| 33.33% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 1 | | adverbTags | | 0 | "The man tutted softly [softly]" |
| | dialogueSentences | 10 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0.167 | | effectiveRatio | 0.167 | |
| 95.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1144 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 43.18% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1144 | | totalAiIsms | 13 | | found | | | highlights | | 0 | "familiar" | | 1 | "facade" | | 2 | "pulsed" | | 3 | "weight" | | 4 | "rhythmic" | | 5 | "cacophony" | | 6 | "constructed" | | 7 | "standard" | | 8 | "scanned" | | 9 | "stomach" | | 10 | "encounter" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 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 | 1 | | hedgeCount | 1 | | narrationSentences | 91 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 95 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 9 | | totalWords | 1136 | | ratio | 0.008 | | matches | | 0 | "Call it in. Wait for backup. Secure the perimeter." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 1053 | | uniqueNames | 10 | | maxNameDensity | 0.47 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Lock | 1 | | Quinn | 5 | | London | 2 | | Tube | 2 | | Oyster | 1 | | Morris | 1 | | Glock | 2 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Glock" |
| | places | | 0 | "Camden" | | 1 | "Lock" | | 2 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 62 | | glossingSentenceCount | 5 | | matches | | 0 | "something like damp soil and copper" | | 1 | "looked like captured moonlight" | | 2 | "felt like a child’s toy against the imp" | | 3 | "felt like it was made of lead, freezing" | | 4 | "suit that seemed to absorb the strange light of the market rather than reflect it" |
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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 | 1136 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 49.39 | | std | 30.69 | | cv | 0.621 | | sampleLengths | | 0 | 101 | | 1 | 54 | | 2 | 69 | | 3 | 56 | | 4 | 58 | | 5 | 31 | | 6 | 90 | | 7 | 44 | | 8 | 74 | | 9 | 8 | | 10 | 115 | | 11 | 10 | | 12 | 95 | | 13 | 20 | | 14 | 37 | | 15 | 51 | | 16 | 36 | | 17 | 21 | | 18 | 9 | | 19 | 32 | | 20 | 23 | | 21 | 23 | | 22 | 79 |
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| 85.98% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 91 | | matches | | 0 | "was supposed" | | 1 | "were lit" | | 2 | "was gone" | | 3 | "was gone" | | 4 | "was made" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 165 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 95 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 314 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 5 | | adverbRatio | 0.01592356687898089 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 95 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 95 | | mean | 11.96 | | std | 7.8 | | cv | 0.652 | | sampleLengths | | 0 | 17 | | 1 | 19 | | 2 | 17 | | 3 | 16 | | 4 | 11 | | 5 | 21 | | 6 | 4 | | 7 | 4 | | 8 | 29 | | 9 | 4 | | 10 | 13 | | 11 | 33 | | 12 | 13 | | 13 | 3 | | 14 | 20 | | 15 | 8 | | 16 | 21 | | 17 | 11 | | 18 | 9 | | 19 | 7 | | 20 | 5 | | 21 | 14 | | 22 | 5 | | 23 | 21 | | 24 | 13 | | 25 | 24 | | 26 | 7 | | 27 | 14 | | 28 | 5 | | 29 | 3 | | 30 | 3 | | 31 | 25 | | 32 | 6 | | 33 | 3 | | 34 | 18 | | 35 | 4 | | 36 | 9 | | 37 | 16 | | 38 | 12 | | 39 | 16 | | 40 | 4 | | 41 | 28 | | 42 | 42 | | 43 | 8 | | 44 | 18 | | 45 | 12 | | 46 | 15 | | 47 | 20 | | 48 | 21 | | 49 | 5 |
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| 55.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.37894736842105264 | | totalSentences | 95 | | uniqueOpeners | 36 | |
| 37.88% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 88 | | matches | | 0 | "Too clean, too controlled." |
| | ratio | 0.011 | |
| 60.91% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 88 | | matches | | 0 | "Her lungs burned, a familiar" | | 1 | "He moved with a liquid" | | 2 | "He wasn't a junkie." | | 3 | "He dodged through a narrow" | | 4 | "She ignored it." | | 5 | "Her focus narrowed to the" | | 6 | "He shouldn't be able to" | | 7 | "He reached the turnstiles, long" | | 8 | "It wasn't an Oyster card." | | 9 | "He swiped it across an" | | 10 | "He slipped through without a" | | 11 | "She’d seen the symbols carved" | | 12 | "She drew her Glock, the" | | 13 | "It swung open silently, the" | | 14 | "She was not in an" | | 15 | "They were lit by flickering" | | 16 | "She’d heard whispers of it," | | 17 | "She was an intruder here," | | 18 | "Her standard-issue Glock felt like" | | 19 | "She walked like a cop." |
| | ratio | 0.398 | |
| 33.86% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 88 | | matches | | 0 | "The suspect vaulted the railings" | | 1 | "Harlow Quinn followed, the worn" | | 2 | "Her lungs burned, a familiar" | | 3 | "Rain slicked the cobblestones below," | | 4 | "The suspect, a lean figure" | | 5 | "He moved with a liquid" | | 6 | "He wasn't a junkie." | | 7 | "This was the third time" | | 8 | "Each time, he’d vanished." | | 9 | "Tonight, she was close enough" | | 10 | "He dodged through a narrow" | | 11 | "Quinn’s foot splashed in a" | | 12 | "She ignored it." | | 13 | "Her focus narrowed to the" | | 14 | "The alley opened onto a" | | 15 | "A faded sign for" | | 16 | "The city had sealed these" | | 17 | "He shouldn't be able to" | | 18 | "The suspect didn't break stride." | | 19 | "He reached the turnstiles, long" |
| | ratio | 0.852 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 4 | | matches | | 0 | "He moved with a liquid grace that spoke of parkour or prison-yard gymnastics, not the panicked clumsiness of a common thief." | | 1 | "She spun around, her gun coming up, her finger tightening on the trigger." | | 2 | "He was impeccably dressed in a sharp, three-piece suit that seemed to absorb the strange light of the market rather than reflect it." | | 3 | "His face was unremarkable, the kind you would forget instantly, but his eyes held a depth that felt ancient and predatory." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 2 | | matches | | 0 | "The man tutted softly, as if disappointed" | | 1 | "He took, his polished shoes making no sound on the grimy concrete" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |