| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 65 | | tagDensity | 0.215 | | leniency | 0.431 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.81% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1391 | | totalAiIsmAdverbs | 2 | | 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) | |
| 89.22% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1391 | | totalAiIsms | 3 | | 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 | 45 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 45 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 66 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1394 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 685 | | uniqueNames | 9 | | maxNameDensity | 2.63 | | worstName | "Eva" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Old | 1 | | Compton | 1 | | Street | 1 | | Raven | 1 | | Nest | 1 | | Rory | 9 | | Eva | 18 | | Silas | 2 | | Cathays | 1 |
| | persons | | | places | | 0 | "Old" | | 1 | "Compton" | | 2 | "Street" | | 3 | "Raven" | | 4 | "Cathays" |
| | globalScore | 0.186 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | 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 | 1394 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 62 | | mean | 22.48 | | std | 24 | | cv | 1.068 | | sampleLengths | | 0 | 57 | | 1 | 17 | | 2 | 27 | | 3 | 6 | | 4 | 13 | | 5 | 16 | | 6 | 12 | | 7 | 16 | | 8 | 74 | | 9 | 1 | | 10 | 7 | | 11 | 35 | | 12 | 5 | | 13 | 25 | | 14 | 2 | | 15 | 3 | | 16 | 3 | | 17 | 64 | | 18 | 19 | | 19 | 2 | | 20 | 12 | | 21 | 8 | | 22 | 54 | | 23 | 11 | | 24 | 34 | | 25 | 9 | | 26 | 3 | | 27 | 9 | | 28 | 65 | | 29 | 2 | | 30 | 14 | | 31 | 46 | | 32 | 11 | | 33 | 10 | | 34 | 3 | | 35 | 38 | | 36 | 12 | | 37 | 4 | | 38 | 7 | | 39 | 51 | | 40 | 18 | | 41 | 5 | | 42 | 58 | | 43 | 8 | | 44 | 87 | | 45 | 6 | | 46 | 5 | | 47 | 24 | | 48 | 25 | | 49 | 4 |
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| 97.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 45 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 108 | | matches | (empty) | |
| 83.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 96 | | ratio | 0.021 | | matches | | 0 | "The face beneath was older, thinner, arranged — every feature set in its place like cutlery before a dinner party." | | 1 | "Silas set two glasses down without asking what they wanted — the good malt in front of Eva, the pint in front of Rory — and tapped the bar twice with his signet ring." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 685 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.033576642335766425 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.00145985401459854 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 14.52 | | std | 12.89 | | cv | 0.888 | | sampleLengths | | 0 | 28 | | 1 | 29 | | 2 | 17 | | 3 | 27 | | 4 | 6 | | 5 | 13 | | 6 | 10 | | 7 | 6 | | 8 | 12 | | 9 | 16 | | 10 | 13 | | 11 | 7 | | 12 | 14 | | 13 | 20 | | 14 | 20 | | 15 | 1 | | 16 | 6 | | 17 | 1 | | 18 | 22 | | 19 | 13 | | 20 | 5 | | 21 | 21 | | 22 | 4 | | 23 | 2 | | 24 | 3 | | 25 | 3 | | 26 | 34 | | 27 | 30 | | 28 | 11 | | 29 | 8 | | 30 | 2 | | 31 | 12 | | 32 | 8 | | 33 | 19 | | 34 | 7 | | 35 | 28 | | 36 | 11 | | 37 | 25 | | 38 | 9 | | 39 | 9 | | 40 | 3 | | 41 | 9 | | 42 | 34 | | 43 | 3 | | 44 | 24 | | 45 | 4 | | 46 | 2 | | 47 | 14 | | 48 | 2 | | 49 | 17 |
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| 65.28% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.4479166666666667 | | totalSentences | 96 | | uniqueOpeners | 43 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 41 | | matches | | 0 | "Then it cooled." | | 1 | "Somewhere in the back, behind" |
| | ratio | 0.049 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 4 | | totalSentences | 41 | | matches | | 0 | "She unslung the bag, set" | | 1 | "He limped toward the shelves" | | 2 | "They had learned new disciplines," | | 3 | "She nodded at Eva's glass" |
| | ratio | 0.098 | |
| 57.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 33 | | totalSentences | 41 | | matches | | 0 | "Rory shouldered into The Raven's" | | 1 | "Silas looked up from the" | | 2 | "She unslung the bag, set" | | 3 | "That was when the woman" | | 4 | "Camel coat draped over the" | | 5 | "Hair cut sharp at the" | | 6 | "The face beneath was older," | | 7 | "The keys dug into her" | | 8 | "Eva laughed, and it was" | | 9 | "Eva's fingers found the stem" | | 10 | "Silas set two glasses down" | | 11 | "He limped toward the shelves" | | 12 | "Eva watched the seam in" | | 13 | "Eva turned back, and her" | | 14 | "They had learned new disciplines," | | 15 | "Eva lifted her glass, held" | | 16 | "Eva's mouth curved, and for" | | 17 | "The word landed flat, no" | | 18 | "The pint was cold and" | | 19 | "Eva turned her ring once" |
| | ratio | 0.805 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 41 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 2 | | matches | | 0 | "Eva's mouth curved, and for a second the curve belonged to a girl who had once stolen a tray of chips off a chip-shop counter in Cathays and fed half of it to g…" | | 1 | "Looked at the shelf with the door in it, at the photographs of men with borrowed faces, at the girl who had climbed everything she'd ever been dared to climb an…" |
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| 89.29% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva's voice lost, grain showing through like bare wood under stripped varnish" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 65 | | tagDensity | 0.062 | | leniency | 0.123 | | rawRatio | 0.25 | | effectiveRatio | 0.031 | |