| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said quietly [quietly]" |
| | dialogueSentences | 56 | | tagDensity | 0.339 | | leniency | 0.679 | | rawRatio | 0.053 | | effectiveRatio | 0.036 | |
| 96.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1304 | | 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) | |
| 76.99% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1304 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "eyebrow" | | 1 | "silence" | | 2 | "measured" |
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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 | 47 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 47 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 84 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 116 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1298 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 25 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 539 | | uniqueNames | 9 | | maxNameDensity | 1.11 | | worstName | "Gethin" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Gethin" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Aurora | 5 | | Carter | 1 | | Cardiff | 2 | | Gethin | 6 | | Silas | 4 | | Yu-Fei | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Carter" | | 4 | "Gethin" | | 5 | "Silas" | | 6 | "Yu-Fei" |
| | places | | | globalScore | 0.943 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 29 | | 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 | 1298 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 24.49 | | std | 26.68 | | cv | 1.089 | | sampleLengths | | 0 | 36 | | 1 | 40 | | 2 | 1 | | 3 | 32 | | 4 | 62 | | 5 | 17 | | 6 | 25 | | 7 | 7 | | 8 | 6 | | 9 | 19 | | 10 | 23 | | 11 | 6 | | 12 | 3 | | 13 | 21 | | 14 | 8 | | 15 | 2 | | 16 | 18 | | 17 | 30 | | 18 | 14 | | 19 | 1 | | 20 | 66 | | 21 | 3 | | 22 | 8 | | 23 | 10 | | 24 | 63 | | 25 | 2 | | 26 | 25 | | 27 | 35 | | 28 | 4 | | 29 | 4 | | 30 | 5 | | 31 | 121 | | 32 | 9 | | 33 | 3 | | 34 | 39 | | 35 | 13 | | 36 | 2 | | 37 | 26 | | 38 | 59 | | 39 | 8 | | 40 | 2 | | 41 | 60 | | 42 | 30 | | 43 | 15 | | 44 | 21 | | 45 | 80 | | 46 | 13 | | 47 | 112 | | 48 | 10 | | 49 | 8 |
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| 90.33% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 47 | | matches | | 0 | "being shifted" | | 1 | "been nineteen" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 103 | | matches | | |
| 40.82% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 84 | | ratio | 0.036 | | matches | | 0 | "She noticed the umbrella first—battered, one rib broken, held out to drip on the mat with the careful deference of someone raised to mind other people's floors." | | 1 | "For a moment nothing moved in his expression; then something rearranged itself behind his eyes, slow as furniture being shifted in a dark room." | | 2 | "Aurora looked at the maps on the walls, the black-and-white photographs of streets that no longer existed, and thought about all the versions of herself that no longer existed either—the girl in that Cardiff kitchen, the girl who'd packed a bag at dawn, the girl who answered texts just fine, thanks." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 545 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.023853211009174313 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.005504587155963303 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 84 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 84 | | mean | 15.45 | | std | 18.96 | | cv | 1.227 | | sampleLengths | | 0 | 36 | | 1 | 27 | | 2 | 13 | | 3 | 1 | | 4 | 3 | | 5 | 24 | | 6 | 5 | | 7 | 27 | | 8 | 21 | | 9 | 14 | | 10 | 4 | | 11 | 13 | | 12 | 2 | | 13 | 23 | | 14 | 7 | | 15 | 6 | | 16 | 18 | | 17 | 1 | | 18 | 23 | | 19 | 6 | | 20 | 3 | | 21 | 3 | | 22 | 18 | | 23 | 8 | | 24 | 2 | | 25 | 7 | | 26 | 11 | | 27 | 17 | | 28 | 13 | | 29 | 7 | | 30 | 7 | | 31 | 1 | | 32 | 42 | | 33 | 24 | | 34 | 3 | | 35 | 8 | | 36 | 9 | | 37 | 1 | | 38 | 51 | | 39 | 12 | | 40 | 2 | | 41 | 3 | | 42 | 22 | | 43 | 6 | | 44 | 8 | | 45 | 21 | | 46 | 4 | | 47 | 4 | | 48 | 5 | | 49 | 7 |
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| 64.68% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.4166666666666667 | | totalSentences | 84 | | uniqueOpeners | 35 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 35 | | matches | | 0 | "Then the face above it," | | 1 | "Somewhere in the city, noodles" |
| | ratio | 0.057 | |
| 71.43% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 35 | | matches | | 0 | "She noticed the umbrella first—battered," | | 1 | "He'd been nineteen the last" | | 2 | "He wore a suit that" | | 3 | "She turned her glass on" | | 4 | "He laughed, and the laugh" | | 5 | "He looked down at his" | | 6 | "He turned the whisky glass" | | 7 | "He exhaled through his nose" | | 8 | "She rubbed the crescent scar" | | 9 | "She flinched, and he saw" | | 10 | "he said quietly" | | 11 | "He drank the last of" | | 12 | "he said, and grinned, and" |
| | ratio | 0.371 | |
| 45.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 29 | | totalSentences | 35 | | matches | | 0 | "The green neon sign of" | | 1 | "She noticed the umbrella first—battered," | | 2 | "The man turned." | | 3 | "He'd been nineteen the last" | | 4 | "The man crossing the bar" | | 5 | "He wore a suit that" | | 6 | "Silas materialised from behind the" | | 7 | "Gethin glanced up at Silas," | | 8 | "Silas poured without comment and" | | 9 | "Gethin started, and stopped" | | 10 | "She turned her glass on" | | 11 | "He laughed, and the laugh" | | 12 | "He looked down at his" | | 13 | "He turned the whisky glass" | | 14 | "Aurora watched the bubbles climb" | | 15 | "The scar in his eyebrow" | | 16 | "The bar door opened and" | | 17 | "Gethin finally drank, a measured" | | 18 | "He exhaled through his nose" | | 19 | "She rubbed the crescent scar" |
| | ratio | 0.829 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 35 | | matches | (empty) | | ratio | 0 | |
| 47.62% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 15 | | technicalSentenceCount | 2 | | matches | | 0 | "He looked down at his hands as if they'd done it without permission." | | 1 | "Aurora looked at the maps on the walls, the black-and-white photographs of streets that no longer existed, and thought about all the versions of herself that no…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 1 | | fancyTags | | 0 | "Aurora repeated (repeat)" |
| | dialogueSentences | 56 | | tagDensity | 0.214 | | leniency | 0.429 | | rawRatio | 0.083 | | effectiveRatio | 0.036 | |