| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 2 | | adverbTags | | 0 | "Eva said finally [finally]" | | 1 | "Eva said quietly [quietly]" |
| | dialogueSentences | 70 | | tagDensity | 0.3 | | leniency | 0.6 | | rawRatio | 0.095 | | effectiveRatio | 0.057 | |
| 89.63% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1447 | | totalAiIsmAdverbs | 3 | | 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) | |
| 79.27% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1447 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "shattered" | | 1 | "eyebrow" | | 2 | "pristine" | | 3 | "traced" | | 4 | "flicker" | | 5 | "flickered" |
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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 | 117 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 117 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 165 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 70 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 24 | | totalWords | 1442 | | ratio | 0.017 | | matches | | 0 | "Remember that brilliant girl from Cardiff? Yes, the one who was going to be a barrister. Bit of a shame, really." | | 1 | "Crime and Punishment" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 13.01% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 876 | | uniqueNames | 17 | | maxNameDensity | 2.74 | | worstName | "Eva" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Rory | 18 | | Raven | 1 | | Nest | 1 | | Tuesday | 1 | | Bennett | 2 | | Mayfair | 1 | | Eva | 24 | | Golden | 1 | | Empress | 1 | | Cardiff | 2 | | Soho | 2 | | Foam | 1 | | Silas | 7 | | Old | 2 | | Sad | 1 | | Thames | 1 | | Marylebone | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Bennett" | | 3 | "Eva" | | 4 | "Foam" | | 5 | "Silas" | | 6 | "Old" |
| | places | | 0 | "Mayfair" | | 1 | "Golden" | | 2 | "Cardiff" | | 3 | "Soho" | | 4 | "Thames" | | 5 | "Marylebone" |
| | globalScore | 0.13 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | 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 | 1442 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 165 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 78 | | mean | 18.49 | | std | 19.03 | | cv | 1.03 | | sampleLengths | | 0 | 14 | | 1 | 47 | | 2 | 3 | | 3 | 40 | | 4 | 5 | | 5 | 8 | | 6 | 34 | | 7 | 57 | | 8 | 1 | | 9 | 5 | | 10 | 27 | | 11 | 1 | | 12 | 18 | | 13 | 46 | | 14 | 4 | | 15 | 33 | | 16 | 15 | | 17 | 10 | | 18 | 6 | | 19 | 32 | | 20 | 21 | | 21 | 2 | | 22 | 1 | | 23 | 1 | | 24 | 40 | | 25 | 22 | | 26 | 7 | | 27 | 2 | | 28 | 3 | | 29 | 18 | | 30 | 10 | | 31 | 45 | | 32 | 25 | | 33 | 2 | | 34 | 6 | | 35 | 12 | | 36 | 2 | | 37 | 23 | | 38 | 1 | | 39 | 3 | | 40 | 2 | | 41 | 29 | | 42 | 4 | | 43 | 44 | | 44 | 39 | | 45 | 53 | | 46 | 2 | | 47 | 44 | | 48 | 24 | | 49 | 51 |
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| 96.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 117 | | matches | | 0 | "been sanded" | | 1 | "being asked" | | 2 | "was gone" |
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| 44.96% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 172 | | matches | | 0 | "was waiting" | | 1 | "was thinking" | | 2 | "was doing" | | 3 | "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 | 165 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 881 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 29 | | adverbRatio | 0.032917139614074914 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007945516458569807 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 165 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 165 | | mean | 8.74 | | std | 8.62 | | cv | 0.986 | | sampleLengths | | 0 | 14 | | 1 | 12 | | 2 | 20 | | 3 | 15 | | 4 | 3 | | 5 | 7 | | 6 | 8 | | 7 | 25 | | 8 | 2 | | 9 | 3 | | 10 | 8 | | 11 | 13 | | 12 | 12 | | 13 | 4 | | 14 | 5 | | 15 | 5 | | 16 | 13 | | 17 | 15 | | 18 | 24 | | 19 | 1 | | 20 | 5 | | 21 | 16 | | 22 | 11 | | 23 | 1 | | 24 | 12 | | 25 | 6 | | 26 | 6 | | 27 | 2 | | 28 | 1 | | 29 | 1 | | 30 | 15 | | 31 | 6 | | 32 | 10 | | 33 | 5 | | 34 | 4 | | 35 | 4 | | 36 | 7 | | 37 | 22 | | 38 | 6 | | 39 | 9 | | 40 | 5 | | 41 | 5 | | 42 | 6 | | 43 | 11 | | 44 | 5 | | 45 | 16 | | 46 | 10 | | 47 | 11 | | 48 | 2 | | 49 | 1 |
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| 80.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.49696969696969695 | | totalSentences | 165 | | uniqueOpeners | 82 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 98 | | matches | | 0 | "Still had that same way" | | 1 | "Just a flicker, there and" | | 2 | "Just the person he'd been" | | 3 | "Mostly she just felt tired." | | 4 | "Just stepped into the rain" |
| | ratio | 0.051 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 98 | | matches | | 0 | "She crouched, gathering shards with" | | 1 | "Her blonde hair was shorter" | | 2 | "They sat at one of" | | 3 | "She'd had a nervous tic" | | 4 | "She took another sip" | | 5 | "They just learned to hide" | | 6 | "She'd got it falling from" | | 7 | "She ignored it this time." | | 8 | "She met Rory's eyes" | | 9 | "He'd told Rory once, late-night" | | 10 | "She stood, pulled on her" | | 11 | "She walked to the door." | | 12 | "She looked back." | | 13 | "She didn't answer." | | 14 | "He nodded, limped back to" | | 15 | "She'd ride her bike through" | | 16 | "She picked up her pint." |
| | ratio | 0.173 | |
| 72.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 98 | | matches | | 0 | "The glass shattered against the" | | 1 | "Silas looked up from wiping" | | 2 | "The Raven's Nest was empty" | | 3 | "She crouched, gathering shards with" | | 4 | "The woman at the corner" | | 5 | "Eva fucking Bennett." | | 6 | "Silas limped over with a" | | 7 | "Rory dumped the glass fragments," | | 8 | "The small crescent-shaped scar on" | | 9 | "Childhood accidents left marks." | | 10 | "Eva rose from her seat." | | 11 | "Designer coat, pristine navy wool" | | 12 | "Her blonde hair was shorter" | | 13 | "The girl who'd worn charity" | | 14 | "Eva crossed the worn floorboards," | | 15 | "Eva glanced at the Golden" | | 16 | "The question landed like a" | | 17 | "Bit of a shame, really.*" | | 18 | "The kind of smile that" | | 19 | "Silas cleared his throat behind" |
| | ratio | 0.776 | |
| 51.02% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 98 | | matches | | | ratio | 0.01 | |
| 73.73% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 3 | | matches | | 0 | "The girl who'd worn charity shop jumpers and laughed too loud had been sanded down, polished, remade into something that belonged in Mayfair boardrooms." | | 1 | "As if Rory had become something to be catalogued, explained to colleagues over after-work drinks." | | 2 | "The maps on the walls marked countries neither would ever visit, battles long finished, borders redrawn by hands that had signed death warrants in triplicate." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 1 | | matches | | 0 | "Eva crossed, heels clicking against wood that had seen decades of secrets" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 1 | | fancyTags | | 0 | "Rory muttered (mutter)" |
| | dialogueSentences | 70 | | tagDensity | 0.1 | | leniency | 0.2 | | rawRatio | 0.143 | | effectiveRatio | 0.029 | |