| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1084 | | 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) | |
| 49.26% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1084 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "gloom" | | 1 | "silence" | | 2 | "footsteps" | | 3 | "resonated" | | 4 | "echoed" | | 5 | "pulsed" | | 6 | "rhythmic" | | 7 | "scanned" | | 8 | "weight" |
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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 | 96 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 96 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1083 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 1 | | matches | | 0 | "This time, she told herself, she would catch the end before it disappeared." |
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| 99.22% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 1083 | | uniqueNames | 11 | | maxNameDensity | 1.02 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 11 | | Soho | 1 | | Morris | 5 | | Tube | 1 | | Camden | 1 | | Veil | 3 | | Market | 3 | | London | 1 | | Raven | 1 | | Nest | 1 | | Londoners | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Morris" | | 2 | "Market" | | 3 | "Raven" |
| | places | | | globalScore | 0.992 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | glossingSentenceCount | 1 | | matches | | 0 | "tasted like copper on the back of her ton" |
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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 | 1083 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 96.70% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 21 | | mean | 51.57 | | std | 25.19 | | cv | 0.488 | | sampleLengths | | 0 | 97 | | 1 | 100 | | 2 | 83 | | 3 | 56 | | 4 | 63 | | 5 | 42 | | 6 | 76 | | 7 | 6 | | 8 | 59 | | 9 | 30 | | 10 | 61 | | 11 | 47 | | 12 | 54 | | 13 | 6 | | 14 | 72 | | 15 | 30 | | 16 | 41 | | 17 | 17 | | 18 | 46 | | 19 | 54 | | 20 | 43 |
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| 90.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 96 | | matches | | 0 | "was boarded" | | 1 | "was muffled" | | 2 | "was made" | | 3 | "was dragged" |
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| 13.78% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 179 | | matches | | 0 | "was running" | | 1 | "was waxing" | | 2 | "wasn't just entering" | | 3 | "was walking" | | 4 | "was breaking" |
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| 53.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 2 | | flaggedSentences | 3 | | totalSentences | 96 | | ratio | 0.031 | | matches | | 0 | "The rain did not wash the city clean; it only slicked the grime into a permanent sheen." | | 1 | "The smell hit her first—damp rot, ozone, and something metallic that tasted like copper on the back of her tongue." | | 2 | "The moon was waxing; if she missed the window, the Market would shift locations again, leaving her chasing a ghost." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1090 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.027522935779816515 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006422018348623854 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 11.28 | | std | 6.35 | | cv | 0.563 | | sampleLengths | | 0 | 17 | | 1 | 24 | | 2 | 13 | | 3 | 18 | | 4 | 14 | | 5 | 11 | | 6 | 10 | | 7 | 17 | | 8 | 29 | | 9 | 10 | | 10 | 21 | | 11 | 13 | | 12 | 22 | | 13 | 16 | | 14 | 18 | | 15 | 13 | | 16 | 14 | | 17 | 17 | | 18 | 18 | | 19 | 5 | | 20 | 5 | | 21 | 4 | | 22 | 4 | | 23 | 3 | | 24 | 4 | | 25 | 20 | | 26 | 11 | | 27 | 8 | | 28 | 5 | | 29 | 15 | | 30 | 15 | | 31 | 7 | | 32 | 20 | | 33 | 23 | | 34 | 17 | | 35 | 8 | | 36 | 8 | | 37 | 10 | | 38 | 10 | | 39 | 3 | | 40 | 3 | | 41 | 20 | | 42 | 6 | | 43 | 7 | | 44 | 11 | | 45 | 8 | | 46 | 7 | | 47 | 5 | | 48 | 16 | | 49 | 9 |
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| 30.21% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.28125 | | totalSentences | 96 | | uniqueOpeners | 27 | |
| 35.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 95 | | matches | | 0 | "Then, a sound like grinding" |
| | ratio | 0.011 | |
| 76.84% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 95 | | matches | | 0 | "She checked the time on" | | 1 | "Her breath came in short," | | 2 | "She was forty-one years old," | | 3 | "She sprinted across the cobblestones," | | 4 | "He looked back, eyes flashing" | | 5 | "She wiped her eyes with" | | 6 | "She had the files." | | 7 | "She knew the rumors." | | 8 | "She approached the entrance." | | 9 | "It was the scent of" | | 10 | "She reached for her pocket," | | 11 | "It had sat in her" | | 12 | "She pulled it out now," | | 13 | "It was the only key" | | 14 | "She needed answers more than" | | 15 | "She pressed the bone against" | | 16 | "She had two minutes before" | | 17 | "She had three minutes before" | | 18 | "She looked down at her" | | 19 | "She thought of the clique" |
| | ratio | 0.358 | |
| 44.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 95 | | matches | | 0 | "The rain did not wash" | | 1 | "Harlow Quinn kicked through a" | | 2 | "She checked the time on" | | 3 | "The hands were precise, ticking" | | 4 | "Quinn adjusted her grip on" | | 5 | "Her breath came in short," | | 6 | "She was forty-one years old," | | 7 | "The ghost of her partner" | | 8 | "Morris had gone missing three" | | 9 | "This time, she told herself," | | 10 | "She sprinted across the cobblestones," | | 11 | "The entrance was boarded up," | | 12 | "The suspect, a tall figure" | | 13 | "He looked back, eyes flashing" | | 14 | "Quinn skidded to a halt," | | 15 | "She wiped her eyes with" | | 16 | "This wasn't just a suspect." | | 17 | "This was the entry point." | | 18 | "She had the files." | | 19 | "She knew the rumors." |
| | ratio | 0.832 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 95 | | matches | | 0 | "Now, she was ten seconds" | | 1 | "To enter, she had to" | | 2 | "If she stepped through, she" | | 3 | "If she stayed, she lost" |
| | ratio | 0.042 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 3 | | matches | | 0 | "She sprinted across the cobblestones, the sound of her boots slapping against wet stone competing with the distant hum of the city." | | 1 | "The wind gusted through the tunnel, carrying a low hum, a vibration that resonated in the bones of her jaw." | | 2 | "She moved into the tunnel, her boots finding purchase on the stone floor that felt older than the city above." |
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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 | |