| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 35 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.07% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1510 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "perfectly" | | 1 | "suddenly" | | 2 | "very" |
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| 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) | |
| 50.33% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1510 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "measured" | | 2 | "weight" | | 3 | "pulsed" | | 4 | "reminder" | | 5 | "silk" | | 6 | "comfortable" | | 7 | "echoed" | | 8 | "gloom" | | 9 | "familiar" | | 10 | "shattered" | | 11 | "predator" | | 12 | "warmth" | | 13 | "flicked" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 87 | | matches | (empty) | |
| 93.60% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 87 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 115 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1510 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1023 | | uniqueNames | 19 | | maxNameDensity | 1.17 | | worstName | "Silas" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Silas" | | discoveredNames | | Tanqueray | 1 | | Soho | 1 | | Cardiff | 1 | | Raven | 2 | | Nest | 2 | | Italian | 1 | | Chancery | 1 | | Lane | 1 | | London | 2 | | Rory | 1 | | Criminal | 1 | | Law | 1 | | Swiss | 1 | | Silas | 12 | | Gareth | 11 | | Guinness | 2 | | Stout | 1 | | Evan | 4 | | Zippo | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Rory" | | 3 | "Law" | | 4 | "Silas" | | 5 | "Gareth" | | 6 | "Stout" | | 7 | "Evan" |
| | places | | 0 | "Soho" | | 1 | "Cardiff" | | 2 | "Chancery" | | 3 | "Lane" | | 4 | "London" | | 5 | "Criminal" |
| | globalScore | 0.913 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 65 | | glossingSentenceCount | 1 | | matches | | 0 | "felt like a physical blow" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1510 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 115 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 72 | | mean | 20.97 | | std | 13.17 | | cv | 0.628 | | sampleLengths | | 0 | 42 | | 1 | 48 | | 2 | 10 | | 3 | 30 | | 4 | 18 | | 5 | 36 | | 6 | 10 | | 7 | 46 | | 8 | 10 | | 9 | 1 | | 10 | 35 | | 11 | 1 | | 12 | 15 | | 13 | 59 | | 14 | 23 | | 15 | 18 | | 16 | 17 | | 17 | 16 | | 18 | 8 | | 19 | 3 | | 20 | 56 | | 21 | 23 | | 22 | 8 | | 23 | 40 | | 24 | 9 | | 25 | 9 | | 26 | 22 | | 27 | 21 | | 28 | 8 | | 29 | 21 | | 30 | 19 | | 31 | 14 | | 32 | 19 | | 33 | 5 | | 34 | 22 | | 35 | 24 | | 36 | 17 | | 37 | 4 | | 38 | 39 | | 39 | 32 | | 40 | 20 | | 41 | 12 | | 42 | 16 | | 43 | 16 | | 44 | 40 | | 45 | 14 | | 46 | 30 | | 47 | 31 | | 48 | 3 | | 49 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 87 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 162 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 115 | | ratio | 0 | | matches | (empty) | |
| 82.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1035 | | adjectiveStacks | 3 | | stackExamples | | 0 | "small crescent-shaped scar" | | 1 | "heavy brass-fitted door" | | 2 | "old yellowed nautical maps" |
| | adverbCount | 18 | | adverbRatio | 0.017391304347826087 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003864734299516908 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 115 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 115 | | mean | 13.13 | | std | 7.97 | | cv | 0.607 | | sampleLengths | | 0 | 19 | | 1 | 4 | | 2 | 19 | | 3 | 9 | | 4 | 21 | | 5 | 18 | | 6 | 10 | | 7 | 16 | | 8 | 14 | | 9 | 18 | | 10 | 15 | | 11 | 21 | | 12 | 10 | | 13 | 14 | | 14 | 15 | | 15 | 17 | | 16 | 10 | | 17 | 1 | | 18 | 7 | | 19 | 6 | | 20 | 22 | | 21 | 1 | | 22 | 5 | | 23 | 10 | | 24 | 14 | | 25 | 3 | | 26 | 14 | | 27 | 28 | | 28 | 23 | | 29 | 18 | | 30 | 17 | | 31 | 16 | | 32 | 8 | | 33 | 3 | | 34 | 5 | | 35 | 10 | | 36 | 26 | | 37 | 15 | | 38 | 23 | | 39 | 8 | | 40 | 12 | | 41 | 18 | | 42 | 10 | | 43 | 9 | | 44 | 9 | | 45 | 5 | | 46 | 17 | | 47 | 21 | | 48 | 8 | | 49 | 7 |
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| 36.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.25217391304347825 | | totalSentences | 115 | | uniqueOpeners | 29 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 15.12% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 82 | | matches | | 0 | "I hoisted the heavy wooden" | | 1 | "My knuckles turned white." | | 2 | "He moved with a rhythmic," | | 3 | "His silver signet ring caught" | | 4 | "His voice carried that quiet" | | 5 | "He shifted his weight, his" | | 6 | "I wiped a bead of" | | 7 | "He shook a black silk" | | 8 | "He wore a crisp tailored" | | 9 | "I froze, a bottle of" | | 10 | "I set the bottle down." | | 11 | "My heart hammered against my" | | 12 | "He looked different." | | 13 | "He paused, his gaze sweeping" | | 14 | "I leaned back against the" | | 15 | "He placed the polished glass" | | 16 | "His eyes stayed on Gareth," | | 17 | "He kept his focus on" | | 18 | "I reached for a rag" | | 19 | "He stepped closer, the smell" |
| | ratio | 0.512 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 79 | | totalSentences | 82 | | matches | | 0 | "I hoisted the heavy wooden" | | 1 | "My knuckles turned white." | | 2 | "Silas didn't look up from" | | 3 | "He moved with a rhythmic," | | 4 | "His silver signet ring caught" | | 5 | "His voice carried that quiet" | | 6 | "He shifted his weight, his" | | 7 | "I wiped a bead of" | | 8 | "The small crescent-shaped scar on" | | 9 | "The heavy brass-fitted door of" | | 10 | "A man stepped inside, framed" | | 11 | "He shook a black silk" | | 12 | "He wore a crisp tailored" | | 13 | "I froze, a bottle of" | | 14 | "The name felt like a" | | 15 | "Nobody in London called me" | | 16 | "Here, I was the girl" | | 17 | "I set the bottle down." | | 18 | "My heart hammered against my" | | 19 | "Gareth vaulted the gap between" |
| | ratio | 0.963 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 48.52% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 7 | | matches | | 0 | "He wore a crisp tailored navy Italian wool suit that screamed of Chancery Lane and high-stakes litigation." | | 1 | "The soft-edged, stuttering boy who’d sat next to me in Criminal Law had vanished." | | 2 | "In his place stood a man with sharp cheekbones and a sleek expensive silver Swiss watch that glinted as he ran a hand through his perfectly groomed hair." | | 3 | "He kept his focus on me, his eyes filled with a pity that made my skin crawl." | | 4 | "He reached out to grab my arm, his fingers aiming for the wrist I was trying to hide." | | 5 | "The mask of the concerned friend slipped, revealing the predatory edge of the man who won cases by breaking witnesses." | | 6 | "The light from the green neon sign hit his face, revealing the familiar sharp line of a jaw I’d once loved and the cold dead eyes I’d spent two years trying to …" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 2 | | matches | | 0 | "Gareth said, his voice devoid of any warmth" | | 1 | "Silas said, his voice a low growl of thunder" |
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| 92.86% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 2 | | fancyTags | | 0 | "Silas interjected (interject)" | | 1 | "I spat (spit)" |
| | dialogueSentences | 35 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0.286 | | effectiveRatio | 0.114 | |