| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 1 | | adverbTags | | 0 | "She turned back [back]" |
| | dialogueSentences | 59 | | tagDensity | 0.356 | | leniency | 0.712 | | rawRatio | 0.048 | | effectiveRatio | 0.034 | |
| 88.99% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1363 | | totalAiIsmAdverbs | 3 | | 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) | |
| 66.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1363 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "fractured" | | 1 | "scanned" | | 2 | "traced" | | 3 | "weight" | | 4 | "perfect" | | 5 | "reminder" | | 6 | "navigating" | | 7 | "treacherous" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 73 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 73 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 111 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 43 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1356 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 708 | | uniqueNames | 11 | | maxNameDensity | 1.69 | | worstName | "Silas" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Silas" | | discoveredNames | | Soho | 1 | | Raven | 2 | | Nest | 2 | | Blackwood | 1 | | Prague | 1 | | Golden | 1 | | Empress | 1 | | Silas | 12 | | Aurora | 9 | | British | 1 | | Isles | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Blackwood" | | 3 | "Silas" | | 4 | "Aurora" | | 5 | "Isles" |
| | places | | | globalScore | 0.653 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | 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 | 1356 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 111 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 47 | | mean | 28.85 | | std | 21.23 | | cv | 0.736 | | sampleLengths | | 0 | 108 | | 1 | 81 | | 2 | 39 | | 3 | 24 | | 4 | 13 | | 5 | 22 | | 6 | 19 | | 7 | 32 | | 8 | 3 | | 9 | 27 | | 10 | 20 | | 11 | 17 | | 12 | 34 | | 13 | 27 | | 14 | 24 | | 15 | 17 | | 16 | 4 | | 17 | 8 | | 18 | 50 | | 19 | 15 | | 20 | 5 | | 21 | 3 | | 22 | 36 | | 23 | 11 | | 24 | 34 | | 25 | 35 | | 26 | 11 | | 27 | 38 | | 28 | 35 | | 29 | 25 | | 30 | 6 | | 31 | 21 | | 32 | 13 | | 33 | 52 | | 34 | 52 | | 35 | 49 | | 36 | 51 | | 37 | 9 | | 38 | 27 | | 39 | 26 | | 40 | 38 | | 41 | 6 | | 42 | 31 | | 43 | 54 | | 44 | 13 | | 45 | 18 | | 46 | 73 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 73 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 116 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 111 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 712 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 13 | | adverbRatio | 0.018258426966292134 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.008426966292134831 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 111 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 111 | | mean | 12.22 | | std | 7.99 | | cv | 0.654 | | sampleLengths | | 0 | 22 | | 1 | 11 | | 2 | 17 | | 3 | 13 | | 4 | 15 | | 5 | 14 | | 6 | 16 | | 7 | 15 | | 8 | 18 | | 9 | 12 | | 10 | 19 | | 11 | 17 | | 12 | 2 | | 13 | 6 | | 14 | 21 | | 15 | 10 | | 16 | 3 | | 17 | 6 | | 18 | 3 | | 19 | 12 | | 20 | 3 | | 21 | 10 | | 22 | 8 | | 23 | 6 | | 24 | 8 | | 25 | 15 | | 26 | 4 | | 27 | 23 | | 28 | 9 | | 29 | 3 | | 30 | 20 | | 31 | 7 | | 32 | 6 | | 33 | 14 | | 34 | 17 | | 35 | 14 | | 36 | 6 | | 37 | 14 | | 38 | 19 | | 39 | 8 | | 40 | 24 | | 41 | 10 | | 42 | 7 | | 43 | 4 | | 44 | 8 | | 45 | 4 | | 46 | 16 | | 47 | 24 | | 48 | 6 | | 49 | 7 |
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| 76.58% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4864864864864865 | | totalSentences | 111 | | uniqueOpeners | 54 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 65 | | matches | (empty) | | ratio | 0 | |
| 60.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 65 | | matches | | 0 | "She wiped her boots on" | | 1 | "She spotted him in the" | | 2 | "His grey-streaked auburn hair caught" | | 3 | "He nursed a glass of" | | 4 | "She approached the table, her" | | 5 | "His hazel eyes widened, then" | | 6 | "He didn't stand." | | 7 | "She slid into the booth" | | 8 | "He signaled the bartender with" | | 9 | "She gestured to the delivery" | | 10 | "She leaned forward, resting her" | | 11 | "He took a sip of" | | 12 | "She tapped the delivery bag" | | 13 | "She gestured to his leg" | | 14 | "He swirled the liquid in" | | 15 | "She stilled her hand." | | 16 | "He took another drink, finishing" | | 17 | "Her hand shook slightly." | | 18 | "She set the glass down" | | 19 | "She turned back to him" |
| | ratio | 0.4 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 60 | | totalSentences | 65 | | matches | | 0 | "The green neon sign above" | | 1 | "Rain slicked the cobblestones, reflecting" | | 2 | "Aurora pushed through the heavy" | | 3 | "The scent of stale beer," | | 4 | "She wiped her boots on" | | 5 | "Maps covered every inch of" | | 6 | "She spotted him in the" | | 7 | "Silas Blackwood sat with his" | | 8 | "His grey-streaked auburn hair caught" | | 9 | "He nursed a glass of" | | 10 | "A silver signet ring glinted" | | 11 | "The last time she saw" | | 12 | "She approached the table, her" | | 13 | "Silas looked up." | | 14 | "His hazel eyes widened, then" | | 15 | "He didn't stand." | | 16 | "The slight limp in his" | | 17 | "The name tasted strange on" | | 18 | "She slid into the booth" | | 19 | "The leather creaked under her" |
| | ratio | 0.923 | |
| 76.92% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 65 | | matches | | | ratio | 0.015 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 28 | | technicalSentenceCount | 1 | | matches | | 0 | "Silas smiled, a genuine expression that cracked the weathered map of his face." |
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| 77.38% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 2 | | matches | | 0 | "She gestured, the logo of the Golden Empress restaurant faded but visible" | | 1 | "Silas leaned, the wood of the booth groaning" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 59 | | tagDensity | 0.068 | | leniency | 0.136 | | rawRatio | 0 | | effectiveRatio | 0 | |