| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.081 | | leniency | 0.162 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.65% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2129 | | totalAiIsmAdverbs | 1 | | 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) | |
| 81.21% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2129 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "echoed" | | 1 | "chilled" | | 2 | "churn" | | 3 | "flickered" | | 4 | "warmth" | | 5 | "velvet" | | 6 | "flicked" | | 7 | "whisper" |
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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 | 216 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 216 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 250 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2129 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 56 | | wordCount | 1994 | | uniqueNames | 21 | | maxNameDensity | 1.5 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Quinn | 30 | | Raven | 1 | | Nest | 1 | | Greek | 1 | | Street | 2 | | East | 1 | | End | 1 | | Old | 1 | | Compton | 1 | | London | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Morris | 2 | | Camden | 2 | | Underground | 1 | | Veil | 1 | | Market | 1 | | Tomás | 2 | | Herrera | 1 | | Metal | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Raven" | | 2 | "Morris" | | 3 | "Underground" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Metal" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "East" | | 3 | "End" | | 4 | "Old" | | 5 | "Compton" | | 6 | "London" | | 7 | "Charing" | | 8 | "Cross" | | 9 | "Road" | | 10 | "Camden" | | 11 | "Market" |
| | globalScore | 0.748 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 130 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.47 | | wordCount | 2129 | | matches | | 0 | "Not clearly, but enough" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 250 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 124 | | mean | 17.17 | | std | 20.17 | | cv | 1.175 | | sampleLengths | | 0 | 1 | | 1 | 30 | | 2 | 62 | | 3 | 51 | | 4 | 37 | | 5 | 31 | | 6 | 1 | | 7 | 27 | | 8 | 58 | | 9 | 8 | | 10 | 38 | | 11 | 5 | | 12 | 71 | | 13 | 26 | | 14 | 41 | | 15 | 4 | | 16 | 6 | | 17 | 38 | | 18 | 41 | | 19 | 5 | | 20 | 2 | | 21 | 6 | | 22 | 6 | | 23 | 2 | | 24 | 4 | | 25 | 2 | | 26 | 68 | | 27 | 6 | | 28 | 3 | | 29 | 4 | | 30 | 4 | | 31 | 27 | | 32 | 2 | | 33 | 4 | | 34 | 53 | | 35 | 11 | | 36 | 3 | | 37 | 55 | | 38 | 14 | | 39 | 4 | | 40 | 26 | | 41 | 4 | | 42 | 25 | | 43 | 1 | | 44 | 2 | | 45 | 1 | | 46 | 11 | | 47 | 6 | | 48 | 12 | | 49 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 216 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 345 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 250 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1613 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 44 | | adverbRatio | 0.02727836329820211 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0024798512089274642 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 250 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 250 | | mean | 8.52 | | std | 7.37 | | cv | 0.865 | | sampleLengths | | 0 | 1 | | 1 | 30 | | 2 | 17 | | 3 | 2 | | 4 | 10 | | 5 | 33 | | 6 | 15 | | 7 | 8 | | 8 | 28 | | 9 | 13 | | 10 | 16 | | 11 | 4 | | 12 | 1 | | 13 | 3 | | 14 | 17 | | 15 | 8 | | 16 | 6 | | 17 | 1 | | 18 | 4 | | 19 | 23 | | 20 | 9 | | 21 | 7 | | 22 | 15 | | 23 | 6 | | 24 | 21 | | 25 | 5 | | 26 | 3 | | 27 | 5 | | 28 | 2 | | 29 | 15 | | 30 | 16 | | 31 | 5 | | 32 | 8 | | 33 | 4 | | 34 | 1 | | 35 | 2 | | 36 | 4 | | 37 | 32 | | 38 | 20 | | 39 | 3 | | 40 | 9 | | 41 | 14 | | 42 | 9 | | 43 | 2 | | 44 | 2 | | 45 | 6 | | 46 | 22 | | 47 | 4 | | 48 | 2 | | 49 | 4 |
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| 66.93% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.428 | | totalSentences | 250 | | uniqueOpeners | 107 | |
| 90.09% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 185 | | matches | | 0 | "Then he had looked up," | | 1 | "Always one step off legal," | | 2 | "Then she saw where he" | | 3 | "Then Tomás Herrera emerged from" | | 4 | "Then every lamp went out." |
| | ratio | 0.027 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 41 | | totalSentences | 185 | | matches | | 0 | "She vaulted a black bollard," | | 1 | "He had come out of" | | 2 | "He took the alleys." | | 3 | "He darted past a late" | | 4 | "It also bought the suspect" | | 5 | "She hurdled the first, landed" | | 6 | "Her shin barked with pain." | | 7 | "She ignored it." | | 8 | "He burst onto Greek Street." | | 9 | "She had seen his face" | | 10 | "He had sat in the" | | 11 | "She cut right instead of" | | 12 | "Her lungs tightened, hot and" | | 13 | "She burst out by Old" | | 14 | "She turned her head, trapped" | | 15 | "He stamped on her instep" | | 16 | "She grabbed his satchel strap." | | 17 | "She kept the satchel." | | 18 | "She ducked into a recessed" | | 19 | "She answered without speaking." |
| | ratio | 0.222 | |
| 62.70% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 147 | | totalSentences | 185 | | matches | | 0 | "The man glanced back once," | | 1 | "She vaulted a black bollard," | | 2 | "A driver leaned out and" | | 3 | "Rain needled her cheeks and" | | 4 | "He had come out of" | | 5 | "That alone would have earned" | | 6 | "The blood on his cuff" | | 7 | "Quinn’s worn leather watch slapped" | | 8 | "He took the alleys." | | 9 | "Alleys narrowed options." | | 10 | "He darted past a late" | | 11 | "A pair of lads with" | | 12 | "Quinn clipped one in the" | | 13 | "That bought her space." | | 14 | "It also bought the suspect" | | 15 | "The alley ahead kinked left" | | 16 | "Graffiti climbed the brick in" | | 17 | "Quinn hit the corner hard" | | 18 | "The suspect had kicked them" | | 19 | "She hurdled the first, landed" |
| | ratio | 0.795 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 185 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 81 | | technicalSentenceCount | 2 | | matches | | 0 | "The blood on his cuff and the bone-white token he had dropped, snatched up, and pocketed before he saw her, that had turned a tail into a chase." | | 1 | "The phrase surfaced from a dozen interviews with junkies, thieves, occult hobbyists, and one terrified accountant who had begged for witness protection after in…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.081 | | leniency | 0.162 | | rawRatio | 0 | | effectiveRatio | 0 | |