| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 23 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 80 | | tagDensity | 0.288 | | leniency | 0.575 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1673 | | 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) | |
| 91.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1673 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "dancing" | | 1 | "familiar" | | 2 | "wavering" |
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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 | 118 | | matches | (empty) | |
| 94.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 118 | | filterMatches | | | hedgeMatches | | 0 | "seemed to" | | 1 | "tried to" | | 2 | "began to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 175 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 59 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1673 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 31 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 90 | | wordCount | 1165 | | uniqueNames | 7 | | maxNameDensity | 3.26 | | worstName | "Eva" | | maxWindowNameDensity | 6 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Eva | 38 | | Rory | 38 | | Silas | 9 | | Cardiff | 2 | | London | 1 |
| | persons | | | places | | 0 | "Raven" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 80 | | 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 | 1673 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 175 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 94 | | mean | 17.8 | | std | 18.15 | | cv | 1.02 | | sampleLengths | | 0 | 53 | | 1 | 16 | | 2 | 5 | | 3 | 5 | | 4 | 57 | | 5 | 11 | | 6 | 53 | | 7 | 8 | | 8 | 1 | | 9 | 54 | | 10 | 40 | | 11 | 4 | | 12 | 3 | | 13 | 18 | | 14 | 3 | | 15 | 1 | | 16 | 20 | | 17 | 6 | | 18 | 66 | | 19 | 8 | | 20 | 14 | | 21 | 12 | | 22 | 6 | | 23 | 18 | | 24 | 38 | | 25 | 25 | | 26 | 7 | | 27 | 31 | | 28 | 8 | | 29 | 9 | | 30 | 71 | | 31 | 7 | | 32 | 11 | | 33 | 2 | | 34 | 9 | | 35 | 19 | | 36 | 7 | | 37 | 3 | | 38 | 6 | | 39 | 4 | | 40 | 75 | | 41 | 16 | | 42 | 6 | | 43 | 11 | | 44 | 5 | | 45 | 2 | | 46 | 17 | | 47 | 30 | | 48 | 39 | | 49 | 6 |
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| 99.32% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 118 | | matches | | 0 | "been disconnected" | | 1 | "been broken" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 215 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 3 | | flaggedSentences | 2 | | totalSentences | 175 | | ratio | 0.011 | | matches | | 0 | "She wanted to say she’d known Eva had been drinking too much, but the thought came with a row of bright, accusing memories: Eva dancing with a bottle in her hand at a Cardiff house party; Eva arriving in London with wine to celebrate Rory’s first room; Eva laughing as she dropped her keys three times outside a taxi." | | 1 | "Eva had always sat down to meals; Rory had simply remembered the parts of her that moved." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1168 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.03767123287671233 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.009417808219178082 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 175 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 175 | | mean | 9.56 | | std | 7.76 | | cv | 0.812 | | sampleLengths | | 0 | 21 | | 1 | 6 | | 2 | 26 | | 3 | 16 | | 4 | 5 | | 5 | 5 | | 6 | 13 | | 7 | 13 | | 8 | 31 | | 9 | 11 | | 10 | 8 | | 11 | 19 | | 12 | 9 | | 13 | 17 | | 14 | 4 | | 15 | 4 | | 16 | 1 | | 17 | 15 | | 18 | 8 | | 19 | 9 | | 20 | 22 | | 21 | 5 | | 22 | 21 | | 23 | 6 | | 24 | 8 | | 25 | 4 | | 26 | 3 | | 27 | 14 | | 28 | 4 | | 29 | 3 | | 30 | 1 | | 31 | 7 | | 32 | 13 | | 33 | 4 | | 34 | 2 | | 35 | 3 | | 36 | 59 | | 37 | 4 | | 38 | 4 | | 39 | 4 | | 40 | 6 | | 41 | 8 | | 42 | 2 | | 43 | 5 | | 44 | 5 | | 45 | 6 | | 46 | 18 | | 47 | 6 | | 48 | 19 | | 49 | 7 |
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| 39.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 19 | | diversityRatio | 0.25142857142857145 | | totalSentences | 175 | | uniqueOpeners | 44 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 99 | | matches | | 0 | "Then someone at the far" | | 1 | "Perhaps that was unfair." | | 2 | "Instead she saw the missed" |
| | ratio | 0.03 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 99 | | matches | | 0 | "She set the bag at" | | 1 | "Her coat lay folded on" | | 2 | "She had imagined this meeting" | | 3 | "She was still a little" | | 4 | "They hugged awkwardly, shoulders touching" | | 5 | "She wanted to say she’d" | | 6 | "He did not look over" | | 7 | "He rarely did when it" | | 8 | "It had come at half" | | 9 | "Her voice was slow and" | | 10 | "She heard the old edge" | | 11 | "He filled the kettle under" | | 12 | "He took the kettle away," | | 13 | "She could still picture Eva" | | 14 | "She put it down." | | 15 | "He ate standing up, studying" | | 16 | "It took an effort." | | 17 | "She folded the damp napkin" | | 18 | "She had wanted a day" | | 19 | "It was strong, just as" |
| | ratio | 0.232 | |
| 25.66% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 99 | | matches | | 0 | "Rory came into the Raven’s" | | 1 | "Silas had forgotten to eat" | | 2 | "She set the bag at" | | 3 | "Silas’s silver signet ring tapped" | | 4 | "Rory had been looking at" | | 5 | "The woman sitting beneath a" | | 6 | "Her coat lay folded on" | | 7 | "A glass of water stood" | | 8 | "The woman smiled uncertainly." | | 9 | "Rory knew her then." | | 10 | "She had imagined this meeting" | | 11 | "None had Silas opening a" | | 12 | "Eva got off her stool." | | 13 | "She was still a little" | | 14 | "They hugged awkwardly, shoulders touching" | | 15 | "Eva’s coat smelled of rain" | | 16 | "Eva glanced toward the ceiling" | | 17 | "Rory looked at the glass" | | 18 | "Eva saw her look and" | | 19 | "Rory hadn’t asked." |
| | ratio | 0.869 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 99 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 1 | | matches | | 0 | "There had been an Eva who could talk their way into the back row of a sold-out cinema, who knew which Cardiff bus drivers would wait if they saw her running." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 23 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 16 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 80 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |