| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 28 | | adverbTagCount | 1 | | adverbTags | | 0 | "She climbed back [back]" |
| | dialogueSentences | 87 | | tagDensity | 0.322 | | leniency | 0.644 | | rawRatio | 0.036 | | effectiveRatio | 0.023 | |
| 96.19% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1311 | | totalAiIsmAdverbs | 1 | | 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) | |
| 92.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1311 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 71 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 71 | | filterMatches | | | 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 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 7 | | totalWords | 1311 | | ratio | 0.005 | | matches | | 0 | "six years ago" | | 1 | "part-time" | | 2 | "Donoghue v Stevenson" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 26 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 582 | | uniqueNames | 10 | | maxNameDensity | 3.44 | | worstName | "Megan" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Megan" | | discoveredNames | | Rory | 16 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | Price | 1 | | Megan | 20 | | Silas | 4 | | Prague | 1 | | Cardiff | 1 | | July | 1 |
| | persons | | 0 | "Rory" | | 1 | "Price" | | 2 | "Megan" | | 3 | "Silas" |
| | places | | 0 | "Nest" | | 1 | "Prague" | | 2 | "Cardiff" | | 3 | "July" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | 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 | 1311 | | 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 | 77 | | mean | 17.03 | | std | 16.31 | | cv | 0.958 | | sampleLengths | | 0 | 62 | | 1 | 16 | | 2 | 6 | | 3 | 4 | | 4 | 54 | | 5 | 3 | | 6 | 30 | | 7 | 6 | | 8 | 2 | | 9 | 1 | | 10 | 13 | | 11 | 7 | | 12 | 40 | | 13 | 4 | | 14 | 24 | | 15 | 1 | | 16 | 7 | | 17 | 21 | | 18 | 5 | | 19 | 20 | | 20 | 5 | | 21 | 6 | | 22 | 18 | | 23 | 14 | | 24 | 34 | | 25 | 1 | | 26 | 46 | | 27 | 33 | | 28 | 3 | | 29 | 41 | | 30 | 5 | | 31 | 31 | | 32 | 9 | | 33 | 6 | | 34 | 39 | | 35 | 1 | | 36 | 56 | | 37 | 5 | | 38 | 4 | | 39 | 26 | | 40 | 31 | | 41 | 2 | | 42 | 33 | | 43 | 4 | | 44 | 3 | | 45 | 37 | | 46 | 18 | | 47 | 6 | | 48 | 1 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 71 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 103 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 129 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 672 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.03273809523809524 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.002976190476190476 | |
| 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 | 10.16 | | std | 7.99 | | cv | 0.786 | | sampleLengths | | 0 | 23 | | 1 | 20 | | 2 | 12 | | 3 | 7 | | 4 | 9 | | 5 | 7 | | 6 | 6 | | 7 | 4 | | 8 | 4 | | 9 | 19 | | 10 | 14 | | 11 | 17 | | 12 | 3 | | 13 | 7 | | 14 | 23 | | 15 | 3 | | 16 | 3 | | 17 | 2 | | 18 | 1 | | 19 | 5 | | 20 | 8 | | 21 | 7 | | 22 | 9 | | 23 | 16 | | 24 | 14 | | 25 | 1 | | 26 | 4 | | 27 | 11 | | 28 | 7 | | 29 | 6 | | 30 | 1 | | 31 | 7 | | 32 | 7 | | 33 | 14 | | 34 | 5 | | 35 | 16 | | 36 | 4 | | 37 | 5 | | 38 | 6 | | 39 | 9 | | 40 | 5 | | 41 | 4 | | 42 | 4 | | 43 | 10 | | 44 | 19 | | 45 | 15 | | 46 | 1 | | 47 | 17 | | 48 | 29 | | 49 | 6 |
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| 52.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3798449612403101 | | totalSentences | 129 | | uniqueOpeners | 49 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 60 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 16 | | totalSentences | 60 | | matches | | 0 | "His signet ring clicked against" | | 1 | "Her hair was cropped close" | | 2 | "She peeled the label from" | | 3 | "She stood a full inch" | | 4 | "She circled a finger in" | | 5 | "She held up her left" | | 6 | "She climbed back onto the" | | 7 | "She laughed, a short breath" | | 8 | "She turned her wedding band" | | 9 | "Her eyes had reddened along" | | 10 | "She put her palm flat" | | 11 | "She turned her head" | | 12 | "She wiped her cheek with" | | 13 | "Her knees felt unreliable, as" | | 14 | "He set down the glass." | | 15 | "His hazel eyes held hers" |
| | ratio | 0.267 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 57 | | totalSentences | 60 | | matches | | 0 | "Rain drove Rory through the" | | 1 | "The green neon bled through" | | 2 | "Silas stood behind the bar," | | 3 | "His signet ring clicked against" | | 4 | "Rory followed his nod." | | 5 | "A woman sat alone on" | | 6 | "Her hair was cropped close" | | 7 | "She peeled the label from" | | 8 | "Rory stopped walking." | | 9 | "Nobody else peeled a label" | | 10 | "Megan Price had done it" | | 11 | "Megan looked up." | | 12 | "The strip fell." | | 13 | "Megan's mouth twitched" | | 14 | "Megan slid off the stool" | | 15 | "She stood a full inch" | | 16 | "She circled a finger in" | | 17 | "She held up her left" | | 18 | "A thin gold band sat" | | 19 | "Rory set the bag on" |
| | ratio | 0.95 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 60 | | matches | (empty) | | ratio | 0 | |
| 98.21% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 16 | | technicalSentenceCount | 1 | | matches | | 0 | "Silas stood behind the bar, polishing a tumbler that was already clean." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 28 | | uselessAdditionCount | 1 | | matches | | 0 | "Silas said, not looking up" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 3 | | fancyTags | | 0 | "She laughed (laugh)" | | 1 | "Megan pressed (press)" | | 2 | "Megan sniffed (sniff)" |
| | dialogueSentences | 87 | | tagDensity | 0.138 | | leniency | 0.276 | | rawRatio | 0.25 | | effectiveRatio | 0.069 | |