| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 64 | | tagDensity | 0.188 | | leniency | 0.375 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.37% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1558 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 90.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1558 | | totalAiIsms | 3 | | 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 | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 134 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1558 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 17 | | unquotedAttributions | 1 | | matches | | 0 | "When he spoke, he spoke to the sink." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 23 | | wordCount | 1125 | | uniqueNames | 11 | | maxNameDensity | 0.44 | | worstName | "Rory" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Rory" | | discoveredNames | | Brick | 1 | | Lane | 1 | | Eva | 4 | | Rory | 5 | | Water | 2 | | Silas | 1 | | London | 1 | | Marseille | 1 | | Ptolemy | 3 | | Silence | 1 | | Lucien | 3 |
| | persons | | 0 | "Eva" | | 1 | "Rory" | | 2 | "Water" | | 3 | "Silas" | | 4 | "Ptolemy" | | 5 | "Lucien" |
| | places | | 0 | "Brick" | | 1 | "Lane" | | 2 | "London" | | 3 | "Marseille" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | 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 | 1558 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 134 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 80 | | mean | 19.48 | | std | 24.04 | | cv | 1.234 | | sampleLengths | | 0 | 25 | | 1 | 54 | | 2 | 19 | | 3 | 5 | | 4 | 4 | | 5 | 14 | | 6 | 24 | | 7 | 5 | | 8 | 2 | | 9 | 2 | | 10 | 63 | | 11 | 31 | | 12 | 44 | | 13 | 5 | | 14 | 10 | | 15 | 61 | | 16 | 9 | | 17 | 67 | | 18 | 39 | | 19 | 4 | | 20 | 39 | | 21 | 5 | | 22 | 8 | | 23 | 2 | | 24 | 31 | | 25 | 13 | | 26 | 46 | | 27 | 2 | | 28 | 1 | | 29 | 2 | | 30 | 98 | | 31 | 7 | | 32 | 21 | | 33 | 19 | | 34 | 10 | | 35 | 1 | | 36 | 4 | | 37 | 61 | | 38 | 12 | | 39 | 8 | | 40 | 2 | | 41 | 4 | | 42 | 4 | | 43 | 7 | | 44 | 1 | | 45 | 30 | | 46 | 39 | | 47 | 6 | | 48 | 3 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 182 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 134 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1127 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 28 | | adverbRatio | 0.024844720496894408 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0008873114463176575 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 134 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 134 | | mean | 11.63 | | std | 11.16 | | cv | 0.96 | | sampleLengths | | 0 | 2 | | 1 | 23 | | 2 | 7 | | 3 | 31 | | 4 | 16 | | 5 | 19 | | 6 | 5 | | 7 | 4 | | 8 | 11 | | 9 | 3 | | 10 | 20 | | 11 | 4 | | 12 | 5 | | 13 | 2 | | 14 | 2 | | 15 | 8 | | 16 | 5 | | 17 | 20 | | 18 | 30 | | 19 | 21 | | 20 | 4 | | 21 | 6 | | 22 | 4 | | 23 | 4 | | 24 | 14 | | 25 | 22 | | 26 | 5 | | 27 | 10 | | 28 | 19 | | 29 | 28 | | 30 | 14 | | 31 | 9 | | 32 | 46 | | 33 | 21 | | 34 | 18 | | 35 | 7 | | 36 | 6 | | 37 | 8 | | 38 | 4 | | 39 | 4 | | 40 | 17 | | 41 | 18 | | 42 | 5 | | 43 | 8 | | 44 | 2 | | 45 | 20 | | 46 | 7 | | 47 | 4 | | 48 | 13 | | 49 | 12 |
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| 60.70% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.39552238805970147 | | totalSentences | 134 | | uniqueOpeners | 53 | |
| 90.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 74 | | matches | | 0 | "Then she pushed the wood" | | 1 | "Then the shirt, and there" |
| | ratio | 0.027 | |
| 14.59% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 38 | | totalSentences | 74 | | matches | | 0 | "She threw the top one," | | 1 | "She looked at him through" | | 2 | "She kept her palm flat" | | 3 | "He'd counted the exits already." | | 4 | "She'd watched him do that" | | 5 | "He'd hissed at Silas." | | 6 | "He'd hissed at the landlord," | | 7 | "She slid the chain off." | | 8 | "He brought rain in with" | | 9 | "He said nothing about any" | | 10 | "He filed everything and paid" | | 11 | "He handed it over." | | 12 | "She leaned it against the" | | 13 | "He unbuttoned the jacket with" | | 14 | "She took a towel off" | | 15 | "He caught it one-handed across" | | 16 | "He got out of it" | | 17 | "He'd done it himself." | | 18 | "She could tell by the" | | 19 | "he said, quiet, in the" |
| | ratio | 0.514 | |
| 41.08% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 74 | | matches | | 0 | "She threw the top one," | | 1 | "Rain came down Brick Lane" | | 2 | "She looked at him through" | | 3 | "She kept her palm flat" | | 4 | "Water ticked off the railings" | | 5 | "Something shifted behind his eyes" | | 6 | "He'd counted the exits already." | | 7 | "She'd watched him do that" | | 8 | "Ptolemy came out of the" | | 9 | "Claws in the paint." | | 10 | "The cat hated everybody." | | 11 | "He'd hissed at Silas." | | 12 | "He'd hissed at the landlord," | | 13 | "She slid the chain off." | | 14 | "Lucien stepped past her and" | | 15 | "He brought rain in with" | | 16 | "Ptolemy sniffed his trouser leg," | | 17 | "Rory shut the door and" | | 18 | "The flat stood around them" | | 19 | "A mug of tea had" |
| | ratio | 0.838 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 3 | | matches | | 0 | "The flat stood around them the way Eva had left it: books stacked two deep along the wall, scrolls furled in a bucket by the radiator, a map of something that w…" | | 1 | "His skin was hot under her hand, hotter than a person runs, and she could feel the beat of him under the heel of her palm moving faster than the rest of him was…" | | 2 | "None of them involved him standing in Eva's kitchen with his shirt off and his hand on her wrist, breathing like a man who'd run the whole way." |
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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 | 1 | | fancyTags | | | dialogueSentences | 64 | | tagDensity | 0.094 | | leniency | 0.188 | | rawRatio | 0.167 | | effectiveRatio | 0.031 | |