| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.6 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 675 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 675 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "echo" | | 1 | "footsteps" | | 2 | "weight" | | 3 | "predator" | | 4 | "charged" | | 5 | "electric" | | 6 | "loomed" | | 7 | "could feel" | | 8 | "echoing" | | 9 | "oppressive" | | 10 | "silence" | | 11 | "glinting" | | 12 | "flicked" | | 13 | "reminder" | | 14 | "depths" |
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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 | 37 | | matches | (empty) | |
| 65.64% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 37 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 39 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 664 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 71.88% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 640 | | uniqueNames | 13 | | maxNameDensity | 1.56 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 10 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Veil | 3 | | Market | 3 | | Cockney | 1 | | Tommy | 1 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Market" | | 4 | "Tommy" | | 5 | "Herrera" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Nest" | | 3 | "Veil" |
| | globalScore | 0.719 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | 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 | 664 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 39 | | matches | (empty) | |
| 31.25% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 6 | | mean | 110.67 | | std | 28.7 | | cv | 0.259 | | sampleLengths | | |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 37 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 95 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 39 | | ratio | 0.103 | | matches | | 0 | "The suspect—a lanky figure in a dark coat—had vanished into the shadows of Soho, leaving only the echo of hurried footsteps and the metallic tang of fear on the wind." | | 1 | "The bone token—a small, polished ivory relic—clutched in her pocket felt cold and heavy, a key to the next phase of the hunt." | | 2 | "She’d lost partners before, but this one—this slippery, supernatural creature—was different." | | 3 | "The air reeked of ozone, damp earth, and something else—copper, blood, and the faint, unsettling scent of ozone." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 651 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.015360983102918587 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.009216589861751152 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 39 | | echoCount | 0 | | echoWords | (empty) | |
| 91.28% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 39 | | mean | 17.03 | | std | 6.44 | | cv | 0.378 | | sampleLengths | | 0 | 17 | | 1 | 28 | | 2 | 30 | | 3 | 17 | | 4 | 22 | | 5 | 23 | | 6 | 20 | | 7 | 21 | | 8 | 11 | | 9 | 19 | | 10 | 15 | | 11 | 15 | | 12 | 6 | | 13 | 25 | | 14 | 7 | | 15 | 21 | | 16 | 18 | | 17 | 17 | | 18 | 16 | | 19 | 12 | | 20 | 21 | | 21 | 15 | | 22 | 10 | | 23 | 26 | | 24 | 7 | | 25 | 9 | | 26 | 17 | | 27 | 13 | | 28 | 17 | | 29 | 33 | | 30 | 7 | | 31 | 17 | | 32 | 13 | | 33 | 9 | | 34 | 14 | | 35 | 19 | | 36 | 15 | | 37 | 16 | | 38 | 26 |
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| 66.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.46153846153846156 | | totalSentences | 39 | | uniqueOpeners | 18 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 37 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 37 | | matches | | 0 | "She’d tracked him here, through" | | 1 | "She’d lost partners before, but" | | 2 | "He moved with a predator’s" | | 3 | "He’d slipped through a hidden" | | 4 | "It was a cavern of" | | 5 | "She drew her weapon, the" | | 6 | "It was Tommy Herrera, the" | | 7 | "She could turn back now," | | 8 | "Her watch ticked on, a" | | 9 | "She shoved the bone token" | | 10 | "she growled, her voice cutting" |
| | ratio | 0.297 | |
| 0.54% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 34 | | totalSentences | 37 | | matches | | 0 | "Rain lashed the pavement like" | | 1 | "Detective Harlow Quinn’s worn leather" | | 2 | "The suspect—a lanky figure in" | | 3 | "Quinn’s boots splashed through a" | | 4 | "She’d tracked him here, through" | | 5 | "The bone token—a small, polished" | | 6 | "The suspect’s path led downward," | | 7 | "Quinn followed, her coat collar" | | 8 | "She’d lost partners before, but" | | 9 | "He moved with a predator’s" | | 10 | "The air grew colder, thicker," | | 11 | "The Raven’s Nest bar loomed" | | 12 | "He’d slipped through a hidden" | | 13 | "It was a cavern of" | | 14 | "The air reeked of ozone," | | 15 | "Stalls lined the narrow aisles," | | 16 | "The suspect was nowhere in" | | 17 | "She drew her weapon, the" | | 18 | "This was the heart of" | | 19 | "A figure stepped from behind" |
| | ratio | 0.919 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 37 | | matches | | 0 | "To follow him meant descending" |
| | ratio | 0.027 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 5 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 1 | | effectiveRatio | 0.4 | |