| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.079 | | leniency | 0.158 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1030 | | 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) | |
| 22.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1030 | | totalAiIsms | 16 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | word | "down her spine" | | count | 1 |
| | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | |
| | highlights | | 0 | "familiar" | | 1 | "weight" | | 2 | "flicker" | | 3 | "etched" | | 4 | "stomach" | | 5 | "glint" | | 6 | "chill" | | 7 | "down her spine" | | 8 | "furrowed" | | 9 | "traced" | | 10 | "raced" | | 11 | "whisper" | | 12 | "pulse" | | 13 | "flicked" |
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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 | 2 | | narrationSentences | 104 | | matches | | 0 | "n in surprise" | | 1 | "n in terror" |
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| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 137 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 28 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 5 | | markdownWords | 6 | | totalWords | 1026 | | ratio | 0.006 | | matches | | 0 | "Shade" | | 1 | "anything" | | 2 | "Unexplained Circumstances" | | 3 | "happen" | | 4 | "shouldn’t" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 757 | | uniqueNames | 9 | | maxNameDensity | 1.85 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 1 | | Quinn | 14 | | Harris | 12 | | Morris | 1 | | Veil | 1 | | Compass | 1 | | Eva | 9 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Harris" | | 4 | "Morris" | | 5 | "Eva" |
| | places | (empty) | | globalScore | 0.575 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like something out of a grimoire" |
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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 | 1026 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 137 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 19.36 | | std | 14.23 | | cv | 0.735 | | sampleLengths | | 0 | 63 | | 1 | 41 | | 2 | 7 | | 3 | 52 | | 4 | 29 | | 5 | 23 | | 6 | 8 | | 7 | 9 | | 8 | 23 | | 9 | 21 | | 10 | 22 | | 11 | 36 | | 12 | 43 | | 13 | 23 | | 14 | 5 | | 15 | 22 | | 16 | 6 | | 17 | 1 | | 18 | 14 | | 19 | 12 | | 20 | 20 | | 21 | 3 | | 22 | 57 | | 23 | 12 | | 24 | 21 | | 25 | 26 | | 26 | 21 | | 27 | 4 | | 28 | 6 | | 29 | 8 | | 30 | 51 | | 31 | 8 | | 32 | 22 | | 33 | 11 | | 34 | 27 | | 35 | 15 | | 36 | 9 | | 37 | 16 | | 38 | 18 | | 39 | 10 | | 40 | 8 | | 41 | 13 | | 42 | 3 | | 43 | 20 | | 44 | 13 | | 45 | 16 | | 46 | 11 | | 47 | 17 | | 48 | 36 | | 49 | 8 |
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| 95.14% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 104 | | matches | | 0 | "been dropped" | | 1 | "were curled" | | 2 | "was etched" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 128 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 137 | | ratio | 0.015 | | matches | | 0 | "Quinn moved to the edge of the platform, her torch catching something else—a symbol, barely visible, carved into the stone." | | 1 | "The beam caught a figure at the edge of the light—a woman, her curly red hair a wild halo in the dimness." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 761 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.030223390275952694 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006570302233902759 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 137 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 137 | | mean | 7.49 | | std | 5.26 | | cv | 0.703 | | sampleLengths | | 0 | 11 | | 1 | 23 | | 2 | 14 | | 3 | 15 | | 4 | 16 | | 5 | 14 | | 6 | 11 | | 7 | 7 | | 8 | 3 | | 9 | 13 | | 10 | 16 | | 11 | 3 | | 12 | 2 | | 13 | 15 | | 14 | 6 | | 15 | 8 | | 16 | 10 | | 17 | 4 | | 18 | 1 | | 19 | 9 | | 20 | 14 | | 21 | 8 | | 22 | 2 | | 23 | 7 | | 24 | 7 | | 25 | 13 | | 26 | 3 | | 27 | 10 | | 28 | 4 | | 29 | 7 | | 30 | 2 | | 31 | 20 | | 32 | 7 | | 33 | 5 | | 34 | 7 | | 35 | 17 | | 36 | 4 | | 37 | 7 | | 38 | 13 | | 39 | 9 | | 40 | 10 | | 41 | 4 | | 42 | 19 | | 43 | 3 | | 44 | 2 | | 45 | 3 | | 46 | 5 | | 47 | 14 | | 48 | 6 | | 49 | 1 |
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| 45.99% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.30656934306569344 | | totalSentences | 137 | | uniqueOpeners | 42 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 87 | | matches | | 0 | "Just the faintest trace of" | | 1 | "Just the hum of the" | | 2 | "Then she saw it." | | 3 | "Then she heard it." | | 4 | "Just a man, frozen in" |
| | ratio | 0.057 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 87 | | matches | | 0 | "She adjusted the worn leather" | | 1 | "He looked up as she" | | 2 | "She didn’t answer." | | 3 | "His fingers were curled, as" | | 4 | "She pried them open." | | 5 | "She glanced up, her brown" | | 6 | "He held out a small" | | 7 | "Her stomach twisted." | | 8 | "She took the bag, turning" | | 9 | "She knelt again, brushing aside" | | 10 | "She picked it up." | | 11 | "She flipped the compass over." | | 12 | "She pocketed the compass." | | 13 | "She didn’t answer." | | 14 | "She traced it with her" | | 15 | "She turned, her hand instinctively" | | 16 | "He shook his head." | | 17 | "She wore round glasses, and" | | 18 | "Her green eyes locked onto" | | 19 | "She tucked a strand of" |
| | ratio | 0.264 | |
| 23.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 87 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "The air hung thick, the" | | 3 | "She adjusted the worn leather" | | 4 | "PC Harris stood by a" | | 5 | "He looked up as she" | | 6 | "She didn’t answer." | | 7 | "The beam of her torch" | | 8 | "A man, mid-thirties, sprawled on" | | 9 | "Quinn crouched, her sharp jaw" | | 10 | "The victim’s eyes were wide," | | 11 | "His fingers were curled, as" | | 12 | "She pried them open." | | 13 | "Harris said, shifting his weight" | | 14 | "She glanced up, her brown" | | 15 | "He held out a small" | | 16 | "Her stomach twisted." | | 17 | "She took the bag, turning" | | 18 | "The tokens were cold." | | 19 | "Quinn stood, her gaze sweeping" |
| | ratio | 0.874 | |
| 57.47% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 87 | | matches | | | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 1 | | matches | | 0 | "Detective Harlow Quinn stepped over a rotted timber beam, her boots kicking up dust that swirled in the dim glow of her torch." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 1 | | matches | | 0 | "She glanced up, her brown eyes sharp" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.026 | | leniency | 0.053 | | rawRatio | 0 | | effectiveRatio | 0 | |