| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 63 | | tagDensity | 0.27 | | leniency | 0.54 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1290 | | 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) | |
| 53.49% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1290 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "measured" | | 1 | "scanned" | | 2 | "silence" | | 3 | "gleaming" | | 4 | "furrowing" | | 5 | "echoes" | | 6 | "warmth" | | 7 | "tension" | | 8 | "shattered" | | 9 | "unspoken" |
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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 | 86 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 86 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 129 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1283 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 26 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 818 | | uniqueNames | 11 | | maxNameDensity | 1.22 | | worstName | "Andrew" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Andrew" | | discoveredNames | | London | 2 | | Stone | 1 | | Cambridge | 2 | | Raven | 1 | | Nest | 1 | | Waiting | 1 | | Andrew | 10 | | Aurora | 5 | | Cardiff | 2 | | Silas | 6 | | Rory | 6 |
| | persons | | 0 | "Stone" | | 1 | "Raven" | | 2 | "Andrew" | | 3 | "Aurora" | | 4 | "Silas" | | 5 | "Rory" |
| | places | | 0 | "London" | | 1 | "Cambridge" | | 2 | "Cardiff" |
| | globalScore | 0.889 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | 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 | 1283 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 129 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 73 | | mean | 17.58 | | std | 11.73 | | cv | 0.668 | | sampleLengths | | 0 | 8 | | 1 | 25 | | 2 | 14 | | 3 | 58 | | 4 | 13 | | 5 | 34 | | 6 | 31 | | 7 | 35 | | 8 | 17 | | 9 | 27 | | 10 | 11 | | 11 | 6 | | 12 | 13 | | 13 | 3 | | 14 | 17 | | 15 | 9 | | 16 | 3 | | 17 | 17 | | 18 | 19 | | 19 | 7 | | 20 | 29 | | 21 | 17 | | 22 | 15 | | 23 | 12 | | 24 | 17 | | 25 | 10 | | 26 | 13 | | 27 | 33 | | 28 | 20 | | 29 | 6 | | 30 | 6 | | 31 | 10 | | 32 | 6 | | 33 | 14 | | 34 | 6 | | 35 | 2 | | 36 | 13 | | 37 | 25 | | 38 | 6 | | 39 | 15 | | 40 | 11 | | 41 | 23 | | 42 | 3 | | 43 | 27 | | 44 | 18 | | 45 | 35 | | 46 | 44 | | 47 | 4 | | 48 | 10 | | 49 | 28 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 86 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 164 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 129 | | ratio | 0.008 | | matches | | 0 | "Just studied her hand as she sliced lemons—scar gleaming from fluorescent green to unnatural blue." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 824 | | adjectiveStacks | 1 | | stackExamples | | 0 | "sudden under cheap bulbs." |
| | adverbCount | 23 | | adverbRatio | 0.027912621359223302 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0012135922330097086 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 129 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 129 | | mean | 9.95 | | std | 6.73 | | cv | 0.677 | | sampleLengths | | 0 | 8 | | 1 | 25 | | 2 | 14 | | 3 | 19 | | 4 | 8 | | 5 | 31 | | 6 | 8 | | 7 | 5 | | 8 | 20 | | 9 | 14 | | 10 | 13 | | 11 | 18 | | 12 | 13 | | 13 | 12 | | 14 | 10 | | 15 | 6 | | 16 | 11 | | 17 | 11 | | 18 | 16 | | 19 | 7 | | 20 | 4 | | 21 | 6 | | 22 | 8 | | 23 | 5 | | 24 | 3 | | 25 | 17 | | 26 | 9 | | 27 | 3 | | 28 | 16 | | 29 | 1 | | 30 | 10 | | 31 | 9 | | 32 | 3 | | 33 | 4 | | 34 | 18 | | 35 | 11 | | 36 | 10 | | 37 | 7 | | 38 | 13 | | 39 | 2 | | 40 | 2 | | 41 | 10 | | 42 | 5 | | 43 | 12 | | 44 | 5 | | 45 | 5 | | 46 | 13 | | 47 | 3 | | 48 | 17 | | 49 | 13 |
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| 64.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.40310077519379844 | | totalSentences | 129 | | uniqueOpeners | 52 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 75 | | matches | | 0 | "Almost a joke" | | 1 | "Just studied her hand as" | | 2 | "Just sipped, swallowed, set his" |
| | ratio | 0.04 | |
| 1.33% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 75 | | matches | | 0 | "His old Cambridge scarf knotted" | | 1 | "She measured him in the" | | 2 | "He shot her an easy," | | 3 | "She kept her tone light," | | 4 | "He scanned the maps on" | | 5 | "She flipped the bar towel" | | 6 | "He winced a fraction, like" | | 7 | "His smile faded, lips pressed" | | 8 | "She tilted the glass, watched" | | 9 | "He lifted his tumbler to" | | 10 | "He sipped, then set the" | | 11 | "he said, fingers worrying a" | | 12 | "She set the bar rag" | | 13 | "His face reddened." | | 14 | "she glanced left, caught her" | | 15 | "She tasted the word, as" | | 16 | "He didn’t drink." | | 17 | "She glanced up, caught his" | | 18 | "His voice was softer." | | 19 | "She sliced another lemon, brow" |
| | ratio | 0.547 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 69 | | totalSentences | 75 | | matches | | 0 | "Aurora didn’t look up from" | | 1 | "Andrew Stone, the kind of" | | 2 | "His old Cambridge scarf knotted" | | 3 | "She measured him in the" | | 4 | "He shot her an easy," | | 5 | "She kept her tone light," | | 6 | "He scanned the maps on" | | 7 | "She flipped the bar towel" | | 8 | "He winced a fraction, like" | | 9 | "His smile faded, lips pressed" | | 10 | "She tilted the glass, watched" | | 11 | "He lifted his tumbler to" | | 12 | "He sipped, then set the" | | 13 | "Pause stretched, the clink and" | | 14 | "he said, fingers worrying a" | | 15 | "She set the bar rag" | | 16 | "His face reddened." | | 17 | "she glanced left, caught her" | | 18 | "Andrew trailed his thumb along" | | 19 | "She tasted the word, as" |
| | ratio | 0.92 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 75 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 1 | | matches | | 0 | "Waiting for the punchline life played on boys who wore old-school ties like talismans." |
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| 66.18% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 2 | | matches | | 0 | "She tasted, as if it’d gone off since last use" | | 1 | "Silas said, voice low" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 63 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.111 | | effectiveRatio | 0.032 | |