| 78.57% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 1 | | adverbTags | | 0 | "He gestured vaguely [vaguely]" |
| | dialogueSentences | 12 | | tagDensity | 0.583 | | leniency | 1 | | rawRatio | 0.143 | | effectiveRatio | 0.143 | |
| 93.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 732 | | 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 732 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "chill" | | 1 | "footsteps" | | 2 | "scanning" | | 3 | "gloom" | | 4 | "echoed" | | 5 | "glinting" | | 6 | "traced" | | 7 | "chilling" | | 8 | "furrowed" | | 9 | "whisper" | | 10 | "weight" | | 11 | "reminder" |
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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 | 1 | | narrationSentences | 50 | | matches | | |
| 85.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 50 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 55 | | 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 | 731 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 660 | | uniqueNames | 13 | | maxNameDensity | 1.67 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 2 | | Harlow | 1 | | Quinn | 11 | | Metropolitan | 1 | | Tomás | 1 | | Herrera | 6 | | Saint | 1 | | Christopher | 1 | | Morris | 1 | | Veil | 1 | | Market | 1 | | Raven | 1 | | Nest | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" | | 7 | "Raven" | | 8 | "Nest" |
| | places | | | globalScore | 0.667 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 40 | | 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 | 731 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 55 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 13 | | mean | 56.23 | | std | 34.96 | | cv | 0.622 | | sampleLengths | | 0 | 102 | | 1 | 95 | | 2 | 79 | | 3 | 82 | | 4 | 14 | | 5 | 22 | | 6 | 29 | | 7 | 61 | | 8 | 27 | | 9 | 31 | | 10 | 58 | | 11 | 12 | | 12 | 119 |
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| 91.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 50 | | matches | | 0 | "was gone" | | 1 | "being hunted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 107 | | matches | | |
| 90.91% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 55 | | ratio | 0.018 | | matches | | 0 | "The view was breathtaking – the sprawling city lights of London, a sea of humanity below." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 661 | | adjectiveStacks | 2 | | stackExamples | | 0 | "short, curly dark hair" | | 1 | "distant, indifferent green above them," |
| | adverbCount | 17 | | adverbRatio | 0.025718608169440244 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.01361573373676248 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 55 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 55 | | mean | 13.29 | | std | 8.37 | | cv | 0.63 | | sampleLengths | | 0 | 22 | | 1 | 35 | | 2 | 16 | | 3 | 29 | | 4 | 5 | | 5 | 21 | | 6 | 21 | | 7 | 15 | | 8 | 20 | | 9 | 13 | | 10 | 15 | | 11 | 9 | | 12 | 10 | | 13 | 12 | | 14 | 8 | | 15 | 16 | | 16 | 9 | | 17 | 10 | | 18 | 9 | | 19 | 11 | | 20 | 4 | | 21 | 23 | | 22 | 15 | | 23 | 10 | | 24 | 10 | | 25 | 4 | | 26 | 18 | | 27 | 4 | | 28 | 6 | | 29 | 14 | | 30 | 9 | | 31 | 3 | | 32 | 18 | | 33 | 24 | | 34 | 11 | | 35 | 5 | | 36 | 9 | | 37 | 14 | | 38 | 4 | | 39 | 4 | | 40 | 27 | | 41 | 3 | | 42 | 3 | | 43 | 20 | | 44 | 3 | | 45 | 8 | | 46 | 7 | | 47 | 10 | | 48 | 4 | | 49 | 12 |
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| 70.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.43636363636363634 | | totalSentences | 55 | | uniqueOpeners | 24 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 50 | | matches | (empty) | | ratio | 0 | |
| 76.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 50 | | matches | | 0 | "Her boots splashed through a" | | 1 | "She didn’t run, but moved" | | 2 | "She followed the faint, frantic" | | 3 | "She waited, crouched low behind" | | 4 | "They climbed, the city lights" | | 5 | "It wasn’t the suspect." | | 6 | "It was Tomás Herrera, his" | | 7 | "He looked terrified, his eyes" | | 8 | "he whispered, his voice thick" | | 9 | "He gestured vaguely towards the" | | 10 | "She couldn’t turn away now." | | 11 | "she said, her voice tight" | | 12 | "She gestured towards the rooftop," | | 13 | "She’d heard whispers, rumors of" | | 14 | "It was uncharted territory, dangerous" | | 15 | "she pressed, ignoring the chill" | | 16 | "He turned and melted back" | | 17 | "She drew her coat tighter" |
| | ratio | 0.36 | |
| 20.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 44 | | totalSentences | 50 | | matches | | 0 | "The rain lashed thepavement like" | | 1 | "Detective Harlow Quinn’s breath misted" | | 2 | "Her boots splashed through a" | | 3 | "Quinn’s military precision kicked in." | | 4 | "She didn’t run, but moved" | | 5 | "The alley was a labyrinth" | | 6 | "She followed the faint, frantic" | | 7 | "The figure was fast, but" | | 8 | "She waited, crouched low behind" | | 9 | "The figure emerged, slipping through" | | 10 | "Quinn followed, her boots silent" | | 11 | "They climbed, the city lights" | | 12 | "Quinn paused, her breath catching" | | 13 | "The view was breathtaking –" | | 14 | "A low, guttural growl echoed" | | 15 | "Quinn spun, her hand instinctively" | | 16 | "A figure emerged from the" | | 17 | "It wasn’t the suspect." | | 18 | "It was Tomás Herrera, his" | | 19 | "A jagged scar, pale against" |
| | ratio | 0.88 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 50 | | matches | | 0 | "Yet, the suspect had vanished" |
| | ratio | 0.02 | |
| 89.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 2 | | matches | | 0 | "Three years ago, her partner, DS Morris, had vanished under circumstances she still didn’t understand, circumstances that had supernatural origins she didn’t ye…" | | 1 | "She drew her coat tighter and followed Herrera into the darkness, the neon sign of The Raven’s Nest blinking a distant, indifferent green above them, unaware of…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 3 | | matches | | 0 | "he whispered, his voice thick with rain and strain" | | 1 | "she said, her voice tight" | | 2 | "Herrera said, his voice barely a whisper" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "he whispered (whisper)" | | 1 | "Quinn demanded (demand)" | | 2 | "she pressed (press)" |
| | dialogueSentences | 12 | | tagDensity | 0.417 | | leniency | 0.833 | | rawRatio | 0.6 | | effectiveRatio | 0.5 | |