| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 91.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1210 | | totalAiIsmAdverbs | 2 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 13.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1210 | | totalAiIsms | 21 | | found | | | highlights | | 0 | "electric" | | 1 | "fractured" | | 2 | "scanned" | | 3 | "rhythmic" | | 4 | "weight" | | 5 | "unspoken" | | 6 | "reminder" | | 7 | "unreadable" | | 8 | "sentinel" | | 9 | "crystal" | | 10 | "tension" | | 11 | "imposing" | | 12 | "sanctuary" | | 13 | "silence" | | 14 | "glinting" |
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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 | 118 | | matches | (empty) | |
| 94.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 118 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 118 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1210 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 7 | | matches | | 0 | "What happened, Rory asked." | | 1 | "Rory, Silas said." | | 2 | "You look good, Rory said." | | 3 | "I look tired, Eva corrected." | | 4 | "I should go, Eva said." | | 5 | "Wait, Rory said." | | 6 | "That was enough, Rory said." |
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| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 1210 | | uniqueNames | 16 | | maxNameDensity | 1.4 | | worstName | "Rory" | | maxWindowNameDensity | 3 | | worstWindowName | "Rory" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 3 | | London | 3 | | Soho | 2 | | Golden | 1 | | Empress | 1 | | Prague | 1 | | Rory | 17 | | Cardiff | 5 | | Evan | 1 | | Silas | 9 | | Eva | 15 | | Pre-Law | 1 | | Spymaster | 1 | | You | 5 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Rory" | | 4 | "Evan" | | 5 | "Silas" | | 6 | "Eva" | | 7 | "You" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Prague" | | 3 | "Cardiff" |
| | globalScore | 0.798 | | windowScore | 0.667 | |
| 18.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 76 | | glossingSentenceCount | 4 | | matches | | 0 | "buzz that seemed to vibrate in Aurora's teeth" | | 1 | "looked like she had come to bury herself" | | 2 | "looked like she had survived without grow" | | 3 | "felt like a closed room" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.826 | | wordCount | 1210 | | matches | | 0 | "not for customers, but for a silhouette she had spent three years trying to forget" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 35.59 | | std | 27.43 | | cv | 0.771 | | sampleLengths | | 0 | 111 | | 1 | 83 | | 2 | 46 | | 3 | 53 | | 4 | 1 | | 5 | 87 | | 6 | 19 | | 7 | 25 | | 8 | 7 | | 9 | 76 | | 10 | 45 | | 11 | 53 | | 12 | 12 | | 13 | 32 | | 14 | 5 | | 15 | 13 | | 16 | 52 | | 17 | 50 | | 18 | 7 | | 19 | 9 | | 20 | 36 | | 21 | 34 | | 22 | 16 | | 23 | 51 | | 24 | 24 | | 25 | 48 | | 26 | 16 | | 27 | 5 | | 28 | 34 | | 29 | 41 | | 30 | 4 | | 31 | 32 | | 32 | 4 | | 33 | 79 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 118 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 219 | | matches | | 0 | "was still wearing" | | 1 | "wasn't writing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 118 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1213 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.025556471558120363 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004122011541632316 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 118 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 118 | | mean | 10.25 | | std | 7.21 | | cv | 0.703 | | sampleLengths | | 0 | 24 | | 1 | 23 | | 2 | 22 | | 3 | 19 | | 4 | 23 | | 5 | 20 | | 6 | 19 | | 7 | 19 | | 8 | 25 | | 9 | 10 | | 10 | 20 | | 11 | 5 | | 12 | 11 | | 13 | 1 | | 14 | 5 | | 15 | 15 | | 16 | 22 | | 17 | 10 | | 18 | 1 | | 19 | 6 | | 20 | 37 | | 21 | 23 | | 22 | 21 | | 23 | 19 | | 24 | 8 | | 25 | 3 | | 26 | 8 | | 27 | 6 | | 28 | 2 | | 29 | 2 | | 30 | 3 | | 31 | 24 | | 32 | 24 | | 33 | 13 | | 34 | 7 | | 35 | 8 | | 36 | 12 | | 37 | 25 | | 38 | 5 | | 39 | 3 | | 40 | 12 | | 41 | 20 | | 42 | 21 | | 43 | 4 | | 44 | 5 | | 45 | 3 | | 46 | 7 | | 47 | 6 | | 48 | 9 | | 49 | 10 |
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| 36.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.23728813559322035 | | totalSentences | 118 | | uniqueOpeners | 28 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 108 | | matches | | 0 | "Then the phone calls stopped." | | 1 | "Just like you." |
| | ratio | 0.019 | |
| 16.30% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 55 | | totalSentences | 108 | | matches | | 0 | "It was late past closing" | | 1 | "She was still wearing the" | | 2 | "Her bright blue eyes scanned" | | 3 | "He didn't look up as" | | 4 | "His silver signet ring glinted" | | 5 | "His left leg gave a" | | 6 | "She walked toward the back," | | 7 | "It was Rory's name now." | | 8 | "She kept it in Cardiff," | | 9 | "You're here, Rory said, her" | | 10 | "I told you to come," | | 11 | "She didn't smile." | | 12 | "She took a long sip" | | 13 | "I told you to call" | | 14 | "You didn't answer." | | 15 | "She had survived the abusive" | | 16 | "She looked like she had" | | 17 | "They had stood together in" | | 18 | "He was a Spymaster in" | | 19 | "He tapped a finger on" |
| | ratio | 0.509 | |
| 20.19% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 95 | | totalSentences | 108 | | matches | | 0 | "The green neon sign above" | | 1 | "It was late past closing" | | 2 | "Aurora stood just inside the" | | 3 | "She was still wearing the" | | 4 | "Her bright blue eyes scanned" | | 5 | "Silas sat at the far" | | 6 | "He didn't look up as" | | 7 | "His silver signet ring glinted" | | 8 | "His left leg gave a" | | 9 | "Aurora tightened her grip on" | | 10 | "She walked toward the back," | | 11 | "The wood creaked under weight." | | 12 | "A woman turned, a glass" | | 13 | "It was Rory's name now." | | 14 | "She kept it in Cardiff," | | 15 | "Here, in the soot-stained light" | | 16 | "The change was immediate and" | | 17 | "This Eva was thinner, her" | | 18 | "The woman who had fled" | | 19 | "You're here, Rory said, her" |
| | ratio | 0.88 | |
| 46.30% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 108 | | matches | | 0 | "Now, they were just characters" |
| | ratio | 0.009 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 3 | | matches | | 0 | "The green neon sign above the entrance of The Raven's Nest hummed with a low, electric buzz that seemed to vibrate in Aurora's teeth." | | 1 | "Here, in the soot-stained light of Silas's bar, she was just the girl who delivered food and lived above the owner's head." | | 2 | "He was a Spymaster in retirement, a man who knew better than to interrupt a conversation that was already broken." |
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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 | |