| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 25 | | adverbTagCount | 2 | | adverbTags | | 0 | "Bethan nodded slowly [slowly]" | | 1 | "she said thickly [thickly]" |
| | dialogueSentences | 60 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0.08 | | effectiveRatio | 0.067 | |
| 65.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1587 | | totalAiIsmAdverbs | 11 | | found | | | highlights | | 0 | "very" | | 1 | "quickly" | | 2 | "slowly" | | 3 | "lightly" | | 4 | "slightly" |
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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) | |
| 96.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1587 | | totalAiIsms | 1 | | 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 | 1 | | narrationSentences | 62 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 62 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 97 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 95 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1592 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 55 | | wordCount | 879 | | uniqueNames | 12 | | maxNameDensity | 2.39 | | worstName | "Rory" | | maxWindowNameDensity | 5 | | worstWindowName | "Bethan" | | discoveredNames | | Rory | 21 | | Berwick | 1 | | Street | 1 | | Golden | 1 | | Empress | 1 | | Nest | 1 | | Thursday | 1 | | Silas | 4 | | Pryce | 2 | | Bethan | 20 | | Roath | 1 | | Cardiff | 1 |
| | persons | | 0 | "Rory" | | 1 | "Silas" | | 2 | "Pryce" | | 3 | "Bethan" |
| | places | | 0 | "Berwick" | | 1 | "Street" | | 2 | "Nest" | | 3 | "Roath" | | 4 | "Cardiff" |
| | globalScore | 0.305 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 35 | | 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 | 1592 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 97 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 27.45 | | std | 30.33 | | cv | 1.105 | | sampleLengths | | 0 | 103 | | 1 | 61 | | 2 | 58 | | 3 | 2 | | 4 | 95 | | 5 | 16 | | 6 | 4 | | 7 | 4 | | 8 | 35 | | 9 | 15 | | 10 | 5 | | 11 | 1 | | 12 | 40 | | 13 | 11 | | 14 | 75 | | 15 | 4 | | 16 | 60 | | 17 | 13 | | 18 | 10 | | 19 | 46 | | 20 | 7 | | 21 | 2 | | 22 | 5 | | 23 | 4 | | 24 | 37 | | 25 | 21 | | 26 | 3 | | 27 | 26 | | 28 | 18 | | 29 | 4 | | 30 | 4 | | 31 | 69 | | 32 | 4 | | 33 | 6 | | 34 | 12 | | 35 | 46 | | 36 | 15 | | 37 | 5 | | 38 | 2 | | 39 | 53 | | 40 | 11 | | 41 | 2 | | 42 | 102 | | 43 | 69 | | 44 | 6 | | 45 | 6 | | 46 | 22 | | 47 | 4 | | 48 | 99 | | 49 | 1 |
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| 76.97% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 62 | | matches | | 0 | "been sanded" | | 1 | "being asked" | | 2 | "were bitten" | | 3 | "were tired" | | 4 | "been built" | | 5 | "been called" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 156 | | matches | | 0 | "was doing" | | 1 | "was sitting" |
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| 54.49% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 97 | | ratio | 0.031 | | matches | | 0 | "Silas was at the far end of the bar with a glass and a cloth, and he looked up when she came in, and then he did something he almost never did — he looked past her, then back at her, a flick of the eyes, a small tilt of the head towards the booths on the left." | | 1 | "He didn't hurry — he never hurried, the left knee wouldn't let him, and he'd made the limp into a kind of pace, an unhurriedness he wore like a good coat." | | 2 | "\"I was thinking I don't know what to do with that. Because when I knew you, you were the reason I ever did anything, and now you're the one holding the coats, and I'm — \" she gestured at herself, the wet hair, the bag, the whole configuration \"— I'm the one being interesting at people. And I don't think either of us picked it. I think it just happened while we weren't looking.\"" |
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| 98.07% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 853 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.04220398593200469 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.008206330597889801 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 97 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 97 | | mean | 16.41 | | std | 19.2 | | cv | 1.17 | | sampleLengths | | 0 | 35 | | 1 | 33 | | 2 | 12 | | 3 | 23 | | 4 | 24 | | 5 | 10 | | 6 | 13 | | 7 | 14 | | 8 | 58 | | 9 | 2 | | 10 | 95 | | 11 | 3 | | 12 | 13 | | 13 | 4 | | 14 | 4 | | 15 | 12 | | 16 | 18 | | 17 | 5 | | 18 | 2 | | 19 | 13 | | 20 | 5 | | 21 | 1 | | 22 | 31 | | 23 | 9 | | 24 | 11 | | 25 | 20 | | 26 | 55 | | 27 | 4 | | 28 | 3 | | 29 | 31 | | 30 | 26 | | 31 | 4 | | 32 | 9 | | 33 | 6 | | 34 | 4 | | 35 | 5 | | 36 | 41 | | 37 | 4 | | 38 | 3 | | 39 | 2 | | 40 | 5 | | 41 | 4 | | 42 | 11 | | 43 | 6 | | 44 | 17 | | 45 | 3 | | 46 | 21 | | 47 | 3 | | 48 | 19 | | 49 | 7 |
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| 63.92% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4329896907216495 | | totalSentences | 97 | | uniqueOpeners | 42 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 50 | | matches | | 0 | "Her shift had ended twenty" | | 1 | "She still had the Golden" | | 2 | "It smelled, permanently now, of" | | 3 | "Her face went through several" | | 4 | "She put the thermal bag" | | 5 | "He didn't hurry — he" | | 6 | "He set a glass of" | | 7 | "Her nails were short and" | | 8 | "she gestured at herself, the" | | 9 | "She smiled, and it was" | | 10 | "she said at last" | | 11 | "She recovered it, the way" | | 12 | "she said thickly" |
| | ratio | 0.26 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 50 | | matches | | 0 | "The green neon over the" | | 1 | "Her shift had ended twenty" | | 2 | "She still had the Golden" | | 3 | "It smelled, permanently now, of" | | 4 | "The walls did what they" | | 5 | "Silas was at the far" | | 6 | "Bethan Pryce was sitting in" | | 7 | "Bethan saw her." | | 8 | "Her face went through several" | | 9 | "Bethan's voice had changed too" | | 10 | "The Cardiff had been sanded" | | 11 | "She put the thermal bag" | | 12 | "Bethan laughed, and the laugh" | | 13 | "Bethan nodded slowly, and did" | | 14 | "Silas came over." | | 15 | "He didn't hurry — he" | | 16 | "He set a glass of" | | 17 | "Silas didn't smile, exactly" | | 18 | "Bethan watched him go." | | 19 | "Bethan turned the tonic glass" |
| | ratio | 0.92 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 2 | | matches | | 0 | "Bethan Pryce was sitting in the second booth with her hands around a glass of tonic water, and it took Rory a full three seconds to be sure, because Bethan Pryc…" | | 1 | "Bethan reached across the table, not quickly, and turned Rory's left hand over with two fingers, the way she used to in double chemistry, and there it was: the …" |
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| 65.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 25 | | uselessAdditionCount | 3 | | matches | | 0 | "Bethan laughed, and the laugh was the same, absolutely unchanged, a snort at the front of it, and the sound went through Rory like a hand through a cobweb" | | 1 | "Silas didn't, exactly" | | 2 | "Bethan looked up, and her eyes were tired in a way the haircut and the coat had been built to hide" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 1 | | fancyTags | | 0 | "Bethan laughed (laugh)" |
| | dialogueSentences | 60 | | tagDensity | 0.217 | | leniency | 0.433 | | rawRatio | 0.077 | | effectiveRatio | 0.033 | |