| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 2 | | adverbTags | | 0 | "Meg said quickly [quickly]" | | 1 | "Evan said softly [softly]" |
| | dialogueSentences | 65 | | tagDensity | 0.292 | | leniency | 0.585 | | rawRatio | 0.105 | | effectiveRatio | 0.062 | |
| 84.37% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1599 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slightly" | | 1 | "really" | | 2 | "quickly" | | 3 | "softly" |
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| 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) | |
| 93.75% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1599 | | 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 | 109 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 109 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 154 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 64 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 9 | | markdownWords | 21 | | totalWords | 1599 | | ratio | 0.013 | | matches | | 0 | "your" | | 1 | "Meg" | | 2 | "Careful." | | 3 | "Gone to London" | | 4 | "dad" | | 5 | "Tell her." | | 6 | "me" | | 7 | "sort of" | | 8 | "Nain's. It's for you, when you've learned to behave." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1016 | | uniqueNames | 27 | | maxNameDensity | 0.79 | | worstName | "Silas" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | Silas | 8 | | Laila | 1 | | Leeds | 1 | | Cardiff | 2 | | Friday-night | 1 | | Pryce | 1 | | Raven | 1 | | Nest | 2 | | Meg | 8 | | Womanby | 1 | | Street | 1 | | Rioja | 1 | | Berlin | 1 | | Danzig | 1 | | Constantinople | 1 | | Vienna | 1 | | Paddington | 1 | | Pret | 1 | | Evan | 2 | | Brownie | 1 | | Tomos | 1 | | Year | 1 | | Eleven | 1 | | Missed | 1 | | Roath | 1 | | Christmas | 1 | | Tell | 4 |
| | persons | | 0 | "Silas" | | 1 | "Laila" | | 2 | "Pryce" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Meg" | | 6 | "Evan" |
| | places | | 0 | "Leeds" | | 1 | "Cardiff" | | 2 | "Womanby" | | 3 | "Street" | | 4 | "Berlin" | | 5 | "Vienna" | | 6 | "Paddington" | | 7 | "Pret" | | 8 | "Brownie" | | 9 | "Year" | | 10 | "Roath" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like she owned a label maker" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1599 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 154 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 74 | | mean | 21.61 | | std | 23.14 | | cv | 1.071 | | sampleLengths | | 0 | 19 | | 1 | 5 | | 2 | 51 | | 3 | 18 | | 4 | 7 | | 5 | 12 | | 6 | 60 | | 7 | 31 | | 8 | 1 | | 9 | 19 | | 10 | 31 | | 11 | 9 | | 12 | 19 | | 13 | 64 | | 14 | 8 | | 15 | 3 | | 16 | 2 | | 17 | 60 | | 18 | 3 | | 19 | 3 | | 20 | 29 | | 21 | 89 | | 22 | 5 | | 23 | 5 | | 24 | 35 | | 25 | 15 | | 26 | 3 | | 27 | 8 | | 28 | 13 | | 29 | 23 | | 30 | 7 | | 31 | 3 | | 32 | 26 | | 33 | 36 | | 34 | 4 | | 35 | 59 | | 36 | 2 | | 37 | 2 | | 38 | 1 | | 39 | 86 | | 40 | 56 | | 41 | 6 | | 42 | 2 | | 43 | 2 | | 44 | 5 | | 45 | 49 | | 46 | 29 | | 47 | 10 | | 48 | 9 | | 49 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 109 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 171 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 154 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1022 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.02837573385518591 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007827788649706457 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 154 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 154 | | mean | 10.38 | | std | 10.17 | | cv | 0.98 | | sampleLengths | | 0 | 19 | | 1 | 5 | | 2 | 23 | | 3 | 7 | | 4 | 8 | | 5 | 13 | | 6 | 8 | | 7 | 10 | | 8 | 7 | | 9 | 5 | | 10 | 5 | | 11 | 2 | | 12 | 27 | | 13 | 5 | | 14 | 6 | | 15 | 2 | | 16 | 20 | | 17 | 22 | | 18 | 9 | | 19 | 1 | | 20 | 9 | | 21 | 10 | | 22 | 11 | | 23 | 5 | | 24 | 4 | | 25 | 11 | | 26 | 5 | | 27 | 4 | | 28 | 10 | | 29 | 9 | | 30 | 3 | | 31 | 38 | | 32 | 11 | | 33 | 11 | | 34 | 1 | | 35 | 5 | | 36 | 3 | | 37 | 3 | | 38 | 2 | | 39 | 12 | | 40 | 15 | | 41 | 10 | | 42 | 23 | | 43 | 3 | | 44 | 3 | | 45 | 5 | | 46 | 1 | | 47 | 23 | | 48 | 5 | | 49 | 19 |
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| 54.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 25 | | diversityRatio | 0.42207792207792205 | | totalSentences | 154 | | uniqueOpeners | 65 | |
| 69.44% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 96 | | matches | | 0 | "Then she drained half her" | | 1 | "Somewhere in the kitchen, Silas" |
| | ratio | 0.021 | |
| 11.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 50 | | totalSentences | 96 | | matches | | 0 | "I was halfway through lying" | | 1 | "I had told him I" | | 2 | "I had told him I" | | 3 | "I had told him I" | | 4 | "I kept my smile pinned" | | 5 | "She crossed the room in" | | 6 | "He didn't look at us." | | 7 | "He didn't need to." | | 8 | "He poured without asking and" | | 9 | "I stayed standing." | | 10 | "She turned the stem of" | | 11 | "Her nails were short and" | | 12 | "She used to bite them" | | 13 | "I picked up my wine." | | 14 | "It tasted of cork and" | | 15 | "It was early yet, the" | | 16 | "I'd never asked about it." | | 17 | "She studied me the way" | | 18 | "I looked at my wrist." | | 19 | "She sipped her wine and" |
| | ratio | 0.521 | |
| 27.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 83 | | totalSentences | 96 | | matches | | 0 | "I was halfway through lying" | | 1 | "The man at the bar," | | 2 | "I had told him I" | | 3 | "I had told him I" | | 4 | "I had told him I" | | 5 | "I kept my smile pinned" | | 6 | "Cardiff lived in that voice." | | 7 | "Pontcanna vowels, a Friday-night rasp." | | 8 | "Megan Pryce stood in the" | | 9 | "Chestnut, cut blunt at the" | | 10 | "The Meg I knew had" | | 11 | "This woman looked like she" | | 12 | "She crossed the room in" | | 13 | "He didn't look at us." | | 14 | "He didn't need to." | | 15 | "Silas could read a room" | | 16 | "The accountant cleared his throat." | | 17 | "Silas told him" | | 18 | "The man went." | | 19 | "Silas lifted a bottle of" |
| | ratio | 0.865 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 96 | | matches | (empty) | | ratio | 0 | |
| 79.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 3 | | matches | | 0 | "It tasted of cork and pencil shavings, which meant Silas had given us the good bottle, which meant he thought I'd need it." | | 1 | "It was early yet, the dead hour between the after-work crowd and the ones who came for other reasons." | | 2 | "The old maps on the walls curled at their edges, Danzig, Constantinople, cities with names that didn't exist anymore." |
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| 72.37% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 2 | | matches | | 0 | "She studied, lips moving slightly" | | 1 | "She waved, the bar, the maps" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 65 | | tagDensity | 0.123 | | leniency | 0.246 | | rawRatio | 0.125 | | effectiveRatio | 0.031 | |