| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 2 | | adverbTags | | 0 | "She turned back [back]" | | 1 | "she said suddenly [suddenly]" |
| | dialogueSentences | 59 | | tagDensity | 0.203 | | leniency | 0.407 | | rawRatio | 0.167 | | effectiveRatio | 0.068 | |
| 95.35% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1075 | | 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) | |
| 90.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1075 | | 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 | 44 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 44 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 90 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 70 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1088 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 647 | | uniqueNames | 10 | | maxNameDensity | 2.63 | | worstName | "Aurora" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Megan" | | discoveredNames | | Cardiff | 1 | | Pryce | 1 | | Megan | 16 | | Aurora | 17 | | Silas | 1 | | Started | 1 | | Evan | 1 | | Paddington | 1 | | Eva | 1 | | Camden | 1 |
| | persons | | 0 | "Pryce" | | 1 | "Megan" | | 2 | "Aurora" | | 3 | "Silas" | | 4 | "Evan" | | 5 | "Eva" |
| | places | | 0 | "Cardiff" | | 1 | "Paddington" | | 2 | "Camden" |
| | globalScore | 0.186 | | windowScore | 0.167 | |
| 60.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 28 | | glossingSentenceCount | 1 | | matches | | 0 | "as if expecting to see through it" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.919 | | wordCount | 1088 | | matches | | 0 | "not pity, which she could have refused, but grief" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 90 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 57 | | mean | 19.09 | | std | 23.25 | | cv | 1.218 | | sampleLengths | | 0 | 74 | | 1 | 19 | | 2 | 6 | | 3 | 52 | | 4 | 1 | | 5 | 19 | | 6 | 6 | | 7 | 2 | | 8 | 2 | | 9 | 49 | | 10 | 24 | | 11 | 3 | | 12 | 44 | | 13 | 2 | | 14 | 9 | | 15 | 1 | | 16 | 1 | | 17 | 9 | | 18 | 57 | | 19 | 2 | | 20 | 1 | | 21 | 30 | | 22 | 3 | | 23 | 2 | | 24 | 44 | | 25 | 4 | | 26 | 4 | | 27 | 13 | | 28 | 5 | | 29 | 69 | | 30 | 11 | | 31 | 1 | | 32 | 1 | | 33 | 2 | | 34 | 8 | | 35 | 36 | | 36 | 74 | | 37 | 5 | | 38 | 3 | | 39 | 29 | | 40 | 39 | | 41 | 2 | | 42 | 6 | | 43 | 2 | | 44 | 11 | | 45 | 90 | | 46 | 21 | | 47 | 3 | | 48 | 2 | | 49 | 14 |
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| 97.29% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 44 | | matches | | |
| 83.04% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 114 | | matches | | 0 | "was telling" | | 1 | "were leaving" |
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| 15.87% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 90 | | ratio | 0.044 | | matches | | 0 | "\"I wasn't being polite.\" Megan flagged the barman — not Silas tonight, one of the weekend lads — and pointed at Aurora's glass." | | 1 | "She didn't know why she had lied about something so small; it had been practice, perhaps, for the bigger lies that came later." | | 2 | "Aurora had forgotten this about her — the way her hands went looking for something to do when her tongue wouldn't behave." | | 3 | "Megan looked at her then, properly, without the arithmetic, and Aurora saw in her face the thing she had been avoiding for three years — not pity, which she could have refused, but grief." |
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| 99.84% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 647 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.0401854714064915 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.00927357032457496 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 90 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 90 | | mean | 12.09 | | std | 13.06 | | cv | 1.081 | | sampleLengths | | 0 | 19 | | 1 | 24 | | 2 | 10 | | 3 | 21 | | 4 | 19 | | 5 | 6 | | 6 | 3 | | 7 | 49 | | 8 | 1 | | 9 | 11 | | 10 | 8 | | 11 | 6 | | 12 | 2 | | 13 | 2 | | 14 | 12 | | 15 | 37 | | 16 | 8 | | 17 | 16 | | 18 | 3 | | 19 | 40 | | 20 | 4 | | 21 | 2 | | 22 | 9 | | 23 | 1 | | 24 | 1 | | 25 | 3 | | 26 | 6 | | 27 | 23 | | 28 | 18 | | 29 | 16 | | 30 | 2 | | 31 | 1 | | 32 | 4 | | 33 | 26 | | 34 | 3 | | 35 | 2 | | 36 | 36 | | 37 | 2 | | 38 | 6 | | 39 | 4 | | 40 | 4 | | 41 | 3 | | 42 | 10 | | 43 | 5 | | 44 | 5 | | 45 | 33 | | 46 | 8 | | 47 | 23 | | 48 | 3 | | 49 | 8 |
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| 54.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.4 | | totalSentences | 90 | | uniqueOpeners | 36 | |
| 90.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 37 | | matches | | 0 | "Then the door swung and" |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 37 | | matches | | 0 | "She'd only come down for" | | 1 | "She unwound her scarf and" | | 2 | "She turned back" | | 3 | "They let that sit." | | 4 | "She'd told Evan it was" | | 5 | "She didn't know why she" | | 6 | "She turned her glass a" | | 7 | "She thought of the girl" | | 8 | "She thought of Eva opening" | | 9 | "Her thumb worried the rim" | | 10 | "she said suddenly" |
| | ratio | 0.297 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 35 | | totalSentences | 37 | | matches | | 0 | "The green neon bled through" | | 1 | "Aurora sat at the end" | | 2 | "Friday pushed itself against the" | | 3 | "She'd only come down for" | | 4 | "Aurora looked up." | | 5 | "Megan Pryce stood three feet" | | 6 | "Megan's hand went to her" | | 7 | "Megan blinked at the ceiling" | | 8 | "She unwound her scarf and" | | 9 | "Megan twisted on the stool" | | 10 | "Aurora almost laughed." | | 11 | "Megan flagged the barman —" | | 12 | "She turned back" | | 13 | "They let that sit." | | 14 | "The bar filled in around" | | 15 | "The wine came." | | 16 | "Megan took a swallow too" | | 17 | "Aurora looked at her hands." | | 18 | "The crescent scar on her" | | 19 | "She'd told Evan it was" |
| | ratio | 0.946 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 37 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 20 | | technicalSentenceCount | 5 | | matches | | 0 | "Megan Pryce stood three feet away with her scarf still wrapped and her cheeks coloured from the cold, the same Megan who had once held her hair back over a sink…" | | 1 | "She unwound her scarf and dropped onto the stool beside Aurora without being invited, which was the Megan Aurora remembered, the one who treated every chair in …" | | 2 | "She didn't know why she had lied about something so small; it had been practice, perhaps, for the bigger lies that came later." | | 3 | "Outside, a taxi horn did its two-note complaint and somewhere behind them a man was telling a joke about his brother-in-law to people who had already heard it." | | 4 | "The clean, uncomplicated grief of someone who had loved her and been left to wonder, and who was now having to rearrange the wondering into something that could…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 59 | | tagDensity | 0.102 | | leniency | 0.203 | | rawRatio | 0 | | effectiveRatio | 0 | |