| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 42 | | tagDensity | 0.405 | | leniency | 0.81 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1112 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 68.53% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1112 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "shattered" | | 1 | "weight" | | 2 | "measured" | | 3 | "echoing" | | 4 | "lilt" | | 5 | "flickered" |
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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 | 60 | | matches | (empty) | |
| 95.24% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 60 | | filterMatches | (empty) | | hedgeMatches | | |
| 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 | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1112 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 18.25% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 759 | | uniqueNames | 16 | | maxNameDensity | 2.64 | | worstName | "Rory" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Rory" | | discoveredNames | | Rory | 20 | | Cardiff | 1 | | Raven | 1 | | Nest | 1 | | Silas | 5 | | Eva | 14 | | Prague | 1 | | Berlin | 1 | | Yu-Fei | 2 | | Cheung | 1 | | Golden | 1 | | Empress | 1 | | London | 1 | | Welsh | 1 | | Chancery | 1 | | Lane | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Silas" | | 4 | "Eva" | | 5 | "Yu-Fei" | | 6 | "Cheung" |
| | places | | 0 | "Cardiff" | | 1 | "Prague" | | 2 | "Berlin" | | 3 | "London" | | 4 | "Chancery" | | 5 | "Lane" |
| | globalScore | 0.182 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | 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 | 1112 | | 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 | 50 | | mean | 22.24 | | std | 19.2 | | cv | 0.863 | | sampleLengths | | 0 | 15 | | 1 | 54 | | 2 | 28 | | 3 | 20 | | 4 | 1 | | 5 | 14 | | 6 | 10 | | 7 | 75 | | 8 | 10 | | 9 | 17 | | 10 | 38 | | 11 | 4 | | 12 | 16 | | 13 | 2 | | 14 | 7 | | 15 | 4 | | 16 | 47 | | 17 | 5 | | 18 | 51 | | 19 | 4 | | 20 | 9 | | 21 | 16 | | 22 | 49 | | 23 | 16 | | 24 | 12 | | 25 | 59 | | 26 | 4 | | 27 | 18 | | 28 | 31 | | 29 | 8 | | 30 | 61 | | 31 | 12 | | 32 | 24 | | 33 | 2 | | 34 | 40 | | 35 | 36 | | 36 | 5 | | 37 | 25 | | 38 | 3 | | 39 | 40 | | 40 | 4 | | 41 | 5 | | 42 | 23 | | 43 | 4 | | 44 | 62 | | 45 | 23 | | 46 | 6 | | 47 | 40 | | 48 | 14 | | 49 | 39 |
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| 99.42% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 60 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 115 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 85 | | ratio | 0.012 | | matches | | 0 | "The bright blue in Rory's eyes had lost the softness of their university days; the roundness in her cheeks had hollowed into sharp planes, worn thin by late deliveries for Yu-Fei Cheung and long nights listening to Silas cataloguing ghosts from dead wars." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 767 | | adjectiveStacks | 1 | | stackExamples | | 0 | "thick against pale skin." |
| | adverbCount | 20 | | adverbRatio | 0.02607561929595828 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.005215123859191656 | |
| 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 | 13.08 | | std | 9.04 | | cv | 0.691 | | sampleLengths | | 0 | 15 | | 1 | 8 | | 2 | 16 | | 3 | 30 | | 4 | 4 | | 5 | 9 | | 6 | 15 | | 7 | 20 | | 8 | 1 | | 9 | 14 | | 10 | 10 | | 11 | 8 | | 12 | 23 | | 13 | 20 | | 14 | 24 | | 15 | 5 | | 16 | 5 | | 17 | 17 | | 18 | 4 | | 19 | 17 | | 20 | 17 | | 21 | 4 | | 22 | 12 | | 23 | 4 | | 24 | 2 | | 25 | 7 | | 26 | 4 | | 27 | 12 | | 28 | 35 | | 29 | 5 | | 30 | 18 | | 31 | 10 | | 32 | 23 | | 33 | 4 | | 34 | 9 | | 35 | 12 | | 36 | 4 | | 37 | 13 | | 38 | 28 | | 39 | 8 | | 40 | 8 | | 41 | 8 | | 42 | 12 | | 43 | 26 | | 44 | 33 | | 45 | 4 | | 46 | 18 | | 47 | 5 | | 48 | 26 | | 49 | 8 |
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| 58.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.4117647058823529 | | totalSentences | 85 | | uniqueOpeners | 35 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 50 | | matches | | 0 | "His silver signet ring rapped" | | 1 | "He rose, his hazel eyes" | | 2 | "His auburn beard, threaded with" | | 3 | "He reached under the bar," | | 4 | "He vanished behind the bookshelf" | | 5 | "She approached the counter with" | | 6 | "She kept her gloves on," | | 7 | "She glanced around the room," | | 8 | "Her lips tightened into a" | | 9 | "She raised her left hand," | | 10 | "She slid it across the" | | 11 | "Her throat worked." | | 12 | "She pressed her thumb down" |
| | ratio | 0.26 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 48 | | totalSentences | 50 | | matches | | 0 | "The pint glass shattered cleanly" | | 1 | "Silas did not look up" | | 2 | "His silver signet ring rapped" | | 3 | "Rory ignored him, ignoring too" | | 4 | "Italian leather boots that had" | | 5 | "Hair cut to a razor-sharp" | | 6 | "Eva stood on the brass" | | 7 | "Rory dropped the broken shards" | | 8 | "Silas closed his ledger with" | | 9 | "He rose, his hazel eyes" | | 10 | "His auburn beard, threaded with" | | 11 | "He reached under the bar," | | 12 | "He vanished behind the bookshelf" | | 13 | "Eva did not sit." | | 14 | "She approached the counter with" | | 15 | "The room smelled of dried" | | 16 | "Rory wrapped the cloth tight" | | 17 | "The name landed like a" | | 18 | "Rory picked up a dry" | | 19 | "Eva laid her leather handbag" |
| | ratio | 0.96 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 68.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 29 | | technicalSentenceCount | 3 | | matches | | 0 | "Rory ignored him, ignoring too the prickle of blood welling along her palm, her eyes fixed on the woman who had just crossed the threshold beneath the green neo…" | | 1 | "Hair cut to a razor-sharp bob that caught the amber glow of the wall sconces." | | 2 | "Rory picked up a dry rag with her good hand and began scouring a circle into the wood, rubbing over a knot that had been polished smooth thirty years before eit…" |
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| 66.18% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 2 | | matches | | 0 | "Eva leaned, both hands bracing against the lip of the bar" | | 1 | "Eva reached out, her fingers catching Rory's wrist, skin burning against skin" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 42 | | tagDensity | 0.214 | | leniency | 0.429 | | rawRatio | 0.111 | | effectiveRatio | 0.048 | |