| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 116 | | tagDensity | 0.121 | | leniency | 0.241 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.26% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2111 | | 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) | |
| 73.95% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2111 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "trembled" | | 1 | "weight" | | 2 | "shattered" | | 3 | "tracing" | | 4 | "silk" | | 5 | "silence" | | 6 | "pulse" | | 7 | "eyebrow" | | 8 | "scanned" | | 9 | "flickered" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "room fell silent" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 206 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 4 | | narrationSentences | 206 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 308 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2111 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 35 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 121 | | wordCount | 1588 | | uniqueNames | 12 | | maxNameDensity | 2.58 | | worstName | "Eva" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Eva" | | discoveredNames | | Aurora | 37 | | Raven | 1 | | Nest | 2 | | Eva | 41 | | Cardiff | 3 | | Central | 1 | | Silas | 28 | | Volvo | 1 | | Evan | 2 | | London | 2 | | Soho | 1 | | Carter | 2 |
| | persons | | 0 | "Aurora" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Eva" | | 4 | "Central" | | 5 | "Silas" | | 6 | "Evan" | | 7 | "Carter" |
| | places | | 0 | "Cardiff" | | 1 | "London" | | 2 | "Soho" |
| | globalScore | 0.209 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 113 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 0.947 | | wordCount | 2111 | | matches | | 0 | "not like a bridge but a building giving way, floor after floor" | | 1 | "not with surprise but recognition" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 308 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 230 | | mean | 9.18 | | std | 11.35 | | cv | 1.237 | | sampleLengths | | 0 | 5 | | 1 | 22 | | 2 | 1 | | 3 | 14 | | 4 | 49 | | 5 | 8 | | 6 | 66 | | 7 | 6 | | 8 | 4 | | 9 | 23 | | 10 | 1 | | 11 | 7 | | 12 | 5 | | 13 | 2 | | 14 | 22 | | 15 | 5 | | 16 | 7 | | 17 | 56 | | 18 | 2 | | 19 | 7 | | 20 | 6 | | 21 | 9 | | 22 | 22 | | 23 | 5 | | 24 | 2 | | 25 | 3 | | 26 | 6 | | 27 | 36 | | 28 | 4 | | 29 | 3 | | 30 | 2 | | 31 | 4 | | 32 | 4 | | 33 | 9 | | 34 | 23 | | 35 | 2 | | 36 | 4 | | 37 | 8 | | 38 | 25 | | 39 | 2 | | 40 | 58 | | 41 | 7 | | 42 | 7 | | 43 | 5 | | 44 | 6 | | 45 | 4 | | 46 | 3 | | 47 | 4 | | 48 | 27 | | 49 | 6 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 206 | | matches | | 0 | "been broken" | | 1 | "been written" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 296 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 308 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1597 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 37 | | adverbRatio | 0.023168440826549782 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.003757044458359424 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 308 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 308 | | mean | 6.85 | | std | 5.19 | | cv | 0.757 | | sampleLengths | | 0 | 5 | | 1 | 12 | | 2 | 10 | | 3 | 1 | | 4 | 14 | | 5 | 13 | | 6 | 17 | | 7 | 19 | | 8 | 8 | | 9 | 16 | | 10 | 18 | | 11 | 18 | | 12 | 7 | | 13 | 4 | | 14 | 3 | | 15 | 6 | | 16 | 4 | | 17 | 10 | | 18 | 13 | | 19 | 1 | | 20 | 7 | | 21 | 5 | | 22 | 2 | | 23 | 5 | | 24 | 17 | | 25 | 5 | | 26 | 7 | | 27 | 26 | | 28 | 9 | | 29 | 2 | | 30 | 19 | | 31 | 2 | | 32 | 3 | | 33 | 4 | | 34 | 6 | | 35 | 3 | | 36 | 6 | | 37 | 22 | | 38 | 5 | | 39 | 2 | | 40 | 3 | | 41 | 6 | | 42 | 15 | | 43 | 8 | | 44 | 13 | | 45 | 4 | | 46 | 3 | | 47 | 2 | | 48 | 4 | | 49 | 4 |
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| 45.78% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.24025974025974026 | | totalSentences | 308 | | uniqueOpeners | 74 | |
| 77.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 172 | | matches | | 0 | "Then Eva crossed the room." | | 1 | "Once, she would have missed" | | 2 | "Once, she had known every" | | 3 | "Somewhere beyond the hidden room," |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 172 | | matches | | 0 | "She wore a cream-coloured suit," | | 1 | "Her hair, once a tangled" | | 2 | "She had lost weight." | | 3 | "It shattered beneath the laughter" | | 4 | "It had not been there" | | 5 | "He placed the broken pieces" | | 6 | "His silver signet ring flashed" | | 7 | "He did not look at" | | 8 | "She caught his wrist before" | | 9 | "He stared at her." | | 10 | "It looked soft and black," | | 11 | "Her thumb found the small" | | 12 | "It was tiny, but Aurora" | | 13 | "She remembered Eva at seventeen," | | 14 | "She remembered her at twenty-two," | | 15 | "She remembered the last message." | | 16 | "I’ll meet you in London." | | 17 | "She had slept on a" | | 18 | "Her nails were short and" | | 19 | "His limp disappeared when he" |
| | ratio | 0.192 | |
| 21.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 151 | | totalSentences | 172 | | matches | | 0 | "Aurora stopped with a tray" | | 1 | "The glasses trembled, sending pale" | | 2 | "The Raven’s Nest pressed close" | | 3 | "Aurora glanced at the mirror" | | 4 | "The woman stood with one" | | 5 | "She wore a cream-coloured suit," | | 6 | "Her hair, once a tangled" | | 7 | "A thin gold chain circled" | | 8 | "She had lost weight." | | 9 | "The woman smiled at the" | | 10 | "Aurora forgot the tray." | | 11 | "A glass slid from its" | | 12 | "It shattered beneath the laughter" | | 13 | "The name crossed the room" | | 14 | "Eva lifted her champagne flute." | | 15 | "Aurora looked at the woman’s" | | 16 | "It had not been there" | | 17 | "Eva’s smile held." | | 18 | "Something behind it withdrew." | | 19 | "Silas bent to gather the" |
| | ratio | 0.878 | |
| 58.14% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 172 | | matches | | 0 | "Now the lie came dressed" | | 1 | "If Eva reaches you, do" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 2 | | matches | | 0 | "Old maps crowded the walls beside black-and-white photographs of men who had died before Aurora’s parents met." | | 1 | "A faint pale line circled the base of her thumb, as if jewellery had worn the skin raw." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 14 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 116 | | tagDensity | 0.121 | | leniency | 0.241 | | rawRatio | 0 | | effectiveRatio | 0 | |