17 | | 1 | 17 | | 2 | 23 | | 3 | 3 | | 4 | 19 | | 5 | 2 | | 6 | 9 | | 7 | 13 | | 8 | 8 | | 9 | 16 | | 10 | 5 | | 11 | 2 | | 12 | 5 | | 13 | 11 | | 14 | 23 | | 15 | 10 | | 16 | 10 | | 17 | 22 | | 18 | 4 | | 19 | 7 | | 20 | 22 | | 21 | 3 | | 22 | 8 | | 23 | 8 | | 24 | 6 | | 25 | 7 | | 26 | 1 | | 27 | 1 | | 28 | 16 | | 29 | 4 | | 30 | 19 | | 31 | 3 | | 32 | 8 | | 33 | 11 | | 34 | 8 | | 35 | 6 | | 36 | 14 | | 37 | 7 | | 38 | 15 | | 39 | 10 | | 40 | 3 | | 41 | 18 | | 42 | 9 | | 43 | 3 | | 44 | 8 | | 45 | 6 | | 46 | 8 | | 47 | 14 | | 48 | 12 | | 49 | 23 |
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| 65.88% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.4566929133858268 | | totalSentences | 127 | | uniqueOpeners | 58 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 107 | | matches | | 0 | "Then he was through the" | | 1 | "Then he stepped aside, gesturing" | | 2 | "Then she walked toward the" | | 3 | "Then she stepped forward into" |
| | ratio | 0.037 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 107 | | matches | | 0 | "Her hand pressed against the" | | 1 | "She needed to end this." | | 2 | "Her hand found the torch" | | 3 | "She'd heard whispers about places" | | 4 | "Her watch — the old" | | 5 | "She'd been chasing this kid" | | 6 | "she muttered, starting down the" | | 7 | "She'd found it." | | 8 | "Her fingers brushed the grip" | | 9 | "She counted the exits." | | 10 | "Their eyes met." | | 11 | "She held up her badge," | | 12 | "He glanced at the badge," | | 13 | "She'd learned, over the past" | | 14 | "He paused, letting the sounds" | | 15 | "She'd spent three years pretending" | | 16 | "she said again, quieter this" | | 17 | "He caught her arm as" | | 18 | "She clicked her torch back" | | 19 | "She could still turn back." |
| | ratio | 0.196 | |
| 95.51% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 107 | | matches | | 0 | "The rain came down in" | | 1 | "Harlow Quinn's boots hammered against" | | 2 | "Quinn didn't slow." | | 3 | "Her hand pressed against the" | | 4 | "The alley stretched ahead, narrow" | | 5 | "A single flickering streetlight cast" | | 6 | "The suspect was already at" | | 7 | "She needed to end this." | | 8 | "The figure's head snapped round." | | 9 | "A young face, pale and" | | 10 | "The stairwell on the other" | | 11 | "The kind of darkness that" | | 12 | "Her hand found the torch" | | 13 | "An abandoned Tube station." | | 14 | "She'd heard whispers about places" | | 15 | "Her watch — the old" | | 16 | "Quarter past midnight." | | 17 | "She'd been chasing this kid" | | 18 | "she muttered, starting down the" | | 19 | "Each step echoed in the" |
| | ratio | 0.729 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 107 | | matches | (empty) | | ratio | 0 | |
| 80.75% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 46 | | technicalSentenceCount | 4 | | matches | | 0 | "Twenty metres ahead, the figure in the dark jacket ducked around a corner, vanishing into the warren of alleyways that threaded through Camden." | | 1 | "Then he was through the door, and Quinn was running again, her shoulder slamming into the metal as she burst through after him." | | 2 | "The witnesses who spoke of impossible things before clamming up entirely." | | 3 | "She clicked her torch back on, the beam revealing rough-hewn walls that looked far older than any Tube station had a right to be." |
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| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 1 | | matches | | 0 | "He caught, his grip firm but not aggressive" |
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| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "she pressed (press)" |
| | dialogueSentences | 18 | | tagDensity | 0.167 | | leniency | 0.333 | | rawRatio | 0.667 | | effectiveRatio | 0.222 | |