| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said finally [finally]" | | 1 | "PC Marsden said slowly [slowly]" |
| | dialogueSentences | 14 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0.333 | | effectiveRatio | 0.286 | |
| 85.75% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 702 | | totalAiIsmAdverbs | 2 | | 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) | |
| 64.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 702 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "echoing" | | 1 | "familiar" | | 2 | "amidst" | | 3 | "etched" | | 4 | "scanning" |
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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 | 45 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 45 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 53 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 709 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 24.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 557 | | uniqueNames | 11 | | maxNameDensity | 2.51 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 14 | | Tube | 1 | | Camden | 1 | | Marsden | 10 | | Eva | 5 | | Kowalski | 1 | | Veil | 3 | | Compass | 2 | | Morris | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Marsden" | | 3 | "Eva" | | 4 | "Kowalski" | | 5 | "Morris" | | 6 | "Market" |
| | places | | | globalScore | 0.243 | | windowScore | 0.333 | |
| 82.43% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 709 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 53 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 37.32 | | std | 22.09 | | cv | 0.592 | | sampleLengths | | 0 | 47 | | 1 | 14 | | 2 | 43 | | 3 | 4 | | 4 | 46 | | 5 | 76 | | 6 | 93 | | 7 | 29 | | 8 | 42 | | 9 | 24 | | 10 | 26 | | 11 | 58 | | 12 | 26 | | 13 | 37 | | 14 | 62 | | 15 | 15 | | 16 | 29 | | 17 | 16 | | 18 | 22 |
| |
| 97.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 45 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 90 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 53 | | ratio | 0.113 | | matches | | 0 | "She spotted a familiar curly red head near the tunnel entrance – Eva Kowalski, huddled with her worn leather satchel full of books." | | 1 | "She made a mental note to ask Eva about it – occult researchers didn't usually attend market gatherings, but sometimes Eva dropped by to...banter, Quinn supposed." | | 2 | "No visible defensive wounds, though – their attacker must have caught them by surprise." | | 3 | "A Veil Compass wasn't worth killing for – while useful, it was also a tool that came and went in the underground market." | | 4 | "This victim should've had a stash on them – but nothing." | | 5 | "Quinn's expression switched to weariness for an instant – PC Marsden didn't need to know about her pet theories, and she wasn't sure Eva was entirely safe in the Veil Market's current climate." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 558 | | adjectiveStacks | 1 | | stackExamples | | 0 | "familiar curly red head" |
| | adverbCount | 19 | | adverbRatio | 0.034050179211469536 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.016129032258064516 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 53 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 53 | | mean | 13.38 | | std | 7.36 | | cv | 0.551 | | sampleLengths | | 0 | 19 | | 1 | 14 | | 2 | 14 | | 3 | 8 | | 4 | 6 | | 5 | 12 | | 6 | 14 | | 7 | 10 | | 8 | 7 | | 9 | 4 | | 10 | 9 | | 11 | 37 | | 12 | 9 | | 13 | 8 | | 14 | 20 | | 15 | 16 | | 16 | 23 | | 17 | 6 | | 18 | 12 | | 19 | 18 | | 20 | 3 | | 21 | 26 | | 22 | 14 | | 23 | 14 | | 24 | 11 | | 25 | 18 | | 26 | 2 | | 27 | 6 | | 28 | 23 | | 29 | 5 | | 30 | 4 | | 31 | 2 | | 32 | 5 | | 33 | 8 | | 34 | 11 | | 35 | 7 | | 36 | 19 | | 37 | 16 | | 38 | 18 | | 39 | 24 | | 40 | 6 | | 41 | 20 | | 42 | 9 | | 43 | 14 | | 44 | 14 | | 45 | 14 | | 46 | 33 | | 47 | 15 | | 48 | 15 | | 49 | 15 |
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| 76.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.49056603773584906 | | totalSentences | 53 | | uniqueOpeners | 26 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 43 | | matches | | 0 | "he said, offering a nod" | | 1 | "She walked toward it, her" | | 2 | "She spotted a familiar curly" | | 3 | "Her hand twitched." | | 4 | "She made a mental note" | | 5 | "she said finally" |
| | ratio | 0.14 | |
| 29.77% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 43 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "The dimly lit tunnel, musty" | | 2 | "A constable in a bright" | | 3 | "he said, offering a nod" | | 4 | "Quinn's gaze swept the platform," | | 5 | "A patch of bright blue" | | 6 | "She walked toward it, her" | | 7 | "PC Marsden fell into step" | | 8 | "PC Marsden replied, consulting his" | | 9 | "Quinn ducked under the tape" | | 10 | "A dropped carton of juicy" | | 11 | "A small crowd of camel-coated" | | 12 | "She spotted a familiar curly" | | 13 | "Quinn's focus returned to the" | | 14 | "A Veil Compass, that brass" | | 15 | "Her hand twitched." | | 16 | "She made a mental note" | | 17 | "Someone had beaten this poor" | | 18 | "PC Marsden said, still scanning" | | 19 | "PC Marsden's theory didn't quite" |
| | ratio | 0.86 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 43 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 1 | | matches | | 0 | "A Veil Compass wasn't worth killing for – while useful, it was also a tool that came and went in the underground market." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 78.57% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn responded (respond)" |
| | dialogueSentences | 14 | | tagDensity | 0.429 | | leniency | 0.857 | | rawRatio | 0.167 | | effectiveRatio | 0.143 | |