| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 143 | | tagDensity | 0.049 | | leniency | 0.098 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2426 | | 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) | |
| 79.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2426 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "velvet" | | 1 | "magnetic" | | 2 | "trembled" | | 3 | "stark" | | 4 | "pulse" | | 5 | "silence" | | 6 | "flicked" |
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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 | 203 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 203 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 339 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2426 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 96 | | wordCount | 1641 | | uniqueNames | 12 | | maxNameDensity | 2.32 | | worstName | "Quinn" | | maxWindowNameDensity | 6 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 38 | | Tube | 1 | | Camden | 1 | | Amit | 1 | | Malik | 24 | | London | 1 | | Transport | 1 | | Renata | 1 | | Vale | 11 | | Eva | 13 | | One | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Amit" | | 3 | "Malik" | | 4 | "Renata" | | 5 | "Vale" | | 6 | "Eva" | | 7 | "One" |
| | places | | | globalScore | 0.342 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 124 | | glossingSentenceCount | 2 | | matches | | 0 | "appeared keen to cross" | | 1 | "looked like scratches from broken glass," |
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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 | 2426 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 339 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 256 | | mean | 9.48 | | std | 10.68 | | cv | 1.127 | | sampleLengths | | 0 | 14 | | 1 | 34 | | 2 | 68 | | 3 | 6 | | 4 | 9 | | 5 | 14 | | 6 | 1 | | 7 | 18 | | 8 | 6 | | 9 | 6 | | 10 | 1 | | 11 | 22 | | 12 | 15 | | 13 | 4 | | 14 | 6 | | 15 | 1 | | 16 | 5 | | 17 | 3 | | 18 | 35 | | 19 | 4 | | 20 | 7 | | 21 | 5 | | 22 | 23 | | 23 | 59 | | 24 | 14 | | 25 | 9 | | 26 | 63 | | 27 | 6 | | 28 | 7 | | 29 | 3 | | 30 | 33 | | 31 | 2 | | 32 | 4 | | 33 | 2 | | 34 | 20 | | 35 | 12 | | 36 | 22 | | 37 | 4 | | 38 | 5 | | 39 | 4 | | 40 | 7 | | 41 | 5 | | 42 | 30 | | 43 | 21 | | 44 | 4 | | 45 | 6 | | 46 | 6 | | 47 | 5 | | 48 | 1 | | 49 | 7 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 203 | | matches | | 0 | "been burned" | | 1 | "been tailored" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 282 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 339 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1652 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.01513317191283293 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0018159806295399517 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 339 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 339 | | mean | 7.16 | | std | 5.2 | | cv | 0.727 | | sampleLengths | | 0 | 14 | | 1 | 14 | | 2 | 7 | | 3 | 3 | | 4 | 10 | | 5 | 8 | | 6 | 27 | | 7 | 33 | | 8 | 6 | | 9 | 9 | | 10 | 3 | | 11 | 11 | | 12 | 1 | | 13 | 14 | | 14 | 4 | | 15 | 6 | | 16 | 6 | | 17 | 1 | | 18 | 22 | | 19 | 2 | | 20 | 13 | | 21 | 4 | | 22 | 6 | | 23 | 1 | | 24 | 5 | | 25 | 3 | | 26 | 10 | | 27 | 14 | | 28 | 2 | | 29 | 2 | | 30 | 7 | | 31 | 4 | | 32 | 7 | | 33 | 5 | | 34 | 14 | | 35 | 9 | | 36 | 8 | | 37 | 31 | | 38 | 8 | | 39 | 12 | | 40 | 14 | | 41 | 9 | | 42 | 15 | | 43 | 8 | | 44 | 18 | | 45 | 6 | | 46 | 16 | | 47 | 6 | | 48 | 7 | | 49 | 3 |
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| 49.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3215339233038348 | | totalSentences | 339 | | uniqueOpeners | 109 | |
| 17.64% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 189 | | matches | | | ratio | 0.005 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 189 | | matches | | 0 | "It marked the ribbed metal" | | 1 | "Her worn leather watch read" | | 2 | "She lowered her face until" | | 3 | "He paused, hopping once." | | 4 | "It no longer resembled anything" | | 5 | "His hands lay palms down," | | 6 | "His face, visible through the" | | 7 | "Her mouth formed a hard" | | 8 | "It smelt of damp paper," | | 9 | "She lifted the man’s chin" | | 10 | "His eyelids stood half open." | | 11 | "His shoes shone except at" | | 12 | "He looked at the dry" | | 13 | "They looked like scratches from" | | 14 | "Its needle pointed at the" | | 15 | "His fingers had no marks." | | 16 | "He stepped into the booth" | | 17 | "He shifted right." | | 18 | "It followed again." | | 19 | "He withdrew, breath rasping inside" |
| | ratio | 0.169 | |
| 34.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 161 | | totalSentences | 189 | | matches | | 0 | "Detective Harlow Quinn found the" | | 1 | "It marked the ribbed metal" | | 2 | "A second stain waited three" | | 3 | "Each one had dried to" | | 4 | "Quinn crouched, trench coat pulling" | | 5 | "The escalator descended into an" | | 6 | "Her worn leather watch read" | | 7 | "DC Amit Malik called from" | | 8 | "Quinn touched nothing." | | 9 | "She lowered her face until" | | 10 | "Malik stood with one shoe" | | 11 | "He paused, hopping once." | | 12 | "Military habits kept her balance" | | 13 | "Malik pulled on his second" | | 14 | "Quinn looked up." | | 15 | "The curved ceiling above the" | | 16 | "Some hard point dragged along" | | 17 | "Malik followed her gaze." | | 18 | "It no longer resembled anything" | | 19 | "Stalls crowded the floor beneath" |
| | ratio | 0.852 | |
| 26.46% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 189 | | matches | | | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 59 | | technicalSentenceCount | 2 | | matches | | 0 | "Black canvas awnings sagged over counters covered in stoppered bottles, knives with teeth carved into their handles, bird skulls bound in copper wire and blocks…" | | 1 | "His hands lay palms down, fingers spread, as if someone had arranged him for a lesson in anatomy." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 2 | | fancyTags | | 0 | "the officer muttered (mutter)" | | 1 | "Malik continued (continue)" |
| | dialogueSentences | 143 | | tagDensity | 0.042 | | leniency | 0.084 | | rawRatio | 0.333 | | effectiveRatio | 0.028 | |