| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.40% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1390 | | 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) | |
| 71.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1390 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "gloom" | | 1 | "weight" | | 2 | "scanned" | | 3 | "etched" | | 4 | "vibrated" | | 5 | "magnetic" | | 6 | "flickered" | | 7 | "echoed" |
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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 | 286 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 7 | | hedgeCount | 0 | | narrationSentences | 286 | | filterMatches | | 0 | "watch" | | 1 | "think" | | 2 | "see" | | 3 | "know" |
| | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 286 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 21 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1387 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 86 | | wordCount | 1387 | | uniqueNames | 12 | | maxNameDensity | 2.16 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Miller" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Harlow | 3 | | Quinn | 30 | | London | 2 | | Miller | 29 | | Guv | 2 | | Morris | 4 | | Detective | 2 | | Veil | 1 | | Market | 1 | | You | 10 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Morris" | | 4 | "Market" | | 5 | "You" |
| | places | | | globalScore | 0.419 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 77 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1387 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 286 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 102 | | mean | 13.6 | | std | 12.39 | | cv | 0.911 | | sampleLengths | | 0 | 58 | | 1 | 31 | | 2 | 16 | | 3 | 36 | | 4 | 3 | | 5 | 13 | | 6 | 49 | | 7 | 6 | | 8 | 7 | | 9 | 54 | | 10 | 5 | | 11 | 1 | | 12 | 9 | | 13 | 23 | | 14 | 8 | | 15 | 18 | | 16 | 45 | | 17 | 10 | | 18 | 19 | | 19 | 15 | | 20 | 13 | | 21 | 6 | | 22 | 4 | | 23 | 37 | | 24 | 2 | | 25 | 4 | | 26 | 18 | | 27 | 10 | | 28 | 37 | | 29 | 10 | | 30 | 10 | | 31 | 29 | | 32 | 4 | | 33 | 3 | | 34 | 3 | | 35 | 29 | | 36 | 11 | | 37 | 4 | | 38 | 26 | | 39 | 3 | | 40 | 2 | | 41 | 2 | | 42 | 15 | | 43 | 32 | | 44 | 7 | | 45 | 1 | | 46 | 10 | | 47 | 9 | | 48 | 4 | | 49 | 38 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 286 | | matches | | 0 | "was dilated" | | 1 | "been dragged" | | 2 | "was taken" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 299 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 286 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1390 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.020863309352517987 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004316546762589928 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 286 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 286 | | mean | 4.85 | | std | 2.95 | | cv | 0.608 | | sampleLengths | | 0 | 21 | | 1 | 16 | | 2 | 10 | | 3 | 10 | | 4 | 1 | | 5 | 15 | | 6 | 16 | | 7 | 6 | | 8 | 10 | | 9 | 4 | | 10 | 9 | | 11 | 14 | | 12 | 3 | | 13 | 6 | | 14 | 3 | | 15 | 4 | | 16 | 4 | | 17 | 5 | | 18 | 5 | | 19 | 5 | | 20 | 5 | | 21 | 14 | | 22 | 3 | | 23 | 5 | | 24 | 3 | | 25 | 9 | | 26 | 6 | | 27 | 5 | | 28 | 2 | | 29 | 2 | | 30 | 7 | | 31 | 12 | | 32 | 7 | | 33 | 12 | | 34 | 4 | | 35 | 10 | | 36 | 3 | | 37 | 2 | | 38 | 1 | | 39 | 9 | | 40 | 8 | | 41 | 10 | | 42 | 5 | | 43 | 5 | | 44 | 3 | | 45 | 6 | | 46 | 2 | | 47 | 10 | | 48 | 5 | | 49 | 10 |
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| 58.33% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.3732394366197183 | | totalSentences | 284 | | uniqueOpeners | 106 | |
| 70.62% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 236 | | matches | | 0 | "Just bricks, Guv." | | 1 | "Just like you." | | 2 | "Then I'll make new rules." | | 3 | "Only his voice remained." | | 4 | "Only the compass glow remained." |
| | ratio | 0.021 | |
| 77.63% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 84 | | totalSentences | 236 | | matches | | 0 | "She checked the worn leather" | | 1 | "He turned as Quinn approached," | | 2 | "She crouched beside the body" | | 3 | "She lifted the man's eyelid." | | 4 | "She ran a finger along" | | 5 | "She turned the hand over." | | 6 | "She wiped her hand on" | | 7 | "Her salt-and-pepper hair caught the" | | 8 | "She reached into her coat" | | 9 | "You're holding a toy compass" | | 10 | "It vibrated against her skin." | | 11 | "You sound like those conspiracy" | | 12 | "She walked toward the tunnel," | | 13 | "She recognised the pattern from" | | 14 | "We need to secure the" | | 15 | "You go in there alone," | | 16 | "She looked Miller in the" | | 17 | "Her bearing stiffened, military precision" | | 18 | "It's a marketplace." | | 19 | "He hadn't been dragged either." |
| | ratio | 0.356 | |
| 53.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 192 | | totalSentences | 236 | | matches | | 0 | "Detective Harlow Quinn stepped over" | | 1 | "The air hung heavy, colder" | | 2 | "She checked the worn leather" | | 3 | "PC Miller stood near the" | | 4 | "He turned as Quinn approached," | | 5 | "Place gives me the creeps," | | 6 | "Quinn ignored the comment." | | 7 | "She crouched beside the body" | | 8 | "A man, mid-thirties, dressed in" | | 9 | "Cause of death?" | | 10 | "Miller shifted his weight." | | 11 | "Paramedics say heart failure." | | 12 | "Looks like he just dropped." | | 13 | "Quinn pulled on latex gloves." | | 14 | "She lifted the man's eyelid." | | 15 | "The pupil was dilated, fixed." | | 16 | "She ran a finger along" | | 17 | "Skin cold, waxy." | | 18 | "She turned the hand over." | | 19 | "Palm facing up." |
| | ratio | 0.814 | |
| 21.19% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 236 | | matches | | 0 | "Because the market opens at" |
| | ratio | 0.004 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 1 | | matches | | 0 | "Damp tiles lined the walls of the abandoned Camden Tube station, slick with condensation that smelled of rust and old copper." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |