| 82.35% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva said softly [softly]" | | 1 | "Quinn said slowly [slowly]" |
| | dialogueSentences | 34 | | tagDensity | 0.441 | | leniency | 0.882 | | rawRatio | 0.133 | | effectiveRatio | 0.118 | |
| 87.77% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1226 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "gently" | | 1 | "softly" | | 2 | "slowly" |
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| 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) | |
| 79.61% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1226 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "mosaic" | | 1 | "could feel" | | 2 | "silence" | | 3 | "weight" | | 4 | "etched" |
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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 | 67 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 67 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 85 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 83 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1218 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 71.02% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 823 | | uniqueNames | 11 | | maxNameDensity | 1.58 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Detective | 1 | | Harlow | 1 | | Quinn | 13 | | Tube | 1 | | Adeyemi | 4 | | Kowalski | 1 | | Roman | 1 | | Eva | 8 | | Recognition | 1 | | Morris | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Adeyemi" | | 3 | "Kowalski" | | 4 | "Roman" | | 5 | "Eva" | | 6 | "Morris" |
| | places | (empty) | | globalScore | 0.71 | | windowScore | 0.833 | |
| 28.05% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like this" | | 1 | "looked like she should be giving a semina" |
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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 | 1218 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 36.91 | | std | 28.69 | | cv | 0.777 | | sampleLengths | | 0 | 16 | | 1 | 98 | | 2 | 51 | | 3 | 11 | | 4 | 59 | | 5 | 14 | | 6 | 26 | | 7 | 8 | | 8 | 85 | | 9 | 17 | | 10 | 24 | | 11 | 57 | | 12 | 5 | | 13 | 44 | | 14 | 3 | | 15 | 34 | | 16 | 16 | | 17 | 74 | | 18 | 17 | | 19 | 48 | | 20 | 53 | | 21 | 4 | | 22 | 118 | | 23 | 62 | | 24 | 38 | | 25 | 14 | | 26 | 53 | | 27 | 65 | | 28 | 5 | | 29 | 44 | | 30 | 19 | | 31 | 25 | | 32 | 11 |
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| 79.08% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 67 | | matches | | 0 | "been found" | | 1 | "was fixed" | | 2 | "was turned" | | 3 | "was scratched" | | 4 | "been drawn" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 133 | | matches | | 0 | "was watching" | | 1 | "was ducking" | | 2 | "wasn't lettering" | | 3 | "wasn't pointing" | | 4 | "was pointing" |
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| 42.02% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 85 | | ratio | 0.035 | | matches | | 0 | "The abandoned Tube station smelled of rust and standing water, of old ozone and something else—something sweet and wrong, like flowers left too long in a sealed room." | | 1 | "Male, late fifties, dressed well—tweed overcoat, silver watch chain, brogues that had seen polish but not recently." | | 2 | "\"There's a market. Underground, literally, most of the time. It sells things that shouldn't exist to people who shouldn't have them. Entry by token—\" she nodded at Adeyemi's evidence bag, \"—only the tokens are usually destroyed at the threshold. He kept his. That means either he was turned away, or he left through somewhere the tokens don't get collected.\" She pulled a book from her satchel, a slim volume with a cracked spine, and held it open to a page of diagrams." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 713 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.0364656381486676 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.012622720897615708 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 85 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 85 | | mean | 14.33 | | std | 13.44 | | cv | 0.938 | | sampleLengths | | 0 | 16 | | 1 | 28 | | 2 | 20 | | 3 | 7 | | 4 | 43 | | 5 | 16 | | 6 | 22 | | 7 | 13 | | 8 | 5 | | 9 | 6 | | 10 | 5 | | 11 | 17 | | 12 | 18 | | 13 | 1 | | 14 | 18 | | 15 | 3 | | 16 | 2 | | 17 | 9 | | 18 | 10 | | 19 | 16 | | 20 | 3 | | 21 | 5 | | 22 | 9 | | 23 | 24 | | 24 | 23 | | 25 | 1 | | 26 | 28 | | 27 | 17 | | 28 | 3 | | 29 | 21 | | 30 | 2 | | 31 | 28 | | 32 | 27 | | 33 | 5 | | 34 | 35 | | 35 | 2 | | 36 | 7 | | 37 | 3 | | 38 | 32 | | 39 | 2 | | 40 | 16 | | 41 | 5 | | 42 | 21 | | 43 | 38 | | 44 | 10 | | 45 | 7 | | 46 | 5 | | 47 | 5 | | 48 | 4 | | 49 | 44 |
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| 78.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.5176470588235295 | | totalSentences | 85 | | uniqueOpeners | 44 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 54 | | matches | | 0 | "Then a voice, bright and" |
| | ratio | 0.019 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 54 | | matches | | 0 | "She crouched beside the body." | | 1 | "His face was fixed in" | | 2 | "She was watching the shadows." | | 3 | "She walked to the column" | | 4 | "She was twenty-six, freckled, and" | | 5 | "She looked up, green eyes" | | 6 | "she nodded at Adeyemi's evidence" | | 7 | "It lay across the tiles" | | 8 | "She approached it the way" | | 9 | "It was pointing at the" | | 10 | "She thought of the third" | | 11 | "She thought of Morris." | | 12 | "She thought of the tallies," | | 13 | "She pocketed the compass in" |
| | ratio | 0.259 | |
| 80.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 54 | | matches | | 0 | "The body had been found" | | 1 | "The abandoned Tube station smelled" | | 2 | "Floodlights the uniformed team had" | | 3 | "The mosaic work beneath her" | | 4 | "She crouched beside the body." | | 5 | "His face was fixed in" | | 6 | "The look of a man" | | 7 | "That was the first thing" | | 8 | "Quinn didn't answer." | | 9 | "She was watching the shadows." | | 10 | "The floodlights stood in four" | | 11 | "She walked to the column" | | 12 | "Eva Kowalski was ducking under" | | 13 | "She was twenty-six, freckled, and" | | 14 | "Eva stopped, tucked a strand" | | 15 | "Eva knelt a careful distance" | | 16 | "Quinn weighed eighteen years of" | | 17 | "Eva lifted the hand gently." | | 18 | "The fingers were stained grey" | | 19 | "She looked up, green eyes" |
| | ratio | 0.759 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 1 | | matches | | 0 | "The look of a man who had understood, one half-second too late, that he had made a mistake." |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "She pocketed, and the needle strained toward the rift-shadow even through the plastic" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 34 | | tagDensity | 0.235 | | leniency | 0.471 | | rawRatio | 0.125 | | effectiveRatio | 0.059 | |