| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 56 | | tagDensity | 0.214 | | leniency | 0.429 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.36% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1893 | | 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) | |
| 57.74% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1893 | | totalAiIsms | 16 | | found | | | highlights | | 0 | "pulse" | | 1 | "silence" | | 2 | "scanned" | | 3 | "lilt" | | 4 | "charged" | | 5 | "flicked" | | 6 | "silk" | | 7 | "calculating" | | 8 | "whisper" | | 9 | "single tear" | | 10 | "perfect" | | 11 | "traced" | | 12 | "racing" | | 13 | "weight" | | 14 | "tension" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 112 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 112 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 156 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1893 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 18 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1137 | | uniqueNames | 12 | | maxNameDensity | 2.29 | | worstName | "Eva" | | maxWindowNameDensity | 5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | Cardiff | 1 | | Earl | 1 | | Grey | 1 | | Welsh | 1 | | London | 1 | | Rory | 25 | | Silas | 5 | | Eva | 26 |
| | persons | | 0 | "Nest" | | 1 | "Earl" | | 2 | "Grey" | | 3 | "Rory" | | 4 | "Silas" | | 5 | "Eva" |
| | places | | 0 | "Raven" | | 1 | "Cardiff" | | 2 | "London" |
| | globalScore | 0.357 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 82 | | 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 | 1893 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 156 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 73 | | mean | 25.93 | | std | 16.9 | | cv | 0.652 | | sampleLengths | | 0 | 77 | | 1 | 39 | | 2 | 3 | | 3 | 16 | | 4 | 63 | | 5 | 11 | | 6 | 34 | | 7 | 6 | | 8 | 41 | | 9 | 1 | | 10 | 47 | | 11 | 37 | | 12 | 17 | | 13 | 36 | | 14 | 12 | | 15 | 76 | | 16 | 4 | | 17 | 30 | | 18 | 4 | | 19 | 29 | | 20 | 26 | | 21 | 25 | | 22 | 4 | | 23 | 27 | | 24 | 21 | | 25 | 43 | | 26 | 18 | | 27 | 3 | | 28 | 36 | | 29 | 4 | | 30 | 18 | | 31 | 40 | | 32 | 13 | | 33 | 34 | | 34 | 4 | | 35 | 55 | | 36 | 18 | | 37 | 27 | | 38 | 22 | | 39 | 45 | | 40 | 1 | | 41 | 11 | | 42 | 19 | | 43 | 21 | | 44 | 26 | | 45 | 17 | | 46 | 25 | | 47 | 33 | | 48 | 27 | | 49 | 26 |
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| 92.73% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 112 | | matches | | 0 | "been sanded" | | 1 | "were painted" | | 2 | "been excised" | | 3 | "been shown" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 193 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 156 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1139 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.01755926251097454 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.006145741878841089 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 156 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 156 | | mean | 12.13 | | std | 8.08 | | cv | 0.666 | | sampleLengths | | 0 | 20 | | 1 | 20 | | 2 | 20 | | 3 | 17 | | 4 | 16 | | 5 | 9 | | 6 | 14 | | 7 | 3 | | 8 | 16 | | 9 | 15 | | 10 | 13 | | 11 | 18 | | 12 | 17 | | 13 | 7 | | 14 | 4 | | 15 | 5 | | 16 | 7 | | 17 | 4 | | 18 | 8 | | 19 | 10 | | 20 | 6 | | 21 | 22 | | 22 | 19 | | 23 | 1 | | 24 | 5 | | 25 | 10 | | 26 | 25 | | 27 | 7 | | 28 | 5 | | 29 | 12 | | 30 | 20 | | 31 | 3 | | 32 | 14 | | 33 | 14 | | 34 | 10 | | 35 | 7 | | 36 | 5 | | 37 | 3 | | 38 | 3 | | 39 | 6 | | 40 | 9 | | 41 | 7 | | 42 | 6 | | 43 | 14 | | 44 | 11 | | 45 | 29 | | 46 | 4 | | 47 | 8 | | 48 | 11 | | 49 | 11 |
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| 42.31% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.21153846153846154 | | totalSentences | 156 | | uniqueOpeners | 33 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 105 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 105 | | matches | | 0 | "She counted the seconds between" | | 1 | "She shook a silver umbrella," | | 2 | "Her face sharpened as the" | | 3 | "Her eyes scanned the room" | | 4 | "Her left wrist brushed the" | | 5 | "She tucked the hand into" | | 6 | "His hazel eyes flicked to" | | 7 | "She didn't turn." | | 8 | "She stopped six inches from" | | 9 | "Her nails were painted a" | | 10 | "She reached for the glass" | | 11 | "Her bright blue eyes bore" | | 12 | "His silver signet ring flashed" | | 13 | "She reached out, fingers hovering" | | 14 | "She saw the limp, the" | | 15 | "She reached into her coat" | | 16 | "She extracted a card, embossed" | | 17 | "She leaned in, her gaze" | | 18 | "She forced her hands to" | | 19 | "She watched a lone figure" |
| | ratio | 0.276 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 102 | | totalSentences | 105 | | matches | | 0 | "The door to The Raven's" | | 1 | "Rory kept her forehead pressed" | | 2 | "The green neon sign above" | | 3 | "She counted the seconds between" | | 4 | "Silas moved behind the bar," | | 5 | "The old maps on the" | | 6 | "The bell chimed." | | 7 | "Rory turned, expecting the delivery" | | 8 | "A woman stood in the" | | 9 | "She shook a silver umbrella," | | 10 | "Her face sharpened as the" | | 11 | "Her eyes scanned the room" | | 12 | "The umbrella handle clattered against" | | 13 | "The woman looked down." | | 14 | "Rory felt the floor shift." | | 15 | "Memories of Cardiff slammed into" | | 16 | "The smell of old paper" | | 17 | "A hand pulling her toward" | | 18 | "The voice carried a lilt" | | 19 | "The woman stepped fully inside," |
| | ratio | 0.971 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 105 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 54 | | technicalSentenceCount | 2 | | matches | | 0 | "The small crescent scar itched, a phantom memory of broken china and a shouting match that ended with her mother sweeping shards into the bin." | | 1 | "The girl who had slept in Rory's guest bedroom, clutching a stuffed rabbit with one ear bitten off, had been excised and replaced by this architect of sharp lin…" |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "Silas said, his voice a gravel rumble that cut through the tension" |
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| 96.43% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 3 | | fancyTags | | 0 | "Eva whispered (whisper)" | | 1 | "Rory repeated (repeat)" | | 2 | "Rory murmured (murmur)" |
| | dialogueSentences | 56 | | tagDensity | 0.125 | | leniency | 0.25 | | rawRatio | 0.429 | | effectiveRatio | 0.107 | |