| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.294 | | leniency | 0.588 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1332 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "really" | | 1 | "slowly" | | 2 | "suddenly" |
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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) | |
| 58.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1332 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "lilt" | | 1 | "weight" | | 2 | "pulse" | | 3 | "quickened" | | 4 | "charged" | | 5 | "flickered" | | 6 | "silence" | | 7 | "disrupt" | | 8 | "unspoken" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
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| | highlights | | 0 | "eyes narrowed" | | 1 | "hung in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 116 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 116 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 151 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 60 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1332 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 61 | | wordCount | 788 | | uniqueNames | 11 | | maxNameDensity | 3.3 | | worstName | "Rory" | | maxWindowNameDensity | 6 | | worstWindowName | "Rory" | | discoveredNames | | Aurora | 1 | | Cardiff | 3 | | London | 2 | | Welsh | 1 | | Raven | 1 | | Nest | 1 | | Maeve | 22 | | Rory | 26 | | Ellis | 1 | | Silas | 1 | | Tommy | 2 |
| | persons | | 0 | "Raven" | | 1 | "Maeve" | | 2 | "Rory" | | 3 | "Ellis" | | 4 | "Silas" | | 5 | "Tommy" |
| | places | | | globalScore | 0 | | windowScore | 0 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like skin sitting easier than the" |
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| 49.85% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.502 | | wordCount | 1332 | | matches | | 0 | "not the polished barrister but the girl who’d once shared16 a bottle of cheap wine in a Car" | | 1 | "not just the years but the silence" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 151 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 27.75 | | std | 20.88 | | cv | 0.752 | | sampleLengths | | 0 | 13 | | 1 | 90 | | 2 | 6 | | 3 | 46 | | 4 | 33 | | 5 | 61 | | 6 | 10 | | 7 | 1 | | 8 | 4 | | 9 | 31 | | 10 | 12 | | 11 | 15 | | 12 | 68 | | 13 | 14 | | 14 | 31 | | 15 | 3 | | 16 | 22 | | 17 | 31 | | 18 | 60 | | 19 | 8 | | 20 | 12 | | 21 | 30 | | 22 | 38 | | 23 | 25 | | 24 | 23 | | 25 | 29 | | 26 | 31 | | 27 | 2 | | 28 | 81 | | 29 | 31 | | 30 | 74 | | 31 | 48 | | 32 | 12 | | 33 | 5 | | 34 | 18 | | 35 | 37 | | 36 | 11 | | 37 | 26 | | 38 | 16 | | 39 | 6 | | 40 | 39 | | 41 | 23 | | 42 | 19 | | 43 | 42 | | 44 | 15 | | 45 | 26 | | 46 | 33 | | 47 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 116 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 126 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 151 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 795 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 17 | | adverbRatio | 0.021383647798742137 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.006289308176100629 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 151 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 151 | | mean | 8.82 | | std | 9.73 | | cv | 1.104 | | sampleLengths | | 0 | 5 | | 1 | 2 | | 2 | 1 | | 3 | 5 | | 4 | 17 | | 5 | 18 | | 6 | 20 | | 7 | 22 | | 8 | 10 | | 9 | 3 | | 10 | 6 | | 11 | 10 | | 12 | 9 | | 13 | 8 | | 14 | 19 | | 15 | 5 | | 16 | 28 | | 17 | 5 | | 18 | 16 | | 19 | 35 | | 20 | 5 | | 21 | 10 | | 22 | 1 | | 23 | 4 | | 24 | 13 | | 25 | 18 | | 26 | 6 | | 27 | 6 | | 28 | 15 | | 29 | 6 | | 30 | 7 | | 31 | 20 | | 32 | 35 | | 33 | 8 | | 34 | 6 | | 35 | 3 | | 36 | 19 | | 37 | 9 | | 38 | 3 | | 39 | 13 | | 40 | 6 | | 41 | 3 | | 42 | 3 | | 43 | 2 | | 44 | 2 | | 45 | 20 | | 46 | 4 | | 47 | 60 | | 48 | 8 | | 49 | 7 |
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| 60.71% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.423841059602649 | | totalSentences | 151 | | uniqueOpeners | 64 | |
| 40.16% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 83 | | matches | | 0 | "Instead of running, she16 smiled." |
| | ratio | 0.012 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 83 | | matches | | 0 | "Her hair, once a messy" | | 1 | "Her voice sounded foreign to" | | 2 | "Her free hand gestured at" | | 3 | "She glanced over her shoulder" | | 4 | "Her voice cracked, the first" | | 5 | "She pulled an older model" | | 6 | "She broke off, and her16" | | 7 | "She’d already clocked the16." | | 8 | "She’d disarmed men bigger than" | | 9 | "They weren’t just16 hunting Tommy" | | 10 | "They were hunting everyone who16." | | 11 | "Her old friend,16.16.16.16." |
| | ratio | 0.145 | |
| 50.36% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 83 | | matches | | 0 | "The bar rag stopped mid-swipe." | | 1 | "Years since she’d heard it." | | 2 | "Rory looked up from the" | | 3 | "The green neon from the" | | 4 | "Maeve O’Sullivan stood at the" | | 5 | "Her hair, once a messy" | | 6 | "The name landed like a" | | 7 | "Rory16 didn’t flinch, but something" | | 8 | "She16 hadn’t been Aurora to" | | 9 | "Her voice sounded foreign to" | | 10 | "Maeve set the glass down." | | 11 | "The base clicked against the" | | 12 | "Her free hand gestured at" | | 13 | "A laugh escaped Maeve, a" | | 14 | "Rory wrung out the rag," | | 15 | "The words hung in the" | | 16 | "Rory’s left hand stilled on" | | 17 | "The small crescent-shaped scar on" | | 18 | "She16 looked at Maeve,16 and" | | 19 | "Maeve’s jaw tightened." |
| | ratio | 0.819 | |
| 60.24% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 83 | | matches | | 0 | "Now he stood,16 newspaper16 abandoned," |
| | ratio | 0.012 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 2 | | matches | | 0 | "A laugh escaped Maeve, a dry, brittle thing that didn’t reach her eyes." | | 1 | "She16 looked at Maeve,16 and for a moment she saw not the polished barrister but the girl who’d once shared16 a bottle of cheap wine in a Cardiff park,16 sweari…" |
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| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.137 | | leniency | 0.275 | | rawRatio | 0 | | effectiveRatio | 0 | |