| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 97.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2369 | | 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) | |
| 78.89% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2369 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "weight" | | 1 | "glint" | | 2 | "perfect" | | 3 | "silence" | | 4 | "loomed" | | 5 | "eyebrow" | | 6 | "depths" | | 7 | "complex" | | 8 | "echoed" |
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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 | 1 | | narrationSentences | 303 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 0 | | narrationSentences | 303 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 305 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2365 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 11 | | unquotedAttributions | 9 | | matches | | 0 | "Only the one, he said." | | 1 | "Kitchen accident when I was nine, Rory said." | | 2 | "If you two would like to avoid an audience, he said, the library is quiet tonight." | | 3 | "I came to say something and it felt so easy on the pavement, she said." | | 4 | "He watched, he said he did not belong there, not like us." | | 5 | "I introduced you to him, she said." | | 6 | "What did he call me, Rory said." | | 7 | "He is in the main room, he said." | | 8 | "Laila, he said." |
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| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 106 | | wordCount | 2348 | | uniqueNames | 17 | | maxNameDensity | 1.28 | | worstName | "You" | | maxWindowNameDensity | 3.5 | | worstWindowName | "You" | | discoveredNames | | Si | 1 | | Rory | 28 | | Cardiff | 3 | | London | 2 | | Eva | 18 | | Silas | 7 | | Take | 1 | | Rita | 1 | | Look | 1 | | Irish | 1 | | Evan | 3 | | Severn | 1 | | Radio | 1 | | Welsh | 1 | | Laila | 2 | | You | 30 | | Do | 5 |
| | persons | | 0 | "Rory" | | 1 | "Eva" | | 2 | "Silas" | | 3 | "Rita" | | 4 | "Evan" | | 5 | "Laila" | | 6 | "You" |
| | places | | 0 | "Cardiff" | | 1 | "London" | | 2 | "Take" | | 3 | "Do" |
| | globalScore | 0.861 | | windowScore | 0.5 | |
| 87.50% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 160 | | glossingSentenceCount | 4 | | matches | | 0 | "as if testing for a shock" | | 1 | "tasted like coins" | | 2 | "tasted like pennies" | | 3 | "as if remembering gravity had become stricter in rooms like this" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2365 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 305 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 173 | | mean | 13.67 | | std | 14.86 | | cv | 1.087 | | sampleLengths | | 0 | 1 | | 1 | 26 | | 2 | 71 | | 3 | 24 | | 4 | 5 | | 5 | 7 | | 6 | 6 | | 7 | 22 | | 8 | 1 | | 9 | 18 | | 10 | 2 | | 11 | 4 | | 12 | 8 | | 13 | 3 | | 14 | 5 | | 15 | 44 | | 16 | 5 | | 17 | 5 | | 18 | 8 | | 19 | 68 | | 20 | 2 | | 21 | 8 | | 22 | 5 | | 23 | 3 | | 24 | 27 | | 25 | 8 | | 26 | 7 | | 27 | 10 | | 28 | 7 | | 29 | 4 | | 30 | 45 | | 31 | 2 | | 32 | 2 | | 33 | 3 | | 34 | 11 | | 35 | 1 | | 36 | 2 | | 37 | 32 | | 38 | 6 | | 39 | 8 | | 40 | 35 | | 41 | 6 | | 42 | 5 | | 43 | 10 | | 44 | 4 | | 45 | 49 | | 46 | 4 | | 47 | 3 | | 48 | 5 | | 49 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 303 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 463 | | matches | | 0 | "was relearning" | | 1 | "was keeping" | | 2 | "was making were laughing" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 305 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1965 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 63 | | adverbRatio | 0.03206106870229008 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.002544529262086514 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 305 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 305 | | mean | 7.75 | | std | 6.21 | | cv | 0.8 | | sampleLengths | | 0 | 1 | | 1 | 3 | | 2 | 11 | | 3 | 12 | | 4 | 17 | | 5 | 18 | | 6 | 20 | | 7 | 6 | | 8 | 10 | | 9 | 7 | | 10 | 3 | | 11 | 14 | | 12 | 5 | | 13 | 7 | | 14 | 6 | | 15 | 7 | | 16 | 10 | | 17 | 5 | | 18 | 1 | | 19 | 18 | | 20 | 2 | | 21 | 4 | | 22 | 2 | | 23 | 1 | | 24 | 5 | | 25 | 3 | | 26 | 5 | | 27 | 7 | | 28 | 22 | | 29 | 15 | | 30 | 5 | | 31 | 5 | | 32 | 3 | | 33 | 5 | | 34 | 11 | | 35 | 8 | | 36 | 35 | | 37 | 14 | | 38 | 2 | | 39 | 8 | | 40 | 5 | | 41 | 3 | | 42 | 4 | | 43 | 6 | | 44 | 9 | | 45 | 8 | | 46 | 8 | | 47 | 3 | | 48 | 4 | | 49 | 10 |
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| 36.89% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 40 | | diversityRatio | 0.25573770491803277 | | totalSentences | 305 | | uniqueOpeners | 78 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 10 | | totalSentences | 260 | | matches | | 0 | "Once upon a time she" | | 1 | "Only the one, he said." | | 2 | "Always was a poor liar." | | 3 | "Of course you do." | | 4 | "Of course he did." | | 5 | "Somewhere beyond, slicers chopped fruit" | | 6 | "Away from here." | | 7 | "Of course he did." | | 8 | "Sometimes bad people arrive on" | | 9 | "Then he offered to buy" |
| | ratio | 0.038 | |
| 7.69% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 138 | | totalSentences | 260 | | matches | | 0 | "She caught the edge with" | | 1 | "Her palms stung." | | 2 | "You are smaller in person." | | 3 | "You look exactly as I" | | 4 | "She kept her voice even." | | 5 | "He watched the two women," | | 6 | "You are a tyrant, Si." | | 7 | "He set a bottle on" | | 8 | "She looked at Rory’s hair," | | 9 | "She looked at the thin" | | 10 | "She swallowed once, a wince" | | 11 | "I live upstairs." | | 12 | "I lend a hand." | | 13 | "I would not dare." | | 14 | "She tucked a strand of" | | 15 | "She stepped back from the" | | 16 | "You look, different." | | 17 | "She held the word like" | | 18 | "I started at four, then" | | 19 | "I am on six." |
| | ratio | 0.531 | |
| 54.23% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 211 | | totalSentences | 260 | | matches | | 0 | "The crate slipped." | | 1 | "Bottles knocked against each other," | | 2 | "She caught the edge with" | | 3 | "Eva stood five feet away," | | 4 | "The eyes were the same," | | 5 | "Rory set the crate on" | | 6 | "Her palms stung." | | 7 | "A white half-moon scar flashed" | | 8 | "You are smaller in person." | | 9 | "You look exactly as I" | | 10 | "Rory wiped her hands on" | | 11 | "The cotton felt gritty with" | | 12 | "She kept her voice even." | | 13 | "Eva’s mouth twitched at that," | | 14 | "Ginger beer, then." | | 15 | "Silas glanced up from the" | | 16 | "The soft light found the" | | 17 | "He watched the two women," | | 18 | "You are a tyrant, Si." | | 19 | "A benevolent one." |
| | ratio | 0.812 | |
| 57.69% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 260 | | matches | | 0 | "Now her wrists were bare," | | 1 | "If you keep that here." | | 2 | "If you two would like" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 83 | | technicalSentenceCount | 3 | | matches | | 0 | "She could hear the glass at her mouth, the way it clicked against tooth, the lemon oil that greased the rim." | | 1 | "I miss the way the pavement sounds when it rains and your father arguing on Radio 4 with a politician who cannot pronounce the Welsh names." | | 2 | "Eva stood too quickly and then sat down again, as if remembering gravity had become stricter in rooms like this." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |