| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.388 | | leniency | 0.776 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.87% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1142 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "very" | | 1 | "gently" | | 2 | "really" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 91.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1142 | | totalAiIsms | 2 | | 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 | 53 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 53 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 81 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 72 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1138 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 13 | | wordCount | 659 | | uniqueNames | 7 | | maxNameDensity | 0.61 | | worstName | "Tom" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Tom" | | discoveredNames | | Aurora | 2 | | Raven | 1 | | Nest | 1 | | Tuesday | 1 | | February | 1 | | Silas | 3 | | Tom | 4 |
| | persons | | 0 | "Aurora" | | 1 | "Raven" | | 2 | "Silas" | | 3 | "Tom" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 32 | | 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 | 1138 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 81 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 27.1 | | std | 21.21 | | cv | 0.783 | | sampleLengths | | 0 | 60 | | 1 | 28 | | 2 | 12 | | 3 | 1 | | 4 | 32 | | 5 | 25 | | 6 | 61 | | 7 | 12 | | 8 | 49 | | 9 | 11 | | 10 | 19 | | 11 | 2 | | 12 | 43 | | 13 | 41 | | 14 | 7 | | 15 | 2 | | 16 | 39 | | 17 | 5 | | 18 | 40 | | 19 | 22 | | 20 | 37 | | 21 | 4 | | 22 | 14 | | 23 | 50 | | 24 | 14 | | 25 | 3 | | 26 | 37 | | 27 | 68 | | 28 | 3 | | 29 | 4 | | 30 | 65 | | 31 | 33 | | 32 | 12 | | 33 | 48 | | 34 | 6 | | 35 | 87 | | 36 | 25 | | 37 | 30 | | 38 | 39 | | 39 | 23 | | 40 | 9 | | 41 | 16 |
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| 92.02% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 53 | | matches | | 0 | "being asked" | | 1 | "been children" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 124 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 81 | | ratio | 0.012 | | matches | | 0 | "\"Look at you. You finished. You've got a practice, a conference badge in your pocket, a wedding ring—\" she nodded at his hand, \"—a whole life that ran on without me in it. And here I am delivering noodles in the rain. We're not the people who sat outside that library, Tom. Those two thought they had all the time in the world to become whatever they wanted. They were wrong.\"" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 639 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 14 | | adverbRatio | 0.02190923317683881 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.009389671361502348 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 81 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 81 | | mean | 14.05 | | std | 13.38 | | cv | 0.952 | | sampleLengths | | 0 | 37 | | 1 | 5 | | 2 | 18 | | 3 | 28 | | 4 | 12 | | 5 | 1 | | 6 | 9 | | 7 | 23 | | 8 | 16 | | 9 | 9 | | 10 | 3 | | 11 | 4 | | 12 | 54 | | 13 | 6 | | 14 | 6 | | 15 | 27 | | 16 | 22 | | 17 | 10 | | 18 | 1 | | 19 | 9 | | 20 | 10 | | 21 | 2 | | 22 | 30 | | 23 | 13 | | 24 | 41 | | 25 | 4 | | 26 | 3 | | 27 | 2 | | 28 | 16 | | 29 | 23 | | 30 | 5 | | 31 | 10 | | 32 | 30 | | 33 | 17 | | 34 | 5 | | 35 | 10 | | 36 | 15 | | 37 | 12 | | 38 | 4 | | 39 | 14 | | 40 | 15 | | 41 | 1 | | 42 | 1 | | 43 | 12 | | 44 | 10 | | 45 | 11 | | 46 | 9 | | 47 | 5 | | 48 | 3 | | 49 | 14 |
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| 72.02% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4691358024691358 | | totalSentences | 81 | | uniqueOpeners | 38 | |
| 70.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 47 | | matches | | | ratio | 0.021 | |
| 0.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 47 | | matches | | 0 | "She unzipped her delivery jacket," | | 1 | "She knew the voice before" | | 2 | "It came up through years" | | 3 | "He half-rose from the stool," | | 4 | "She recognised him." | | 5 | "He laughed, and the laugh" | | 6 | "She set the bag down" | | 7 | "He took this in" | | 8 | "He set the crate down" | | 9 | "he murmured to her, and" | | 10 | "He turned the glass in" | | 11 | "He held up a hand" | | 12 | "She picked up the whisky," | | 13 | "He had the grace to" | | 14 | "She did the arithmetic and" | | 15 | "They had been children when" | | 16 | "They had thought they understood" | | 17 | "She finally drank, a small" | | 18 | "He stared at her." | | 19 | "She turned the glass" |
| | ratio | 0.553 | |
| 34.47% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 40 | | totalSentences | 47 | | matches | | 0 | "The green neon buzzed overhead," | | 1 | "The bar was nearly empty." | | 2 | "A Tuesday in February did" | | 3 | "She unzipped her delivery jacket," | | 4 | "That was when the man" | | 5 | "She knew the voice before" | | 6 | "It came up through years" | | 7 | "He half-rose from the stool," | | 8 | "She recognised him." | | 9 | "That was the trouble." | | 10 | "The name came out flat" | | 11 | "He laughed, and the laugh" | | 12 | "She set the bag down" | | 13 | "He took this in" | | 14 | "Silas came up from the" | | 15 | "He set the crate down" | | 16 | "he murmured to her, and" | | 17 | "Tom watched him go." | | 18 | "He turned the glass in" | | 19 | "He held up a hand" |
| | ratio | 0.851 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 47 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 2 | | matches | | 0 | "Underneath the weight he'd put on, beneath the receding line of his hair and the beard that hadn't been there when they were nineteen, she found the boy who'd s…" | | 1 | "Down the bar, Silas wiped a glass that was already clean and pretended not to listen, which was its own kind of listening." |
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| 72.37% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 2 | | matches | | 0 | "He laughed, and the laugh was the same, that quick nervous bark she'd forgotten she'd known" | | 1 | "he offered, as if the name might bridge something" |
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| 88.78% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "He laughed (laugh)" | | 1 | "he murmured (murmur)" | | 2 | "she agreed (agree)" |
| | dialogueSentences | 49 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0.429 | | effectiveRatio | 0.122 | |